Author: "Helen Zhang" / Topic: humans - Searchworks@Jio Institute Digital Library Search Results

38 results on '"Helen Zhang"'

1. Radiation Oncology AcaDemic Mentorship Program (ROADMAP) for Junior Faculty: One-Year Results of a Prospective Single Institution Initiative

Author: Diana Lin, Daniel R. Gomez, Yue Helen Zhang, Renee Gennarelli, Jason A. Efstathiou, Chris A. Barker, Daphna Y. Gelblum, Monika K. Shah, Laura Liberman, Ariel E. Hirsch, Oren Cahlon, and Erin F. Gillespie
Subjects: Cancer Research, Faculty, Medical, Radiation, Oncology, Mentors, Radiation Oncology, Humans, Radiology, Nuclear Medicine and imaging, Prospective Studies, Program Evaluation
Abstract: Although mentorship has been associated with promotion, job satisfaction, and retention, data are limited on the mentorship experience of clinical- versus research-track physicians as well as feasibility and relative priority of formal program components.Within a single-institution, multi-site, academic network, we implemented a Radiation Oncology AcaDemic Mentorship Program (ROADMAP) for junior faculty. Validated surveys assessing mentee satisfaction were distributed at baseline and 1 year. The statistical analysis included Wilcoxon rank sum and signed tests. Mentees assessed the likelihood to recommend each program component (10-point Likert-type scale), and means with standard error (SE) are reported.Among 42 eligible junior faculty, 36 (86%) opted into the program. The median time since residency was 2.5 years (interquartile range, 1.75-5.25) on the clinical track (n = 12) and 3 years (interquartile range, 2.75-5.00) on the research track (n = 24). At baseline, research-track physicians reported higher satisfaction with mentoring than physicians on the clinical track (2.92 vs 2.16; P = .02). Among 32 physicians completing 1 year, overall satisfaction with mentoring increased compared with baseline (2.72 vs 3.87; P.001), which persisted on subset analysis for both clinical- (2.16 vs 4.03; P.001) and research-track physicians (2.99 vs 3.77; P = .005). At 1 year, 28 mentees (88%) opted to continue the program. Program components were rated 8.25 (SE, 0.37) for mentor-mentee pairings, 7.22 (SE, 0.39) for goal setting, 6.84 (SE, 0.47) for administrative support, 6.69 (SE, 0.44) for peer mentoring, and 6.53 (SE, 0.45) for steering committee oversight. Ratings of peer mentoring were not associated with track (P = .59) or years in practice (P = .29).Clinical-track physicians may be less satisfied with mentorship than research-track faculty. However, all junior faculty, regardless of track, appeared to benefit from formalizing dyadic mentor-mentee relationships, goal setting, and peer mentoring. Further work is needed to determine the role of mentorship in addressing physician burnout.
Published: 2022

2. Analysis of Microbiological and Chemical Hazards in Edible Insects Available to Canadian Consumers

Author: Krystyna Johaniuk, Helen Zhang, Beata M. Kolakowski, and Etsuko Yamamoto
Subjects: Canada, Salmonella, Organophosphonates, chemistry.chemical_element, Food Contamination, Biology, medicine.disease_cause, Microbiology, chemistry.chemical_compound, Edible Insects, Escherichia coli, medicine, Animals, Humans, Food science, Arsenic, Cadmium, Pesticide residue, business.industry, Pesticide, Mercury (element), chemistry, Glyphosate, Food processing, business, Food Science
Abstract: Edible insects are a novel food in most countries; their popularity is growing because of their high protein/low fat content, ease of cultivation, and small environmental impact. To our knowledge, this is the first report that addresses both microbiological and chemical hazards in edible insects. Samples were collected from retail stores or purchase through e-commerce. A total of 51 samples of dried whole insects or insect powders were tested for generic Escherichia coli ( E. coli ) which serves as an indicator of the overall sanitation conditions throughout the food production chain, and the bacterial pathogen Salmonella species (spp.) Neither Salmonella spp. nor generic E. coli (>100 Colony Forming Units (CFU)/g) were found in any of the samples. 43 samples of crickets (protein bars, powders, flours, whole insects) and 4 samples of silkworm (whole insects) were analyzed for up to 511 pesticides. 39 samples contained residues from one to four pesticides; 34 samples were compliant and 5 samples were non-compliant with Canadian regulations. Seven different pesticide residues were detected with glyphosate and its metabolite, AMPA, being the predominant residues detected. Nineteen of the samples tested for pesticides were also analyzed for arsenic, cadmium, mercury, and lead; there was insufficient material remaining to allow for metals testing. The positive rate for arsenic, cadmium, lead and mercury was 100%, 79%, 58% and 74%, respectively. The detected concentrations ranged from 0.030 mg/kg to 0.34 mg/kg for arsenic, from 0.031 mg/kg to 0.23 mg/kg for cadmium, 0.019 mg/kg to 0.059 mg/kg for lead, and from 0.00094 mg/kg to 0.028 mg/kg for mercury. Based on the lack of detection of microbiological contamination, and the positive rate and levels of pesticides and metals observed in the products, Health Canada determined that all insect products analyzed were safe for human consumption. This is a limited study; CFIA will continue to monitor this novel food.
Published: 2021

3. Attitudes and access to resources and strategies to improve quality of radiotherapy among US radiation oncologists: A mixed methods study

Author: Y Helen Zhang, Elaine Cha, Kathleen Lynch, Renee Gennarelli, Jeffrey Brower, Michael V Sherer, Daniel W Golden, Susan Chimonas, Deborah Korenstein, and Erin F Gillespie
Subjects: Oncology, Attitude, Surveys and Questionnaires, Radiation Oncologists, Radiation Oncology, Humans, Radiology, Nuclear Medicine and imaging
Abstract: We aimed to assess contouring-related practices among US radiation oncologists and explore how access to and use of resources and quality improvement strategies vary based on individual- and organization-level factors.We conducted a mixed methods study with a sequential explanatory design. Surveys were emailed to a random 10% sample of practicing US radiation oncologists. Participating physicians were invited to a semi-structured interview. Kruskal-Wallis and Wilcoxon rank-sum tests and a multivariable regression model were used to evaluate associations. Interview data were coded using thematic content analysis.Survey overall response rate was 24%, and subsequent completion rate was 97%. Contouring-related questions arise in ≥50% of clinical cases among 73% of respondents. Resources accessed first include published atlases (75%) followed by consulting another radiation oncologist (60%). Generalists access consensus guidelines more often than disease-site specialists (P = 0.04), while eContour.org is more often used by generalists (OR 4.3, 95% CI 1.2-14.8) and younger physicians (OR 1.33 for each 5-year increase, 95% CI 1.08-1.67). Common physician-reported barriers to optimizing contour quality are time constraints (58%) and lack of access to disease-site specialists (21%). Forty percent (40%, n = 14) of physicians without access to disease-site specialists indicated it could facilitate the adoption of new treatments. Almost all (97%) respondents have formal peer review, but only 43% have contour-specific review, which is more common in academic centres (P = 0.02).Potential opportunities to improve radiation contour quality include improved access to disease-site specialists and contour-specific peer review. Physician time must be considered when designing new strategies.
Published: 2022

4. Group IIA secreted phospholipase A2 is associated with the pathobiology leading to COVID-19 mortality

Author: Maurizio Del Poeta, Michael C. Seeds, Chiara Luberto, Stefano Guerra, Floyd H. Chilton, Yusuf A. Hannun, Christian Bime, Ryan Sprissler, Ashley J. Snider, Jeehyun Karen You, Qiuming Wang, Susan Sergeant, Laurel Johnstone, Hao Helen Zhang, Guang Yao, Karen Lutrick, Charles E. McCall, Tara F. Carr, Brian Hallmark, Richard R. Kew, Manja Zec, Xia Wang, Sairam Parthasarathy, and Justin M. Snider
Subjects: Adult, Male, medicine.medical_specialty, Adolescent, Inflammation, Group II Phospholipases A2, Severity of Illness Index, Gastroenterology, Disease-Free Survival, Internal medicine, Severity of illness, Humans, Medicine, Child, Survival rate, Blood urea nitrogen, Aged, Aged, 80 and over, SARS-CoV-2, business.industry, Molecular pathology, Organ dysfunction, COVID-19, General Medicine, Middle Aged, Hypoxia (medical), Survival Rate, Cohort, Female, medicine.symptom, business, Research Article
Abstract: There is an urgent need to identify the cellular and molecular mechanisms responsible for severe COVID-19 that results in death. We initially performed both untargeted and targeted lipidomics as well as focused biochemical analyses of 127 plasma samples and found elevated metabolites associated with secreted phospholipase A(2) (sPLA(2)) activity and mitochondrial dysfunction in patients with severe COVID-19. Deceased COVID-19 patients had higher levels of circulating, catalytically active sPLA(2) group IIA (sPLA(2)-IIA), with a median value that was 9.6-fold higher than that for patients with mild disease and 5.0-fold higher than the median value for survivors of severe COVID-19. Elevated sPLA(2)-IIA levels paralleled several indices of COVID-19 disease severity (e.g., kidney dysfunction, hypoxia, multiple organ dysfunction). A decision tree generated by machine learning identified sPLA(2)-IIA levels as a central node in the stratification of patients who died from COVID-19. Random forest analysis and least absolute shrinkage and selection operator–based (LASSO-based) regression analysis additionally identified sPLA(2)-IIA and blood urea nitrogen (BUN) as the key variables among 80 clinical indices in predicting COVID-19 mortality. The combined PLA-BUN index performed significantly better than did either one alone. An independent cohort (n = 154) confirmed higher plasma sPLA(2)-IIA levels in deceased patients compared with levels in plasma from patients with severe or mild COVID-19, with the PLA-BUN index–based decision tree satisfactorily stratifying patients with mild, severe, or fatal COVID-19. With clinically tested inhibitors available, this study identifies sPLA(2)-IIA as a therapeutic target to reduce COVID-19 mortality.
Published: 2021

5. Acute promyelocytic leukemia with Chediak-Higashi like giant granules

Author: Y. Helen Zhang and Xin Han
Subjects: Leukemia, Promyelocytic, Acute, Antigens, CD, Immunology, Humans, Female, Cell Biology, Hematology, Chediak-Higashi Syndrome, Cytoplasmic Granules, Biochemistry, Aged
Published: 2021

6. Robust regression for optimal individualized treatment rules

Author: Hao Helen Zhang, Wei Xiao, and Wenbin Lu
Subjects: FOS: Computer and information sciences, Statistics and Probability, Mathematical optimization, Epidemiology, Computer science, Asymptotic distribution, Conditional expectation, 01 natural sciences, Article, Robust regression, Methodology (stat.ME), 010104 statistics & probability, 03 medical and health sciences, Consistency (database systems), 0302 clinical medicine, Clinical Protocols, Humans, 030212 general & internal medicine, Precision Medicine, 0101 mathematics, Statistics - Methodology, Models, Statistical, Estimator, Quantile regression, Outlier, Regression Analysis, Algorithms, Quantile
Abstract: Because different patients may respond quite differently to the same drug or treatment, there is an increasing interest in discovering individualized treatment rules. In particular, there is an emerging need to find optimal individualized treatment rules, which would lead to the "best" clinical outcome. In this paper, we propose a new class of loss functions and estimators based on robust regression to estimate the optimal individualized treatment rules. Compared to existing estimation methods in the literature, the new estimators are novel and advantageous in the following aspects. First, they are robust against skewed, heterogeneous, heavy-tailed errors or outliers in data. Second, they are robust against a misspecification of the baseline function. Third, under some general situations, the new estimator coupled with the pinball loss approximately maximizes the outcome's conditional quantile instead of the conditional mean, which leads to a more robust optimal individualized treatment rule than the traditional mean-based estimators. Consistency and asymptotic normality of the proposed estimators are established. Their empirical performance is demonstrated via extensive simulation studies and an analysis of an AIDS data set.
Published: 2019

7. Timing of surgery following SARS-CoV-2 infection: an international prospective cohort study

Author: Dmitri, Nepogodiev, Joana Ff Simoes, Elizabeth, Li, Maria, Picciochi, James, C Glasbey, Glauco, Baiocchi, Ruth, Blanco-Colino, Daoud, Chaudhry, Ehab, Alameer, Kariem, El-Boghdadly, Funmilola, Wuraola, Dhruva, Ghosh, Rohan, R Gujjuri, Ewen, M Harrison, Herman, Lule, Haytham, Kaafarani, Mohammad, Khosravi, Irmgard, Kronberger, Sezai, Leventoğlu, Harvinder, Mann, Kenneth, A Mclean, Mengistu Gebreyohanes Mengesha, Maria Marta Modolo, Faustin, Ntirenganya, Lisa, Norman, Oumaima, Outani, Riinu, Pius, Peter, Pockney, Ahmad Uzair Qureshi, April Camilla Roslani, Sohei, Satoi, Catherine, Shaw, Aneel, Bhangu, Omar, M Omar, Waheed-Ul-Rahman, Ahmed, Leah, Argus, Alasdair, Ball, Edward, P Bywater, Amanpreet, Brar, Brett, E Dawson, Irani, Duran, Muhammed, Elhadi, Conor, S Jones, Sivesh, K Kamarajah, James, M Keatley, Samuel, Lawday, Ella, J Marson, Riinu, Ots, Irène, Santos, Elliott, H Taylor, Isobel, M Trout, Chris, Varghese, Mary, L Venn, William, Xu, Irida, Dajti, Arben, Gjata, Salah Eddine Oussama Kacimi, Luis, Boccalatte, Daniel, Cox, Philip, Townend, Felix, Aigner, Irmgard Elisabeth Kronberger, Elgun, Samadov, Amer, Alderazi, Kamral, Hossain, Greg, Padmore, Gabrielle van Ramshorst, Ismaïl, Lawani, Anis, Cerovac, Samir, Delibegovic, Gustavo Mendonça Ataíde Gomes, Igor Lima Buarque, Muhammad, Gohar, Mihail, Slavchev, Chukwuemeka, Nwegbu, Arnav, Agarwal, Janet, Martin, Joshua, Ng-Kamstra, Maricarmen, Olivos, Wenhui, Lou, Dong-Lin, Ren, Jose Andres Calvache, Carlos, J Perez Rivera, Ana Danic Hadzibegovic, Tomislav, Kopjar, Jakov, Mihanovic, Pablo Mijahil Avilés Jiménez, Nikolaos, Gouvas, Jaroslav, Klat, René, Novysedlák, Nicolas, Amisi, Peter, Christensen, Alaa, El-Hussuna, Sylvia, Batista, Eddy, Lincango-Naranjo, Sameh, Emile, Danilo Alfonso Arévalo Sandoval, Hailu, Dhufera, Samuel, Hailu, Mengistu, G Mengesha, Joonas, H Kauppila, Alexis, P Arnaud, Zaza, Demetrashvili, Markus, Albertsmeier, Hans, Lederhuber, Markus, W Löffler, Daniel Kwesi Acquah, Bernard, Ofori, Stephen, Tabiri, Symeon, Metallidis, Georgios, Tsoulfas, Maria-Lorena, Aguilera-Arevalo, Gustavo, Recinos, Tamás, Mersich, Dániel, Wettstein, Gabriele, Kembuan, Peiman Brouki Milan, Mohammad Hossein Khosravi, Masoud, Mozafari, Ahmed, Hilmi, Helen, Mohan, Oded, Zmora, Gaetano, Gallo, Francesco, Pata, Gianluca, Pellino, Yuki, Fujimoto, Naoto, Kuroda, Mohamad, K Abou Chaar, Faris, Ayasra, Ildar, Fakhradiyev, Intisar Hisham Said Hamdun, Jang, Jin-Young, Mohammad, Jamal, Lina, Karout, Aiste, Gulla, Fanjandrainy, Rasoaherinomenjanahary, Luc Hervé Samison, Iran Irani Durán Sánchez, Diana Samantha Gonzalez, Laura, Martinez, María José Martínez, Alejandra, Nayen, Antonio Ramos-De la Medina, Jade, Nunez, Pueya Rashid Nashidengo, Rakesh, Shah, Ashish Lal Shrestha, Pascal, Jonker, Schelto, Kruijff, Milou, Noltes, Pieter, Steinkamp, Deborah, Wright, Lukman, Abdur-Rahman, Adesoji, Ademuyiwa, Adewale, Adisa, Babatunde, Osinaike, Justina, Seyi-Olajide, Omolara, Williams, Emmanuel, Williams, Sofija, Pejkova, Zainab Al Balushi, Raza, Sayyed, Mustafa Abo Mohsen, Sadi, A Abukhalaf, Moises, Cukier, Hugo, Gomez-Fernandez, Sebastian Shu Yip, Ximena Paola Vasquez Ojeda, Marie Dione Sacdalan, Piotr, Major, José, Azevedo, Miguel, F Cunha, Ahmad, Zarour, Eduard-Alexandru, Bonci, Ionut, Negoi, Sergey, Efetov, Viktor, Kochetkov, Andrey, Litvin, Jc Allen Ingabire, Georges, Bucyibaruta, Ntirenganya, Faustin, Sosthene, Habumuremyi, Alphonsine, Imanishimwe, Haragirimana Jean de Dieu, Emmanuel, Munyaneza, Isaie, Ncogoza, Abdourahmane, Ndong, Dejan, Radenkovic, Min Hoe Chew, Frederick, Koh, James, Ngu, Arpád, Panyko, Uros, Bele, Jurij Aleš Košir, Hassan, Daoud, Ana Maria Minaya Bravo, Umesh, Jayarajah, Dakshitha, Wickramasinghe, Mohammed Elmujtba Adam Essa Adam, Martin, Rutegård, Malin, Sund, Michel, Adamina, Eleftherios, Gialamas, Karoline, Horisberger, Muhammad, Alshaar, Abel, Huang, Varut, Lohsiriwat, Shane, Charles, Haithem, Jlassi, Arda, Isik, Hervé Monka Lekuya, Slava, Kopetskyi, Hayder, Alsaadi, Sattar, Alshryda, Osaid, Alser, Brittany, Bankhead-Kendall, Kerry, Breen, Hassan, Mashbari, Fernando Bonilla Cal, Hamza, Al-Naggar, Mayaba, Maimbo, Dennis, Mazingi, Tom, Abbott, Melika, Akhbari, Ruth, Benson, Shivam, Bhanderi, Bruce, Biccard, Edward, Caruana, Sohini, Chakrabortee, Reema, Chapatwala, Ainhoa, Costas-Chavarri, Andreas, K Demetriades, Anant, Desai, Salomone Di Saverio, Thomas, Drake, John, Edwards, Jonathan, Evans, Marco, Fiore, Samuel, Ford, Christina, Fotopoulou, Alexander, Fowler, Kaori, Futaba, Ian, Ganly, Harelimana Grace James, Ewen, Griffiths, Alessandro, Gronchi, Peter, Hutchinson, Gabriella Yael Hyman, Joseph, Incorvia, Ritu, Jain, Michael, Jenkinson, Tabassum, Khan, Stephen Richard Knight, Angelos, Kolias, Søren, Kudsk-Iversen, Tsun Yu Kwan, Elaine, Leung, Julio, Mayol, Siobhan, Mckay, John, G Meara, Emily, Mills, Susan, Moug, Akshay, Patel, Roberto, Perinotti, Henry, E Rice, Keith, Roberts, Andrew, Schache, Richard, Shaw, Neil, Smart, Matthew, Stephens, Grant, D Stewart, Ella, Teasdale, Peter, Vaughan-Shaw, Raghavan, Vidya, Naomi, Wright, Natalie, Zimmelman, Ervis, Agastra, Dariel, Thereska, Sergio Martin Lucchini, Veronica, Laudani, Carina, Chwat, Ivana Ines Pedraza Salazar, Diana Alejandra Pantoja Pachajoa, Agustin, Duro, José Alfredo Calderón Arancibia, Giuliana, D'Aulerio, Nagendra, Dudi-Venkata, Natasha, Egoroff, Shebani, Farik, Natalie, Lott, Jana-Lee, Moss, Sarah, Rennie, Lorwai, Tan, Uyen Giao Vo, David, Watson, David, Watters, Tim, Bright, Paul, Hollington, Xuanyu, Zhou, Hidde, M Kroon, Anthony, Farfus, John, Barker, Eleanor, Watson, Sean, Stevens, Haider, Latif, Amanda Caroline Dawson, Alwin, Chuan, Vijayaragavan, Muralidharan, Enoch, Wong, Travis, Ackermann, Maurizio, Pacilli, Russell, Hodgson, Alexander, Heriot, Peter, Choong, Wendy, Brown, Surjit, Lidder, Justin, Yeung, Luke, Traeger, Guillermo, Regalo, Ralph, Gourlay, Sarit, Badiani, Cherry, Koh, Soni, Putnis, Fayza, Haider, Ashrarur Rahman Mitul, Niels, Komen, Bert, Dhondt, Serge, Cappeliez, Manon, Pigeolet, Martijn, Schoneveld, Jasper, Stijns, Wouter, Oosterlinck, Nicolas, Flamey, Cyrille, Kpangon, Mouhamed, Agbadebo, Sèmèvo Romaric Tobome, Aldo Vieira Barros, Samuel Aguiar Júnior, Heloisa Galvão do Amaral Campos, Jefferson, Gross, Felipe José Fernandez Coimbra, Luiz Paulo Kowalski, Fabiana, Makdissi, Suely, Nakagawa, Joao Pedreira Duprat Neto, Jose Guilherme Vartanian, Guilherme, Yazbek, Stenio, C Zequi, Ronald, Flumignan, Natalia, Jaworska, Angela, Dell, Harsha, Shanthanna, Abdollah, Behzadi, Carolyn, Nessim, Michelle, Mozel, Pascal, St-Germain, Crispin, Russell, Gary, Groot, Najib, Safieddine, Duminda, Wijeysundera, Antoine, Eskander, Sami, Chadi, Shawn, Mackenzie, Alana, Flexman, Fernando, Heredia, Julio, Villanueva, Sofia, Waissbluth, Roberto, Macchiavello, Mario, I Escudero, Tyare, Fuentes, Ximena, Mimica, Dinimo Bolivar Saenz, Lina, Caicedo, Juan Pablo Alzate, Joaquin, Luna, Nestor Fabian Pedraza Alonso, Camilo Ortiz Silva, Juliana, Rodriguez, Liliana, Silva-Igua, Martha Luz Torres, Lina María Trujillo, Albaro José Nieto Calvache, Julián, Balanta-Melo, Rafael, Figueroa-Casanova, Oscar-Julián, García-Montoya, Carlos Andres Marulanda Toro, Marcela Velez Botero, Maria Clara Mendoza Arango, Eneida Diaz Martinez, Valentina Gutiérrez Perdomo, Emileth, Montenegro, Julia, Rodriguez-Abreu, Dolores, Mejía, Galal, Abouelnagah, Sameh, Shehata, Ahmed Hossam Eldin Fouad Rida, Ramy, A Hassan, Mahmoud, M Saad, Mohamed Reda Loaloa, Badr, Mostafa, Mohamed, Qassem, Mohamed, Fahmy, Hesham, Abozied, Ahmed, Y Azzam, Sherief, Ghozy, Asser, Sallam, Ahmed, Shehta, Mohamed, Abdelkhalek, Rehab, Samaka, Amr, Morsy, Ahmed Elshawadfy Sherif, Abraham, Negussie, Tigist, Fisseha, Kibruyisfaw, Shumbash, Metasebia, Abebe, Seid Mohammed Yasin, Yemisirach Bizuneh Akililu, Abebe, Megersa, Teshome, Tefera, Melatework, Assefa, Bahru, Atnafu, Bereket, Tsegaye, Yoseph Solomon Bezabih, Silamlak, Sisay, Kebebe, Bekele, Moa, Jira, Habtamu, Derilo, Eyueal, Degefa, Anteneh, Tadesse, Melkamu, Nidaw, Elise, Sarjanoja, Sylvie, Testelin, Sophie, Boucher, Lionel, Jouffret, Zaher, Lakkis, Alban Zarzavadjian Le Bian, Luke, Harper, Marc Danguy des Déserts, Benoît, André, Karem, Slim, Romain, Verhaeghe, Andrea, Police, Edouard, Girard, Alexandre, Chebaro, Armande Subayi Nkembi, Laurent, Arnalsteen, Quentin, Ballouhey, Diane, Mege, Clement, Jeandel, Emilie, Duchalais, Pierre-Alban, Bouche, Gilles, Manceau, Célia, Crétolle, Erik, Hervieux, Noémie, Girard, Agathe, Seguin-Givelet, Sebastien, Gaujoux, Belinda De Simone, Matthieu, Boisson, Damien, Bergeat, Fabien, Fredon, Francesco, Nappi, Radwan, Kassir, Aurélien, Scalabre, Federico, Migliorelli, Anne-Cecile, Ezanno, Barbara, Seeliger, Charlotte, Vaysse, Helene, Charbonneau, Vincent, Misrai, Olivier, Abbo, Martina Aida Angeles, Laurent, Brunaud, Ali, Modabber, Sebastian, Wolf, Carsten, Kamphues, Philipp, Höhn, Tim, R Glowka, Alexander Christopher Rokohl, Ulrich, Bork, Georg, Fluegen, Raymund, E Horch, Andrea, Schmedding, Andreas, Schnitzbauer, Helge, Eberbach, Daniel, Schlager, Fritz, Spelsberg, Lena, Keppler, Andreas, Hecker, Susanne, Wolfer, Ulrich, Ronellenfitsch, Christine, Nitschke, Christian, Peiper, Ibrahim, Hakami, Stefan, Welter, Karine, Nikolaieva, Andreas, Roth, Judith, Lindert, Konstantinos, Gousias, Anke, Rissmann, Valerie Catherine Linz, Nuh, Rahbari, Marie-Claire, Rassweiler-Seyfried, Anna Eleonora Gut, Jens, Gempt, Daniel, Reim, Arthur, Wagner, Alexander, M Keppler, Mircea Gabriel Stoleriu, Tim, Saier, Josef, Stadler, Julia Christina Kaiser, Stefan, M Brunner, Karin, Pfister, Jonas, Herzberg, Kai, Nowak, Tobias, Reinhard, Gregor, A Stavrou, Alfred, Königsrainer, Christian, Konrads, Markus, Quante, Simon, Laban, Silke von Pusch, Markus, Hirschburger, Johannes, Doerner, Armin, Wiegering, Ekaterini Christina Tampaki, Alitza Gutiérrez Ruiz, Alejandra, Rodas, Ana Lucía Portilla, Jacqueline, Carrera, Amalia Barrios Duarte, Megan, Lowey, Sabrina, Barillas, Atul, Suroy, Dhaivat, Vaishnav, Raghunandan Gorantlu Chowdappa, Irappa, Madabhavi, Dhananjaya, Bhat, Sunil Kumar Venkatappa, Sumit, Thakar, Kavitha, Jain, Aruna, Kumar, Manoj, Nagar, Tushar, Mishra, Arunkumar, Sekar, Anand, Gupta, Lileswar, Kaman, Madhivanan, Karthigeyan, Manjul, Tripathi, Ashwin, Rammohan, Sudheer Othiyil Vayoth, Anupama, Rajanbabu, Anbukkani, Subbian, Rahul, Gupta, Monish, Raut, Nissi, Evelyn, R Lavanya Kannaiyan, Anil, Matai, Sanjeev, Misra, Vishal, Bhende, Sathish, Muthu, Indranil, Ghosh, Abhishek, Sharma, Ankur, Bajaj, Shiv, Rajan, Gaurav, Agarwal, Pranay, Pawar, Philip, Alexander, M Vijayakumar Vijayakumar, Zeeshan, Hameed, Badareesh, L, Navneet Kumar Chaudhry, Lipika, Baliarsing, Satish, Dharap, Amruta, Kulkarni, Yuvaraja, Thyavihally, Rahul Deo Sharma, S Pramesh, C, Rajesh, Soni, Surya Kumar Dube, Shilpa, Sharma, Harvinder, Singh, Lovenish, Bains, Rahul, Ghodke, Ashwani, Kumar, Vivek, Sodhai, Suvendu, Maji, Somprakas, Basu, Chandrashekhar, Mahakalkar, Ravi, Kannan, Asif, Mehraj, Ranganath, N, Ashish, Phadnis, Yadev, I, Alfie, Kavalakat, Rohin, Mittal, Karthik Chandra Vallam, Hamed, Akhavizadegan, Esmaeil Rezghi Maleki, Naser Yousefzadeh Kandevani, Hilary, Ikele, Catherine, Mcnestry, Christina, Fleming, Stephen, O'Brien, Sami Abd Elwahab, Niall, Davis, Mohsen, Javadpour, Brendan, Mcdonnell, Clare, O Connor, Jarlath, Bolger, Cillian, Clancy, Stefanie, M Croghan, Noel, Donlon, Carolyn, Cullinane, Ben, Creavin, Muheilan, Muheilan, Helen, Earley, Syed Mohammad Umar Kabir, Muhammad, Fahadullah, Éanna, Ryan, Tara, Connelly, Daisuke, Hashimoto, Majdi Ali Alqudah, Amer, Alajalen, Rand, Y Omari, Abdulrahman, Qasem, Yazan, Alawneh, Amer, Ahmad, Omar, Aladawi, Bourhan, Alrayes, Hanan, Haidar, Shatha, Husain, Faisal, Qassem, Adnan, Sumadi, Ala'a Abu Salhiyeh, Balqees Mahmoud Al-Manaseer, Zaid, Alsunna, Hazim, Ra'Ed, Faten Reyad Bani Hamad, Amro, Abuleil, Elmi Ahmed Mohamed Jimaale, Marah, Abu-Mehsen, Noor, Olaywah, Omar, Wafi, Hazim, Ababneh, Luai, Abu-Ismail, Almu'Atasim, Khamees, Ahmad, Alkhatib, Raikhan, Bolatbekova, Mukhtar, Kulimbet, Talgat, Nurgozhin, Timur, Saliev, Baurzhan, Zhussupov, Ydyrys, Almabayev, Dilyara, Kaidarova, Khalil, Tamoos, Ahmed, Aqeelah, Alsnosy Abdullah Khalefa Mohammed, Faraj Al Maadany, Ghadah, Alkadeeki, Milad, Gahwagi, Wafa, Aldressi, Mohamed, Amnaina, Arowa Hassan Abdulrahman Alansari, Akram, Alkaseek, Ghozlan, Yagoub, Anass Ben Amer, Marwa, Salem, Ayman, Almugaddami, Dania, Burgan, Mohammed, Abdelkabir, Khayriyah, Alshareef, Rayet Al Islam Ben Jouira, Ayman, Meelad, Ahmad, Bouhuwaish, Sumayya Essayah Dwaga, Houda, Khalifa, Bushray, Almiqlash, Taha, Suliaman, Mohammed, Alawami, Fras, Elhajdawe, Hajir, Aboazamazem, Ibrahim, Ellojli, Ahmed, Msherghi, Ismail Ali Saleh, Mohammed, Alayan, Marcel Didier Ndayishyigikiye, Akutu, Munyika, Philipp, Plarre, David, W Borowski, Cameron, Wells, Rebecca, Teague, Brodie, Elliott, David, Kieser, Omar, Mohyieldin, Nick, Mcintosh, Cheyaanthan, Haran, Jasmin, King, Jeong, Ha, 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Mohammed Shadrul Alam, Akm Khairul Basher, Shahnoor, Islam, M Nazmul Islam, S, Sabbir, Karim, Muntasir, Faisel, Sadia, Khan, Iftekhar Ibne Mannan, Nawshin, Nazia, Malissa, Bentham, Sasha, Corbin, Alex, Doyle, Asha, Eastmond, Amelia, Haynes, Margaret, O'Shea, Emil, Phillips, Keisha, Walkes, Evgeniy, L Artyushkov, Mark, L Kaplan, Vladislav, Straltsov, Yauheniya, Litvina, Alexei, Lyzikov, Victor, E Tihmanovich, Mafalda, Borges, Tim, Brits, Nicolas De Hous, Stefan De Wachter, Tomas, Menovsky, Xavier, Mortiers, Dorien, Vermeulen, Nils, Vleminckx, Dirk, Ysebaert, Julie, Bontinck, Danny, Bulthé, Ricky, Rasschaert, Anne-Sophie Van Haver, Koen Van Belle, Laurence, Verstraeten, Manon, Vounckx, Sébastien, D'Ulisse, Yasmine De Bruyne, Badih El Nakadi, Tessely, Heloise, Matteo, Luisetto, Sotirios, Marinakis, Maxime, Maton, Claire, Viste, Elke Van Daele, Mathieu, Vandeputte, Marc, Duinslaeger, Daniel, Jacobs-Tulleneers-Thevissen, Yanina, Jansen, Ward, Janssens, Rastislav, Kunda, Nouredin, Messaoudi, 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Amanda Lira Dos Santos Leite, Laercio, Pol-Fachin, Tainá Santos Bezerra, Alcimar Lavareda Dos Santos Junior Alcimar, Robson, Amorim, Maria Eduarda Bellotti Leão, João José Corrêa Bergamasco, Cintia Cardoso Pinheiro, Rubem Alves Da Silva Neto, Elaine Francisca De Araújo, Lilian, Guimaraes, Tatiana, Liborio-Kimura, Isabelle Melo da Camara, Victor Ripardo Siqueira, Juan, Rodriguez, Jeancarllo, Silva, Rubem Alves Silva Junior, Cláudio, Cardoso, Andre, Guimaraes, Wislene Sarajane Moreira Alves, Agna Soares Da Silva Menezes, Marcelo, Araujo, Kattiucy, Brito, Josie Marcelle Lira Albuquerque, Jairo Alberto Dussan-Sarria, Andressa, Souza, Andre Ricardo Stüker, Tiago, Bresciani, Luciana Cadore Stefani, Leandro Totti Cavazzola, Tainá, Costa, Matheus, Dasqueve, Gustavo De Bacco Marangon, Otávio Ritter Silveira Martins, Josy, Rodrigues, Guilherme Roloff Cardoso, Brasil Silva Neto, Tilaê, Soares, Aline, Zanella, Lucas Torelly Filippi, Enilde Eloena Guerra, Ricardo Pedrini Cruz, Antonio Nocchi Kalil, Gustavo, Laporte, Moacyr, Salem, Murilo de Lima Brazan, Caio Antonio de Campos Prado, Elaine Christine Dantas Moises, Hilda Satie Suto, Alice Gadotti Yasuda, Ana Carolina Tagliatti Zani, Roberto Cardoso Cardoso Dos Santos, Geraldo, Duarte, Edwaldo Edner Joviliano, Carolina Lourenço Gomes Dos Santos, Silvana Maria Quintana, Edwin, Tamashiro, Fabiana Cardoso Pereira Valera, Marília Veccechi Bijos Zaccaro, Isis, Andreotti, Leonardo, Lima, Wilson Salgado Jr, Alonço da Cunha Viana Júnior, Lana, Moutinho, Cora Pichler de Oliveira, Isabela Trindade Martins, Isabela Vieira Toledo, Marco Antonio Correa Guimaraes-Filho, Rodrigo, Arrivabeno, Claudio Bovolenta Murta, Danniel Frade Said, Jose Pontes Junior, Felipe Guimarães Pugliesi, Paulo, Gregorio, Alessandro, Mariani, Fabio, Minamoto, Juliana Lourenço da Silva, Bruno, Muller, Caio, Zanon, Vladimir, C Carvalho, Ana Lucia Munhoz Lima, Priscila, R Oliveira, Jorge Dos Santos Silva, Mariana Faccini Teixeira, Marco, Kulcsar, Leandro, Matos, Kamilla 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Volpe, Arthur, Gatti, Caroline, Nardi, Ramon, Oliva, Layze Braz de Oliveira, Herica Emilia Félix de Carvalho, Ivonizete Pires Ribeiro, Gabriel Renan Soares Rodrigues, Álvaro Francisco Lopes de Sousa, Michel, Chebel, Paulo Henrique de Sousa Fernandes, Marcelo Augusto Faria Freitas, Juliano Rodrigues da Cunha, Zeenia, Ather, Dobromir, Dimitrov, Emil, Filipov, Elitsa, Gyokova, Ivelina, Ilieva, Tsvetomir, Ivanov, Martin, Karamanliev, Vasil, Neykov, Boyko, Atanasov, Nikolay, Belev, Paolina, Kamenova, Kaloyan, Tonev, Tsanko, Yotsov, Evguenia, Hristova, Kolyo, Spassov, Tsanko, Tsankov, Dragomir, Dardanov, Petar, Gribnev, Manol, Sokolov, James, Brown, John, Tanyi, Heather, Hurdle, Anthony, Maclean, Michael, Sander, Salim Al Riyami, Krittika, Bali, David, Bigam, Khaled, Dajani, Mehran, Anvari, Rafik, Bolis, David, Choi, Susan, Ellis, Michael, Gupta, Wael, Hanna, Dennis, Hong, Cynthia, Horner, Lea, Luketic, Peter, Moisiuk, Yaron, Shargall, Hilda, Alfaro, Nawar, Alkhamesi, Laura, Allen, Muriel, Brackstone, Eunice, Chan, Davy, Cheng, Jason, Chui, Nelson, Gonzalez, Brent, Lanting, S Danielle MacNeil, Robert, Mayer, Jacob, Mcgee, Mahesh, Nagappa, Nicholas, Power, Agya, Prempeh, Mehdi, Qiabi, Hassan, Razvi, Emil, Schemitsch, Herman, Sehmbi, Ushma, Shah, Yamini, Subramani, Edward, Vasarhelyi, Kelly, Vogt, Homer, Yang, Ali Ghorbani Abdehgah, Saba, Balvardi, Liane, Feldman, Julio Flavio Fiore Jr, Melissa, Hanson, Brent, Hopkins, Pepa, Kaneva, Lawrence, Lee, Julia, Leonard, Jad, Abou-Khalil, Andre, Martel, James, Stevenson, Susan, Lee, Richard, Merchant, Samuel, Avoine, Laurence, Belanger, Mathieu, Belanger, Etienne, Belzile, Anne-Sophie, Blais, Sofia, Boucher-Kovalik, Cindy, Boulanger-Gobeil, Etienne, Cardinal, Pierre-Olivier, Champagne, Jonathan, Cloutier, Simon, Corriveau-Durand, Mathieu, Cote, Maxime, Cote, Mathilde, Côté, Valérie, Courval, Suzanne, Demers, Christine, Desbiens, Mehdi El Ouazzani, Karine, Girard, Ève-Marie, Girard, Karo, Gosselin, Helene, Khuong, Nathalie, Labrecque, Eve-Lyne, Langlais, Audrey, Larouche, Andréane, Lavallée, Madeleine, Lemyre, Sarah, Maheux-Lacroix, Melissa, Marien, Patrick, Marin, Julie, Mauger, Genevieve, Milot, Sylvie, Nadeau, Sebastien, Nguyen, Mélinda, Paris, Stéphane, Pelet, Marie, Plante, Carole, Plante, Frederic, Pouliot, Marie Claude Renaud, Mathilde, Sarlabous, Isabelle, Schmit, Raphaël, St-Germain, Brian, Johnston, Nicole, Labine, Amit, Persad, Hong, Pham, Melissa, Wood, Riordan, Azam, Amit, Atrey, Andrew, Beckett, Daniel, Cohen, Sunit, Das, Julian, Daza, Cheryl, Dunkerton, Yosef, Ellenbogen, Ciara, Hanley, Sorcha, Kellett, Amir, Khoshbin, Karim, Ladha, Amanda, Mcfarlan, Spencer, Montgomery, Janneth, Pazmino-Canizares, Hrishikesh, Suresh, Ravleen, Vasdev, Maira, Ahmed, Fred, Gentili, Aristotelis, Kalyvas, Matthias, Millesi, Can, Sarica, Taariq, Shaikh, Leslie, St, Jacques, Raha, Tabasinejad, Gelareh, Zadeh, Marvin, Hsiao, Nicole, Jedrzejko, Peter, Black, Claire, Broe, Charlotte, Dandurand, Rebecca, Grey, Philemon, Leung, Andrew, Lindberg, Juan Mata Gutierrez, Kelly, Mayson, Adam, Meneghetti, Drew, Phillips, John, Street, Carolina Carvajal Calderón, Daniel Alejandro Donoso Pizarro, Esteban, Fernández, Ángela, Molero, Nastassja, Mutarello, Jorge Núñez Lucic, Katherine Ochoa Gaete, Catalina Ruiz Lopez, Rina, Sepúlveda, Camila, Ulloa, Mauricio, Barreda, Matias, Dallaserra, Matías Günther Wood, Mauricio Sandoval Tobar, Francisco, Garcia-Huidobro, Jimena, Dona, Carolina Soto Diez, Daniel, Igor, Javier, Mena, José Tomás Reyes, Catalina Arredondo Soto, Jose, Campos, Javier, Gonzalez, Barbara, Rivera, Jaime, Altamirano-Villarroel, David, Cohn, Luis Marin de Amesti, Pedro, Recabal, Camilo, Sandoval, Bruno Catoia Fonseca, Monica, Contador, Andres, Hodali, Camila, Pincheira, Andrea Ramos Mantilla, Claudio Salas Garrido, Janina, Torres, Marco, Valenzuela, Zehua, Chen, Jiankun, Hu, Jin, Tao, Kun, Yang, Yuexin, Zhang, Yuan, Chen, Wang, Cunchuan, Wah, Yang, Xueli, Bai, Tingbo, Liang, Tao, Ma, Ning, Pu, Hanlin, Yin, Camilo, Caicedo, Diego Felipe Tellez Beltran, Alejandro, Escobar, Carolina Perez Granados, Lisbeth Alexandra Urueña Pinzon, Carlos, Bonilla, Diana, Santana, Oscar, Serrano, Andrea, Garcia, Fernando Giron Luque, Nasly, G Patino-Jaramillo, Laura Giselle Contreras Baquero, Nestor Augusto Muñoz Botero, Jhoana Andrea Murillo Castellanos, Andrea Juliana Vega Calvera, Lina, M Acosta Buitrago, Maria Paz Bohórquez-Tarazona, Paulo Andrés Cabrera Rivera, J Felipe Casas, Valeria Cormane Alfaro, Julian, Corso, Bayron, Guerra, Albert Franz Guerrero-Becerra, Akram Kadamani Abiyomaa, Felix Ramon Montes, Manuel Santiago Mosquera Paz, Camilo Andrés Polanía Sandoval, Natalia, Quintana, Carlos Fernando Roman Ortega, Carlos, Santacruz, Manuela, Téllez, Camilo Alejandro Velandia Sánchez, Fernando, Arias-Amézquita, Luis Felipe Cabrera Vargas, Natalia Cortes Murgueitio, Elkin, Escorcia, Kemel Ahmed Ghotme Ghotme, Jorge Luis Gomez-Mayorga, Alvaro Felipe Guerrero Vergel, Gabriel, 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Juliana Sanchez, David Mauricio Solano Varela, Paula Torres Gomez, Marcial, Trillos, Ana María Vargas Patiño, Felipe, Vargas-Barato, Juan Pablo Villate Leon, María Alejandra Wagner Useche, Jesus, Acosta, Adriana, Almeciga, Luis Becerra Mendez, Rafael Jose Beltran, Ana, Bonilla, Miguel, Buitrago, Marino, Cabrera, Pedro Hernando Calderon Quiroz, Diego, Camacho-Nieto, Carlos Andrés Carvajal Fierro, Sergio Cervera Bonilla, Sandra, Diaz, Helena, Facundo, Jorge, Forero, Mauricio Garcia Mora, Karena Victoria Garcia Tirado, German Fabian Godoy Perez, Felipe, Gonzalez, Oscar, Guevara, Jairo Alonso Hernandez, David Ricardo Herrera Mora, Juan David Lalinde, Carlos, Lehmann, Eduardo Leon Llanos, Byron Eduardo Lopez De Mesa Lopez, Ivan, Mariño, Monica, Medina, Raúl Eduardo Pinilla Morales, Angela, Puerto, Andrea, Quintero-Ortíz, Juliana Rendón Hernández, Elio Fabio Sánchez Cortés, Alvaro Eduardo Sánchez Hernández, Raul, Suarez, Oscar, Suescun, Marco, Vanegas, Rodolfo, Varela, Jorge Luis Velez 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Vanja, Žufić, Matea, Zuzul, Srdan Ante Anzic, Iva, Carevic, Dubravka Heli Litvic, Duska, Markov-Glavas, Tatjana Savic Jovanovic, Goran, Augustin, Jerko, Biloš, Bojan, Biočina, Vedrana, Biosic, Dino, Bobovec, Iva, Botica, Boris, Bumber, Petra, Čerina, Katarina Duric Vukovic, Hrvoje, Gasparovic, Lucija, Gatin, Kresimir, Grsic, Ika Gugić Radojković, Ivan, Jelčić, Zeljko, Kastelan, Juraj, Kolak, Tomislav, Kulis, Kristian, Kunjko, Marjan, Maric, Marcel Marjanović Kavanagh, Borna, Milicic, Trpimir, Morić, Miram, Pasini, Luka, Penezić, Drago, Prgomet, Ratko, Prstacic, Andreja, Prtorić, Rudolf, Radojković, Ivan, Romić, Tomislav, Sečan, Dora Škrljak Šoša, Juraj, Slipac, Mislav, Tomic, Jurica, Zedelj, Toni, Zekulić, Zoran, Zimak, Mia, Lorencin, Ivica, Luksic, Matija, Mamic, Norberto Miranda Espinsa, Ailén Sánchez Cruz, Stavros, A Antoniou, Michael, Kakas, Yiannis, Panayiotou, Heyam, Almezghwi, Kalbim, Arslan, Hasan, Besim, Ali, Özant, Necdet, Özçay, Georgios, Kokkinos, Panayiotis, Papatheodorou, Ioanna, Pozotou, Olga, Stavrinidou, Anneza, Yiallourou, Lukáš, Burda, Michal, Dosoudil, Lubomir, Martinek, Wladyslaw, B Gawel, Milan, Lerch, Matúš, Peteja, Jan, Žatecký, Toman, Daniel, Martin, Formánek, Veronika, Javurkova, Markéta, Kepičová, Nicole, Macečková, Petr, Matousek, Ondrej, Simetka, Petr, Vavra, Karol, Zelenik, Jan, Černý, Sami, Khan, Robert, Lischke, Martin, Přibyl, Temoore, Younus, Yenuksha, Amarasena, Karel, Klíma, Setareh, Pirmorad, Zuzana, Hotová, Aleš, Mladěnka, Nulvin, Bozo, Julie Lykke Harbjerg, Louise, Hviid, Helle, Ø Kristensen, Mira, Mekhael, Mette Fugleberg Nielsen, Laerke, Paulsen, Saija, Sinimäki, Louise Zinck Mogensen, Peter, Bonde, Anders Lyng Ebbehøj, Anne-Sophie, Fenger, Aleksander Fjeld Haugstvedt, Christine Hangaard Hansen, Maria Lovisa Jönsson, Lars, N Jorgensen, Peter-Martin, Krarup, Anne-Louise, Lihn, Christian, Meyhoff, Helena, Otte, Anas Ould Si Amar, Henrik, Palm, Nis, Schlesinger, Henry, Smith, Ida, Tryggedsson, Anne Reiss Axelsen, Jens Kristian Baelum, Mark Bremholm Ellebaek, Signe Bremholm Ellebaek, Anders, Hogh, Maria, Acosta, Yancy, Acosta, Pedro, Baez, Luis Alfredo Betances, Luis Rodolfo Bonilla, Daniel, Cabreja, Wilton Cabrera Cruz, María Mabel Collado Expósito, Aldo, Crespo, Octavio, Cruz-Pineda, Claudio Samuel D'óleo García, Pedro, Díaz, Remberto, Escoto, Jatnna, Figueroa, Luis, Garcia, U Garcia-Dubus Rodriguez, Humberto, Gomez, Adrian, Grullon, Fatherin, Guerrero, Lillian, Guzman, Thelma Rocío Jiménez Mosquea, Engels, Lazala, Ariela, Lopez, Leeany Maletta Francisco, Jhomayri, Mercado, Herisardy, Munoz, Jeffrey, Paulino, Maricely Ambar Perez Fernandez, Rudeily, Reyes, Lilia Rosa Reyes Guilamo, Ruben, Rivas, Claudia Alejandra Rivas Torres, Ada, Rodríguez, Madelin Tamar Rosario Villa, Eleazar, Santana, Joel, Soto, Nassin, Tactuk, Cristina Kristel Tonos Sardiñas, Raul, Ubinas, Jose, Victoria, Aaron, Villegas, Ricardo, Acra-Tolari, Larissa, Beltran, Luis Fernand Betances, Alejandro, Blaubach, Denisse Idalia Campos Mejía, Nathalia, Capellan, María Fernanda Cedeño Bruzual, Saray Cordero Spencer, Gabriella Cuevas Lantigua, Luis De Jesus, Gabriela, Díaz, Pedro Pablo Díaz Vásquez, Karla, Disla, Jesús Antonio Echavarría Uceta, Paola Irina Eusebio Jimenez, Damaris, Fernandez, Marlin Fernandez Camilo, Jiomar Manuel Figueroa Germosen, Maruel, Fortunato, Leyla Gabriel Fernández, Alicia German Dihmes, Héctor, Herrera, Elizabeth Leon Cuevas, Jose Alejandro Mata, Mirian Mateo De La Cruz, Dolores Mejia De la Cruz, Marcos, Mirambeaux, Fabio Ortiz De La Cruz, Henry, Paulino, Gabriela, Pelletier, Merycarla, Pichardo, Rodriguez Pumarol Próspero Enrique, Julio, Rivas, Ann Stephany Sanchez Marmolejos, Roman Santana Santana, Irina Suero Almanzar, María Magdalena Vásquez Sánchez, Nicolás, Campuzano, José Ricardo Negrete Ocampo, Maria, Armas, Alvaro Santiago LeMarie Guerra, Gustavo León Vizcaya, Omar Ahmed Abdelwahab, Ahmed, O Elmehrath, Mohammed Ezzat Mostafa, Gamal, Amira, Ibrahim, Sallam, Mohamed, Sherief, Ahmed, Abdelmajeed, Mostafa, Abdou, Ahmed Abo Shanab, Aya, Abodeeb, Roger, Aboelkhel, Nour Eldin Abosamak, Amna Abou Bakr, Samar, Aboubakr, Yossof, Abouelnagah, Omar, Aboumadawy, Dina, Adel, Nermeen, Afifi, Sara, Ahmed, Abedelrahman, Ahmed, Mohamed Al Sayed, Abdelrahman, Alberkamy, Mohamed, Aldahma, Ahmed, Ali, Mostafa, Ali, Mohamed Amin Bakr, Alaa, Anter, Doaa, Asal, Fouad, Ashoush, Olfat, Ashraf, Abdel Rahman Ashraf, Rewan, Atta, Salma, Badr, Mohamed, Bahaaeldin, Mostafa, Bastawesy, Sara, Darwish, Rasha El Kharashy, Ziad, Elassar, Seifeldin, Elbadawy, Dina, Elkhity, Alromisaa, Elsaka, Manal, Elsayed, Mostafa Elsayed Elsayed Hewalla, Mohamed, Elshafey, Youssof, Eshac, Fares, Eshac, Amr, Essameldin, Moataz, Ewedah, Yara, Ezz, Mohamed, Farag, Inas, Gadelkarim, Ziyad, Gadelrab, Dina, Gamal, Mira, Ghaly, Nathalie, Girgis, Omar, Gouda, Nancy, H El Goweini, Nour, Hafez, Youssef, Hafez, Abdelkader Hamed Abdin, Rodina, Hanno, Aliaa, Hassanin, Mohamed, Hassanin, Hanan, M Hemead, Abdelrahman, Hemida, Maher, Hosain, Ahmed, Hossam, Hamza Hussein Aly Salama Aly, Mohamed, Ibrahim, Abdelrahman, Ibrahim, Nourhan, Ibrahim, Noha Mohamed Salah Ibrahim Moussa Hamouda, Monica, Iskander, Islam, Khalifa, Mostafa, Kotb, Nada, Mahmoud, Alaa Mahmoud Abo Shabana, Nora, Mamdouh, Maram, Metwalli, Khaled, Metwally, Mahmoud, Moghazy, Moustafa, Mohamed, Mohamed, Mourad, Esraa, Moustafa, Kamilia, Mubarek, Nour Eldin Nader, Ahmed, Nasser, Samaa, Omar, Mostafa Shehata Qatora, Mohamed, Ragab, Marawan, Ragal, Dina, Ramadan, Rana, Ramadan, Sara, Ramadan, John, Romany, Ahmed, Sabry, Yasmeen, Said, Sara, Salamah, Ahmed Mostafa Saleh, Mona Saleh Mesbah Mohamed Elkaffas, Alaa, Salem, Hashem, Salim, Ahmed, Samih, Ahmed Samir Abdelaal, Ahmed, Shaheen, Yassmeen Sharafeldin Mohammed, Abdelrahman, Shehata, Mohamed, Shemeis, Karim, Shenit, Mennatallah, Sheta, Asmaa, Soffar, Yousef, Tanas, Ahmed, Tarek, Nermin, Yehia, Omar Khaled Mohamed Eid, Ammar Yasser Abdulfattah, Ahmed, Elsaghir, Mohamed Fouad Elganainy, Omar Ibrahim Elsayed, Omar, Elmandouh, Omar, Hamam, Mostafa Ahmed Shehata, Areej, A Abdelaziz, Ahmed, M Abbas, Wael Abd El-Ghani, Hossam Aldein, S Abd Elazeem, Mustafa Abd Elsayed, Ahmed Yassien Abd-Elkariem, Shimaa, Abdalla, Mahmoud, Abdel-Aleem, Khaled, Abdelazeem, Mahmoud, Abdelfattah, Mohammed, Abdelhafez, Mohamed, M Abdelkarem, Ali, Abdelraouf, Lamess, Abdullaha, Hossam, Abubeih, Moaiad Eldin Ahmed, Nagm Eldin Abu Elnga Ahmed, Ahmed, Ahmed, Sarah Ahmed Saad, Sherif, Alaa, Ali, Alhussaini, Abdelrahman Ahmed Abdelrahman Ali, Ibrahim, Ali, Wagdi, Ali, Mohamed Ali Mohamed, Mohamed, Ashraf, Muhammad, Bassiouni, Mohamed, G El-Adawy, Mohammad, El-Sharkawi, Hussein, Elkhayat, Khaled, Elmaghraby, Shady, Elsdfy, Mohamed, Elsharkawy, Almoutaz, Eltayeb, Mohamed Esmat Mohamed, Esraa, Essam, Mahmoud, Fahd, Osama, Farouk, Rabea, Gadelkareem, Ahmed, Ghoneim, Mohammed Hamada Takrouney, Ahmed, Hamdan, Abd El-Rahman Hamed, Ahmad, Hasan, Mohamed Abdelghafor Hassanin, Islam, Hawal, Kerollos, Henes, Mohamed Omar Herdan, Helal, F Hetta, Omar, Ibrahem, Mostafa, Ibrahim, Islam, H Ibrahim, Mahmoud, Kamel, Mohamed, Khallaf, Shrouk, M Elghazaly, Abobakr, Mahfouz, Osman, Mahmoud, Wesam, Mahran, Doha, Mahrous, Abadeer, Marsis, Ayman, Mohamed, Aliae Mohamed Hussein, Amr Mohamed Sayed, Mahmoud, M Mohammed, Ahmed, Mokhtar, Fatma, A Monib, Ahmed, Nageeb, Mariam Albatoul Nageh, Mohammed, Nageh, Nehal Gamal Omar, Abdelrahman, Ragab, Abdelrahman, Ramadan, Abdallah Rashad Temerik, Aya, Sabry, Hadeer, Safwat, Omar, Salah, Ahmed Elhussiny Salah Mahmoud, Ahmed, Saleh, Mahmoud, Sallam, Ahmed, Samir, Reem, Sayad, Esraa, Sayed, Ahmed, S Sedik, Mohammed, Shahine, Antonios, Soliman, Wael, Soliman, Mohamed Gamal Taher, Randa, Wanees, Ebrahim Ahmed Yousof, Ahmed, Youssef, Omar Zein Elabedeen, Ahmed Emad Sayed Hassan, Shrouk Abdel Fattah, Ahmed, Abostate, Samar, Ali, Saad Ali Saad Salama, Mohamed, Allam, Ahmed, Alsadek, Ebrahim, Arafa, Ahmed, Barakat, Mohammed El Sherpiny, Gehad, Elbehairy, Mahmoud, Eleisawy, 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Krause, Frank, Meyer, Salmai, Turial, Bilal, Al-Nawas, Marco Johannes Battista, Jan, Goedeke, Annette, Hasenburg, Julia, Heider, Lena Katharina Mueller, Simon, Zeller, Sina Louisa Patrizia Jentschura, Karl-Friedrich, Kowalewski, Maximilian, Kriegmair, David, Männle, Christoph, Reissfelder, Nicole, Rotter, Claudia, Scherl, Steffen, Seyfried, Andreas, Kirschniak, Jens, Rolinger, Peter, Wilhelm, Franz, G Bader, Stephanie, Ottl, Alexandra Viktoria Behr, Alexandros, Diamantis, Andreas, Fichter, Florian, Grill, Matthias, Heck, Daniel, Jira, Michael, Kallmayer, Florestan, Koll, Stefan, Luhne, Bernhard, Meyer, Robert, Patachia, Ilaria, Pergolini, Seyer, Safi, Christoph, Schäffer, Moritz, Schirren, Helmut, Wegmann, Markus, Wirth, Zhaojun, Zhu, Ughur, Aghamaliyev, Mahmoud, Almaghrabi, Wolfgang, Böcker, Jan, Bruder, Konstantin, Frank, Viktoria, Herterich, Verena, Huber, Matthias, Ilmer, Christian, Kammerlander, Carl, Neuerburg, Viktor, H von Ehrlich-Treuenstätt, Jens, Werner, Nikolaus, Börner, 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Santhosh Kumar Sampengere Annayappa, Tulika, Agrawal, Premkumar, Anandan, Aditya, Baindur, Manjunath, Bd, Savitha, C, S Anitha, G, Sandeep, Harigond, K Tejeswini, K, Venkatesh, Kesarla, Mallikarjuna, Manangi, B Hareesh, P, Jyothi K, R Ranjan, Athish, Shetty, Santhosh Shivashankar Chikkanayakanahalli, Tanvi, Sunil, Shreya, Syamala, Kavya, Tharanath, Aditya, Atal, Niranjana, Rajagopal, S Srinath, B, Vinod, Nk, L Thirumanikandan, P, Nitu, Mishra, Sushruta, Shrivastava, Rekha, Wadhwani, Reyaz, Ahmad, Zainab Ahmad Haq, Prateek, Behera, Ritika, Dhurwe, Rehan, Haq, Vaibhav, Jain, Anuj, Jain, Sunaina, Karna, Kameshwarachari, Pushpalatha, Sumit, Raj, John Ashutosh Santoshi, Pooja, Singh, Virendra, Verma, Vaishali, Waindeskar, Moorat Singh Yadav, Zaheda, Aziz, Koyel, Chakraborty, Preetam, Chappity, Gurudip, Das, Debajyoti, Datta, Saubhagya Kumar Jena, Madhabananda, Kar, Susanta, Khuntia, Pankaj, Kumar, Ravi, Kumar, Aswathi, Kv, Abhijeet, Mishra, Swastik, Mishra, Yash, Mittal, Dillip, Muduly, 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Somil, Jaiswal, Yashpal, Jaware, Navneet, Kala, Ruchi, Karnatak, Apjit, Kaur, Vijay, Kumar, Manoj, Kumar, Ambrish, Kumar, Upander, Kumar, Amit Kumar Shrivastava, Seema, Mehrotra, Brijesh, Mishra, Anand, Mishra, Namrata, Bal Krishna Ojha, Ahmad, Ozair, Uma Shanker Pal, Amita, Pandey, Nancy, Raja, Pooja, Ramakant, Ashutosh, Roy, Rekha, Sachan, Satyanarayan, Sankhwar, Pushp, Sankhwar, Divya, Sarin, Ayushi, Shukla, Sushil, Singh, Mohit, Singh, Kul Ranjan Singh, Renu, Singh, Uma, Singh, Urmila, Singh, Vandana, Solanki, Abhinav Arun Sonkar, Chhitij, Srivastava, Parijat, Suryawanshi, Vivek, Tewarson, Rajat, Verma, Manju Lata Verma, Awdhesh, Yadav, Gyan, Chand, K M, M Vishvak Chanthar, Dileep, Hoysal, Anjali, Mishra, Nitin, Batra, Arun, Bhatti, Rupali, Chopra, Shakina, David, Tapasya, Dhar, Uma Kant Dutt, Rohini, Dutta, Sumir, Gandhi, Parvez David Haque, B Paul Sudhakar John, Sreejith Kannummal Veetil, Gurvinder, Kaur, Navneet, Kumar, Anil, Luther, Anupam, Mahajan, Amit, Mahajan, Kavita, 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Venkateshwaran, Narasiman, Meer, Chisthi, Gejoe, George, Harihara, Jothi, Dilber, Pareed, Godwin David, C Mathew, Livingston, Abel, Mansi, Agrawal, Rabindranath, B, Manish, Baldia, Ravi Kishore Barla, Manisha, Beck, Santosh, Benjamin, Jeremy, Bliss, Lisa, Cherian, Sreekar, Devarakonda, Geley, Ete, Arun Jacob Philip George, Amish, Gohil, Deeptiman, James, Mark Ranjan Jesudason, Lallu, Joseph, Treasa, Joseph, Kathir, Joyson, Gomathi, Karnan, Albert, Kota, Pushplata, Kumari, Anitha, Loganathan, Vasanth Mark Samuel, John, Mathew, Senthil, K Nathan, Joby Elizabeth Ninan, Ajay, Philip, R Priyadarshini Priyadarshini, Suganya, S, Habie, Samuel, Gilbert, Samuel, Daniel, Selvaraj, Srujan, Sharma, Suraj, Surendran, D Hariharan, T, Santhosh Kumar Thangaraj, Vinotha, Thomas, Varghese, Thomas, John, K Thomas, S Harish, Y, Manobhiram, Boggavarapu, Murali Krishna Voonna, Marilaeta Cindryani Lolobali, Christopher, Ryalino, Tjokorda Gde Agung Senapathi, Mahadewa, Tjokorda, I Made Gede Widnyana, Dita, 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Giuseppe, Esposito, Federica, Frongia, Adolfo, Pisanu, Mauro, Podda, Nicola, Cillara, Alessandro Cannavera Putzu, Raffaele, Sechi, Emmanuele, Abate, Massimiliano, Casati, Letizia, Laface, Marcello, Schiavo, Fabio, Marino, Fabrizio, Perrone, Paolo, Annicchiarico, Alessandro, Cappellani, Matteo, Cavallo, Alessia, Giaquinta, Rossella, Gioco, Massimiliano, Veroux, Pierfrancesco, Veroux, Antonino, Zanghì, Antonio, Cianci, Arturo Lo Giudice, Maria Grazia Matarazzo, Giorgio Ivan Russo, Giuseppe, Sarpietro, Carmen Emanuela Scandura, Ida, Barca, Adriano, Carnevali, Antonio, Carpino, Maria Giulia Cristofaro, Gilda De Paola, Giuseppe, Giannaccare, Daniela, Novembre, Giuseppe, Sammarco, Vincenzo, Scorcia, Angeli Christy Yu, Marcello, D'Andrea, Lorenzo, Mongardi, Luigino, Tosatto, Mirko, Barone, Felice, Mucilli, Angelo, Muraglia, Ottavia, Caserini, Domenico Benvenuto Giuliani, Marco, Monti, Alessia, Morello, Edoardo, Segalini, Felice, Borghi, Desiree, Cianflocca, Alberto, Daniele, Danilo, Donati, 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Massimiliano, Gennaro, Lorenzo, Giannini, Marcello, Guaglio, Marco, Guzzo, Andrea, Leva, Alberto, Macchi, Elena, Manzo, Fabio, Martinelli, Ilaria, Mattavelli, Vincenzo Maria Mazzaferro, Francesco, Raspagliesi, Luigi, Rolli, Laura, Sala, Roberto, Salvioni, Mario, Santinami, Silvia, Segattini, Luca, Sorrentino, Carlotta, Zaborra, Alexandre, Anesi, Gianmaria Casoni Pattacini, Mattia Di Bartolomeo, Francesca, Pecchini, Arrigo, Pellacani, Micaela, Piccoli, Federico, Fusini, Andrea, Gattolin, Marco, Migliore, Roberto, Rimonda, Diego, Sasia, Elisabetta, Travaglio, Federica, Brunetti, Marco, Cereda, Marco, Ceresoli, Luca, Cigagna, Cristina, Dell'Oro, Alessandro, Fogliati, Robert, Fruscio, Tommaso, Grassi, Maini Marzia Isabella, Luca Carlo Nespoli, Massimo, Oldani, Sara, Ornaghi, Nicolò, Tamini, Carmine, Antropoli, Antonio, Castaldi, Alessio, Palumbo, Gaia, Altieri, Umberto, Bracale, Francesco, Corcione, Marcello De Luca, Giovanni Domenico De Palma, Maria Michela Di Nuzzo, Ruggero, Lionetti, 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Matteo, Filardo, Marika Sharmayne Milani, Franco, Ragni, Andrea de Manzoni Garberini, Edoardo, Baldini, Diana, Carpaneto, Michele, Cauteruccio, Corrado, Ciatti, Luigi, Conti, Serena, Gattoni, Pietro, Maniscalco, Gerardo, Palmieri, Calogero Puma Pagliarello, Giuseppe, Caristo, Raffaele, Galleano, Michele, Malerba, Marcello, Calabrò, Francesca, Farnesi, Elia Giuseppe Lunghi, Andrea, Muratore, Nicoletta Sveva Pipitone Federico, Joel Reuben Abel, Lorenzo, Andreani, Darienzo, Antonio, Vittorio, Aprile, Riccardo, Balestri, Giacomo, Benettini, Stefano, Berrettini, Luca, Bruschini, Massimo, Chiarugi, Federico, Coccolini, Simone, Colangeli, Camilla, Cremonini, Lodovica Cristofani Mencacci, Iacopo, Dallan, Silvia De Santi, Gregorio Di Franco, Lorena Di Girolami, Giacomo, Fiacchini, Niccolò, Furbetta, Stylianos, Korasidis, Marco, Lucchi, Andrea, Morandi, Luca, Morelli, Serena, Musetti, Carlo Maria Neri, Matteo, Palmeri, Miriana, Picariello, Francesco, Porcelli, Marco, Puccini, Nicolo, Roffi, Erica, Statuti, Dario, Tartaglia, Alberto, Tonelli, Matteo, Vianini, Gianluca, Baronio, Mauro, Montuori, Enrico, Pinotti, Stefano Maria Massimiliano Basso, Federica, Maffeis, Paolo, Ubiali, Lorenzo, Aguzzoli, Saverio, Coiro, Giuseppe, Falco, Vincenzo Dario Mandato, Valentina, Mastrofilippo, Simone, Mele, Caterina, Baldi, Carlo, Corbellini, Gianluca Matteo Sampietro, Massimo, Dugo, Mauro, Garino, Chiara, Marafante, Antonio, Masciandaro, Elisabetta, Moggia, Alessandra, Murgese, Felice Eugenio Agro, Gabriella Teresa Capolupo, Filippo, Carannante, Marco, Caricato, Vincenzo, Denaro, Erica, Mazzotta, Rocco, Papalia, Giuseppe, Pascarella, Alessandro, Strumia, Biagio, Zampogna, Matteo, Cinquepalmi, Marco, Colasanti, Celeste Del Basso, Federica, Falaschi, Nicola, Guglielmo, Roberto Luca Meniconi, Alessandra, Pecoraro, Sofia, Usai, Giacomo, Crescentini, Antonella, Larcinese, Emanuele, Picone, Giovanni, Sinibaldi, Annamaria, Agnes, Salvatore, Agnes, Sergio, Alfieri, Francesco, Belia, Valentina, Bianchi, Giuseppe, Bianco, Alberto, Biondi, Paola, Campennì, Valerio, Cozza, Sabatino, D'Archi, Domenico, D'Ugo, Veronica De Simone, Marta Di Grezia, Sofia Di Lorenzo, Federica, Ferracci, Valeria, Fico, Gianluca, Franceschini, Pietro, Fransvea, Giulio, Gasparini, Luca, Gordini, Antonio La Greca, Francesco, Litta, Celestino Pio Lombardi, Angelo Alessandro Marra, Angelo, Parello, Marco Maria Pascale, Romeo, Patini, Gilda, Pepe, Roberto, Persiani, Caterina, Puccioni, Carlo, Ratto, Fausto, Rosa, Gianmarco, Saponaro, Lorenzo, Scardina, Tedesco, Silvia, Giuseppe, Tropeano, Maria, Benevolo, Daniele, Bugada, Flaminia, Campo, Maria Gabriella Dona, Valentina, Manciocco, Paolo, Marchesi, Riccardo, Mastroianni, Francesco, Mazzola, Silvia, Moretto, Raul, Pellini, Gerardo, Petruzzi, Barbara, Pichi, Giuseppe, Simone, Gabriele, Tuderti, Jacopo, Zocchi, Luigi Marino Cosentino, Andrea, Sagnotta, Roberta, Angelico, Vittoria, Bellato, Michela, Campanelli, Marzia, Franceschilli, Michele, Grande, Giorgio, Lisi, Tommaso Maria Manzia, Lorenzo, Petagna, Bruno, Sensi, Giuseppe, Sica, Leandro, Siragusa, Giuseppe, Tisone, Marco, Assenza, Barbara, Binda, Massimo, Biondi, Gioia, Brachini, Placido, Bruzzaniti, Mauro, Casagrande, Flavia, Ciccarone, Pierfranco Maria Cicerchia, Bruno, Cirillo, Daniele, Crocetti, Giancarlo, D'Ambrosio, Vito, D'Andrea, Francesca De Felice, Giorgio De Toma, Carlo Della Rocca, Giulia, Duranti, Pietro, Familiari, Enrico, Fiori, Giovanni Battista Fonsi, Alessandro, Frati, Stefania La Rocca, Filippo La Torre, Pierfrancesco, Lapolla, Giovanni, Marruzzo, Simona, Meneghini, Andrea, Mingoli, Andrea, Picchetto, Antonella, Polimeni, Diego, Ribuffo, Maurizio, Salvati, Antonio, Santoro, Paolo, Sapienza, Luigi, Simonelli, Valentino, Valentini, Martina, Zambon, Giuseppa, Zancana, Emma, Zuppi, Simone, D'Annunzio, Cosimo De Nunzio, Silvia, Fiorelli, Mohsen, Ibrahim, Chiara, Loffredo, Domenico, Massullo, Cecilia, Menna, Rocco, Monica, Massimiliano, Pelli, Erino Angelo Rendina, Leonardo, Teodonio, Andrea, Tubaro, Giulio, Argenio, Giulio, Accarino, Accarino, Giancarlo, Antonio Nicola Giordano, Luca, Cardinali, Elisa, Sebastiani, Grazia, Travaglini, Erika, Andreatta, Emanuele Luigi Giuseppe Asti, Daniele, Bernardi, Luigi, Bonavina, Caterina, Froiio, Andrea, Lovece, Chiara, Copelli, Alfonso, Manfuso, Pasquale Di Maio, Marco, Giudice, Oreste, Iocca, Rosario, Cennamo, Tommaso, Cornali, Francesco Di Marzo, Cristian, Altana, Francesco, Bussu, Giampiero, Capobianco, Anna Giacomina Carta, Sandro, Ciccarello, Maria Laura Cossu, Pietrina, Cottu, Giacomo De Riu, Salvatore, Dessole, Francesco, Dessole, Salvatora, Dettori, Carlo, Doria, Alessandro, Fancellu, Claudio, F Feo, Giorgio Carlo Ginesu, Giuliana, Giuliani, Marco Giuseppe Iannuccelli, Massimo, Madonia, Roberto, Mancino, Olindo, Massarelli, Gianfranco, Meloni, Fabio, Milia, Andrea, Mulliri, Teresa, Perra, Marco, Petrillo, Antonio, Piras, Franco, Piredda, Francesco, Pisanu, Alberto, Porcu, Davide, Rizzo, Angelino, Sanna, Antonio Mario Scanu, Fabrizio, Scognamillo, Damiano, Soma, Anna Rita Tanca, Alessandro, Tedde, Matteo, Tedde, Luigi Angelo Vaira, Andrea Bartalini Cinughi de Pazzi, Osvaldo Carpineto Samorani, Daniele, Fusario, Luigi, Marano, Gaia, Oldrà, Anna Lisa Pesce, Stefania Angela Piccioni, Luca, Resca, Vincenzo, Ricchiuti, Franco, Roviello, Vinno, Savelli, Alberto, Abrate, Pierpaolo, Bordoni, Lorenzo, Bosio, Guglielmo, Clarizia, Francesco, Fleres, Marco, Franzini, Antonio, Fratto, Pierluigi, Giumelli, Alessandro, Grechi, Alessandro, Longhini, Fabrizio, Lorusso, Elisa, Scarnecchia, Federica, Scolari, Alessandro, Spolini, Vincenzo, Maiuri, Matteo, Papandrea, Arturo, Roncone, Lorenzo, Conti, Andrea, Rizzi, Marco, Rovagnati, Alberto, Brolese, Tommaso, Cai, Francesco Antonio Ciarleglio, Francesca, Dalprà, Gianni, Malossini, Liliana, Mereu, Irene, Tamanini, Saverio, Tateo, Giovanni, Viel, Enrico, Battistella, Paolo Boscolo Rizzo, Cristoforo, Fabbris, Marco, Massani, Giacomo, Spinato, Roberta, Tutino, Ugo, Grossi, Alessandro, Iacomino, Simone, Novello, Maurizio, Romano, Serena, Rossi, Giulio, Santoro, Giacomo, Zanus, Sokol, Trungu, Giada, Aizza, Gabriele, Bellio, Selene, Bogoni, Marina, Bortul, Biagio, Casagranda, Sara, Cortinovis, Nicolò de Manzini, Davide, Drigo, Paola, Germani, Manuela, Mastronardi, Lucia, Paiano, Silvia, Palmisano, Pier Luigi Filosso, Francesco, Guerrera, Matteo, Marro, Mauro, Rinaldi, Enrico, Ruffini, Stefano, Salizzoni, Laura, Bardelli, Mattia, Berselli, Giacomo, Borroni, Eugenio, Cocozza, Matteo, Desio, Bottazzoli, Elisa, Giuseppe, Ietto, Valentina, Iori, Domenico, Iovino, Lorenzo, Livraghi, Valentina, Marchionini, Stefano, Megna, Emma Amal Nahal, Mara, Palumbo, Valeria, Quintodei, Alessandra, Zullo, Lucrezia, D'Alimonte, Giovanni, Pirozzolo, Chiara, Vignotto, Tommaso, Campagnaro, Andrea, Caravati, Simone, Conci, Carlotta De Cristofaro, Gabriele, Gecchele, Tommaso, Giuliani, Jacopo, Graziosi, Alfredo, Guglielmi, Salvatore, Paiella, Corrado, 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Yu, Mitsuru, Ida, Masahiko, Kawaguchi, Kenji, Kawamura, Munehiro, Ogawa, Hiroshi, Okada, Shunji, Endo, Yoshinori, Fujiwara, Masaharu, Higashida, Hisako, Kubota, Toshimasa, Okada, Hironori, Tanaka, Tomio, Ueno, Kazuhiko, Yoshimatsu, Motohiro, Kikukawa, Akira, Kuriyama, Susumu, Matsushime, Hishikawa, Hidehiko, Haruaki, Hino, Yoji, Hisamatsu, Akio, Kamiya, Hidefumi, Kinoshita, Masato, Kita, Toshinori, Kobayashi, Taku, Michiura, Hirokazu, Miki, Tomohiro, Murakawa, Hidetaka, Okada, Tomohito, Saito, Ryoichi, Saito, Motohiko, Sugi, Genichiro, Sumi, Yamaki, So, Tomohisa, Yamamoto, Aya, Yoshida, Tatsuya, Kambara, Sayaka, Kanematsu, Okazaki, Satoshi, Takeshi, Hijikawa, Hiroaki, Kitade, Hidesuke, Yanagida, Tomoyuki, Fujita, Satsuki, Fukushima, Naoki, Tadokoro, Taku, Furukawa, Yusuke, Iizuka, Yuji, Otsuka, Masamitsu, Sanui, Ikumi, Sawada, Hideki, Iwahashi, Morikazu, Miyamoto, Masashi, Takano, Tsutomu, Mieda, Chihiro, Ando, Tetsuro, Isada, Taku, Ishizaki, Qaed Bani Amer, Sachi, Ishida, Yasuma, Kobayashi, Norifumi, Kuratani, Tomoe, Sakurai, Misa, Takada, Yutaka, Iba, Junji, Tsukagoshi, Akira, Yamada, Yuki, Amano, Kentaro, Fumoto, Shusaku, Noro, Ayataka, Fujimoto, Kyoichi, Tomoto, Yukiyasu, Okamura, Teiichi, Sugiura, Katshuhiko, Uesaka, Natsuki, Takemura, Kento, Kuroda, Megumi, Hayashi, Satoi, Kaneko, Izumi, Kawagoe, Tsukasa, Kochiyama, Yamaguchi, Ai, Nobutsugu, Abe, Tadao, Ando, Mieko, Chinzei, Kouichi, Hirano, Yoichi, Kobayashi, Ryota, Matsuki, Hironori, Matsumoto, Akira, Motoyasu, Harumasa, Nakazawa, Hikari, Noguchi, Kaio, Okamura, Motoaki, Ono, Hiroyuki, Seki, Eiji, Sunami, Atsushi, Tajima, Shinji, Tanigaki, Kohji, Uzawa, Hidenobu, Watanabe, Yukari, Furuhata, Satoshi, Toyama, Tokujiro, Uchida, Yutaka, Enomoto, Yuri, Furukawa, Yoko, Hasumi, Jinso, Hirota, Katsuyuki, Iida, Shingo, Ikeda, Tatsuhiko, Ikeda, Natsuko, Kawamata, Yosuke, Kawasaki, Minako, Koizumi, Yoshiharu, Kono, Shigeki, Kuzuhara, Junichi, Maeda, Mai, Moriyama, Shoichi, Nagamoto, Rinako, Nakanishi, Michio, Noda, Atsushi, Seichi, Shintaro, Takahashi, Kobayashi, Takashi, Misuzu, Takeda, Kenta, Tanakura, Katsuyuki, Terajima, Munechika, Tsuji, Chiharu, Ueshima, Naoya, Yamamoto, Mari, Yamamoto, Toshiya, Yokota, Yuki, Yoshioka, Sakoh, Yuri, Shintaro, Iwata, Akira, Kawai, Shuhei, Osaki, Masashi, Ishikawa, Masae, Iwasaki, Tomonori, Morita, Reina, Hirooka, Yoshinori, Nakata, Shigehito, Sawamura, Yusuke, Ishida, Aya, Kawachi, Takayuki, Kobayashi, Fumiaki, Nagashima, Naoki, Suzuki, Kohei, Ando, Noriko, Miyazawa, Yukio, Tanaka, Khatab, Abuissa, Alaa, Aljabali, Amal, Almasri, Mera, Alrabadi, Rana, Alshara, Yasmeen, Alzghoul, Ahmed, Alzughoul, Ali, Freihat, Emran, Jeitan, Qais, Owais, Aseel, Smadi, Mahmoud, Abukhadra, Huthifa, Abunawas, Abdallah Ahmed Mezel Al-Azzam, Ammar, Aladaileh, Ismail, Albadawi, Mohammad, Alhawatmeh, Mohannad Yaser Alkhaza'leh, Roaa Salem Jwaid Alneimat, Saba, Alwahedy, Abdel Rahman Mohannad Ahmad Alwardat, Slsabela, Dhoon, Khaled, El-Qawaqzeh, Tarteel, Hrerat, Enas, Jaradat, Teeba, Mubaydeen, Husam, Sarayrah, Mohammad Abdel Elah, A Tarawneh, Mohammad Yousef Karasneh, Amany, Zurgan, Mohammad, Alassaf, Sayel, H Alzraikat, Justin, Z Amarin, Ahmed, Hijjawi, Haya, H Suradi, Louay, Y Zaghlol, Ahmad, Abdalah, Ghaida, Abdallah, Ammar Abu Tarieh, Tareq, Abu-Libdeh, Faisal, Abualteen, Murad Al Abdallah, Zakaria Al Bdour, Seif Al Dahabrh, Farah, Al-Kasaji, Zaid Al-Sheikh Ali, Mohammad, Al-Thaher, Tareq, Alnajjar, Mohannad, Alqedrh, Mohammad, Alsaleh, Mohammad, Alzaatreh, Ghandi, Amayreh, Yazeed, Ayasra, Ahmad, Eid, Ibrahim, Ghayada, Khaled Moh'd Ahmed Hasanein, Anas, Hassouneh, Raid, Hijazeen, Majedah, Hmeidan, Ahmed, Khaled, Barihan, Khasawneh, Omar, Kifayeh, Yasser, Lafi, Jasem, Seba, Qusai, Semrin, Fda, Sma, Mohammad, Theab, Haitham, Tumeh, Muna Aba Zaid, Alaa, Abazeed, Bader, Abbad, Osaid, Abbadi, Nizar, Abu-Ishkerih, Malk, Al-Osta, Reham, Al-Zyadat, Bashar Omar Falah Alawneh, Majd, Alhattab, Mohd Mujahed Alkurdieh, Ziad, Awwad, Mohammad, Hammouri, Hamzeh, Hussein, Sereen, Khader, Mariam, Khalil, Omar, Mansour, Ahmad, Othman, Haitham, Qandeel, Mohammad, Saleem, Fouad, Souri, Ahmad, Tahboub, Raed, Tayyem, Munir, Abdallah, Ibrahim, Abdel-Hafez, Hamza Abu Obead, Hadeel, Abudari, Ehab, Abuhamour, Samer, Abusadah, Nimer, Al-Azzeh, Sofian, Al-Adwan, Ali, Al-Darabah, Mohammad, Al-Qannas, Basil, Albaba, Qutaiba, Alradawneh, Mohammad, Amaireh, Subhi, Ashour, Reem, Chabaan, Ammar, Eskander, Ahmad, Fawzi, Manal, Haij, Hisham, Hamad, Haya, Hamdan, Ahmed, Hamss, Esraa, Hassouneh, Ayah, Hyasat, Zainab, Ibrahim, Saja, Jamaliah, Mohammed, Khalil, Anas, Massad, Ishak, Mohamed, Mahmoud, Qandeel, Dalia, Qasrawi, Mahmoud, Raggad, Ebaa, Rifai, Nedal, Semreen, Reham, Shehada, Marwan, Shorman, Abdallah, Al-Shibi, Shatha, Alsa'Afin, Morad, Bani-Hani, Bahaa, Nimer, Sandra, Nofal, Layth, Qaraqe, Mohammed, Salameh, Ma'Moun, Saleh, Ghaidaa, Sanjuq, Alaa, Sharabi, Alaa, Sweiti, Jamal, Yaghmour, Sabri, Zaza, Rahaf, Alhindi, Eman, Baninasr, Mahmoud, Al-Masri, Hani, Al-Najjar, Fade, Alawneh, Motasem, Al-Latayfeh, Abdel-Ellah, Al-Shudifat, Abd Almonem Shaikh Ahmad, Abdelkarim, Abdeljalil, Haneen, Abdelrahman, Abdulmalek, Abdulmalek, Lina, Abedalqader, Laith Abu Abed, Omar Al Smadi, Salameh, Alarood, Mohammad, Allouzi, Maha, Almansour, Sari, Almi'Ani, Abdulhakim, Alsaiad, Khaled, Alshaikh, Farah, Androus, Mirna, Awbakh, Belal, Azzam, Hammam Bany Yasin, Maher, Daoud, Lujain, Dawod, Omar, Dawod, Reem, Elmusa, Saba, Fadli, Farah, Hammad, Majd, Hunaiti, Mahmoud, Muhtaseb, Rawan, Owaimer, Zaki, Qulaghassi, Saleh, Romman, Rawand, Saket, Said, Sharawi, Alaa, Tawalbeh, Mohammed, Al-Howthi, Laila, Ababneh, Roba, Ababneh, Faris Jamal Abu Za'nouneh, Sajedah Al Dhoun, Ahmad Al Khassawneh, Ahmed Al Sharie, Ayah Ahmad Al Shraideh, Tawfik, Al-Dabaa, Salsabeel, Al-Jarrah, Hothaifa, Al-Jarrah, Hadeel, Al-Othman, Zuhair, Alaradi, Qutaiba, Alshannaq, Rima, Alsulaiman, Motasem, Alzaqh, Yazan, Alzu'Bi, Ziad, Audat, Mohammad, Bahhour, Saja, Bdour, Roa, Khatatbeh, Fares, Marji, Tagleb, Mazahreh, Dina, Nail, Amjad, Nuseir, Asmaa, Rabab'Ah, Taher, Sawadi, Ghina, Zidan, Dima Abu Muhfouz, Abdullah, Nael, Ahmad, Qasim, Amar, Al-Jarrah, Faisal, Rawagah, Dima, Y Abu Ismail, Wedad, Al-Dolat, Mai, Alzoubi, Malak, Alzoubi, Mu'Taz, Alzu'Bi, Ruba, Asha, Ali, Guboug, Hasan, Hussein, Shireen, Rawashdeh, Mohammad, Sanwar, Adnan, Ababneh, Basil, Abu-Eisheh, Haneen Ahmad Alhami, Saif Aldeen Al Dwairi, Hana' Al Shurman, Zakaria Al Yahya, Hussam, Al-Atiyah, Hasan, Al-Balas, Anas, Aljaiuossi, Shatha, Alqawasmi, Sadeel, Alqudah, Mahmoud, Alshourman, Abdulsalam Bani Hamad, Anas Bany Issa, Raed, Ennab, Sara, Hamdoni, Zaid, Hijazi, Ali, Khafaja, Mohamed, Mraiyan, Riyad, Obeidat, Dina, Olaywah, Mohamad, Qudah, Mhamdd, Talal, Askar, Aidarov, Aisulu, Sarmenova, Almat, Kodasbaev, Orazbek, Sakhov, Shynar, Tanabayeva, Sultan, Amrayev, Berik, Dzhumabekov, Maulen, Malgazhdarov, Kanat, Tezekbaev, Yerlan, Kukubassov, Alima, Satanova, Michael, Mwachiro, Robert, Parker, Ian, Simel, Shamshuddin, Mohammedali, Claude, Mwaria, Ralph, Obure, Sayed Shah Nur Hussein Shah, Chetan, Hirani, Muthoni, Kibunyi, Claire, Mailu, Victor, Mwangi, Roy, Ogenya, Lydia, Osea, Davies, Cheruiyot, Omar, Hirsi, Carolyne, Muiru, Samuel, Wanjara, Chang Woo Kim, Suk Hwan Lee, Ahmad, Almulla, Mustafa, Dashti, Omar, Elghany, Abdullah, Sultan, Husain, Al-Mahmeed, Jasim, Alabbad, Ibtisam, Albader, Shuaib, Aldalal, Mahdi, Baba, Fareed, Abdulsalam, Naser, Alhajri, Athari, Alwael, Brook, Ayele, Hatem, Marie, Ahmed, Zaitoun, Ahmad, M AlAli, Ahmed, Alkhamis, Ous, Alozairi, Sulaiman, Almazeedi, Salman, Alsabah, Mostafa, Abdelkarim, Asmaa Al Rashed, Saud Al Subaie, Tariq, Al-Shaiji, Abdullatif, Al-Terki, Omar, Alhunaidi, Ali, Aljewaied, Khaled, Alzamel, Ahmed, Farag, Hussein, Hayati, Salah, Termos, Sarah, Wood, Maha, Al-Gilani, Mutlaq, Al-Sihan, Haifa, Alotaibi, Dmitrijs, Lobovs, Andrejs, Pcolkins, Armands, Sivins, Ingus Arnolds Apse, Annija Evelīna Berga, Anna, Ivanova, Jevgenijs, Demicevs, Igors, Ivanovs, Aleksejs, Kaminskis, Janis, Opincans, Jānis, Pāvulāns, Agris, Rudzāts, Anna, Udre, Samer, Dbouk, Imadeddine, Farfour, Mohamad, Rakka, Ingrid, Antonios, Joe El Hage, Stephanie, Hage, Lea, Haiby, Hiba, Kahi, Anthony, Lichaa, Rita Milan Moussa, Michel, Salameh, Riad, Sarkis, Obey, Albaini, Tony, Haykal, Bassem, Safadi, Hussein, Abou-Abbass, Reem Al Makari, Zeina Al Zein, Mariam, Baidoun, Rania, Itani, Mustafa, Owiedat, Ali, H Abdel Sater, Michel, Akl, Nour, Badran, Sara El Mustapha, Fathallah, Fatouh, Youssef, Ghoussoub, Nader, Saad, Fatima, Serhan, Bassem, Souleiman, Walid, Alame, Peter, Ghiya, Firas, Ibrahim, Rasha, Abdallah, Mohamad, B Kassab, Samar, Karout, Aya Abdul Al, Hilal, Chaaban, Ghinwa, Fakih, Huzifa, Haj-Ibrahim, Rabih, Awad, Hadi El Assaad, Ahmed, Alfeqeeh, Hosam, Muftah, Taha, Abdulrahman, Naeimah, Ahseen, Hayat Omar Abunaaja, Abdulmuez, Abdulmalik, Abd El Jawad Al Gasi, Sara, Alsaeiti, Fatimah, Elkhafeefi, Nasre, Elrefai, Rofida, Ahmad, Tarek, Alhouni, Safya, Alkarky, Hana, Alkeelani, Rauoof, Alkuwafi, Rema, Benhariz, Nagat, Bettamer, Musa, Busarira, Hassan, Bushaala, Tariq, Diryaq, Osama, Elbargathe, Tarik, Eldarat, Mustafa, Elfadli, Jamael, Elfitori, Asma, Elmahgoub, Reem, Elsahti, Yosef, Hassan, Fathi, Hussain, Taha, Mirsal, Marwa, Mohammed, Ebtesam, Saad, Haifaa Il Hadad, Mostafa, Mosabha, Awatif, Alhaje, Aaya, Haron, Suhir, Alsuwiyah, Mona, Hamed, Samer, Khel, Sana, Omar, Rowaida, Yousef, Abubaker, Abdelmalik, Yahya, Abosnaina, Ma'Aly, Abuhlaiga, Ahmed Omar Abushahma, Alhosen Saleh, M Aldelensi Alzubi, Asma, Aljanfi, Mohamed Alnaser Alnehum, Ahmad, Alzedam, Malik, Delhen, Galia, Maderi, Faisel, Matoug, Burooj, Mohammed, Mohamed, Sawalem, Alhadi, Jahan, Abdussalam, Jahan, Basma, Alazabi, Bahauldin, Alezabi, Mona Masaud Amro Alazabi, Amin Salem Ahmed Egdeer, Fatma, Sefi, Elhusain, Kamoka, Abdulrauf, Salamah, Alya, Abdalhadi, Saedah, Abdeewi, Marwah, Alhadad, Ahmed, Awrayit, Mabroka, Eshnaf, Mohammad, Yahmad, Abdulhafid, Abudher, Faisl, Elamin, Farah, Abojeila, Mohamed, Aliwa, Sana, Shagour, Ahmed, Altobal, Omar, Aljuroushi, Hadeel, Almadani, Hajer, Attia, Amal Sharif Eljali, Ayyah, Emran, Mabruka, Omar, Radhwan, Salim, Ashraf, Samer, Nayrouz Abdullah Abulshuwashi, Emadeddin T, M Ben Khalifa, Hdaya Abdalla, S Benabdalla, Hatem, Elnageh, Ali, Hammad, Duha Milad Abdullah, Marwa, Abusalem, Esraa Ben Esmael, Fatma, Benmasoud, Laila, Debri, Randa, Elmokhtar, Manal Ezeddin Kamel Sheta, Bushra Sayeh Mohamed Elhabashi, Balkees, Younis, Amna, Elmabrouk, Ahmed, Momen, Abdalrahem Essied Alzubi Alzoubi, Kusay, Ayad, Ameerah Mahdi Abraheem Hasan, Fawzie, Musrati, Malek, Abusannoga, Ans, Malek, Hatem, Ajaj, Eman, Abdulwahed, Mohammed, Aboubeirah, Aya, Alqurpaa, Entisar, Alshareea, Sarra, Aribi, Tomather, Aribi, Marwa, Biala, Reem, Ghamgh, Mohannned, Issa, Amani Hamid Lamari, Mohammed, Mufth, Omar Sudig Abboud, Muad Fathi Khalleefah Abu Hallalah, Saleh, Abumahara, Sarah, Aburima, Mohammed, Adrees, Abdurahman, Ahtash, Asmaa Al Shukri, Hind, Alameen, Rehab, Alarabi, Alarabi, Alarabi, Aisha, Alelwany, Abdulkarim, Alesmail, Noran, Algadi, Khuloud, Almaqrahi, Hadel, Alosta, Mohamed, Alsharedi, Rasha, Bashir, Mahmoud Ben Ghrema, Sari Bin Nour, Sofian, Elbarouni, Ahmed, Elhadi, Abdulmohimen, A Elkhadar, Esraa, Elkouba, Nabiha, Elmsherghi, Abdulmohimen, Gammoudi, Abdulraouf, Ghariba, Fatima Zayed Gjam, Arwa, Haidar, Hala, Helal, Mohammed, Huwaysh, Ahmed Khalid Alhadheeri, Eslam, Kriem, Halim, Osman, Mohamed Ali Ossman, Marwah, Shilfeet, Laila, Turshani, Nouran, Albishty, Abdulmueti, Alhadi, Salem, Ali, Manal Ahmed Altoumi, Sumayyah Ghayth Bahroun, Eman, Younes, Hana, Atarabulsi, Mohamed, Elshaibi, Abdullmujeeb, Othman, Hayat Ben Hasan, Najat Shaban Ben Hasan, Rabab Shaban Ben Hasan, Vidmantas, Barauskas, Edvinas, Dainius, Zilvinas, Dambrauskas, Albertas, Dauksa, Inga, Dekeryte, Rita, Gudaityte, Kristijonas, Jasaitis, Akvilė, Koženiauskaitė, Virgilijus, Krasauskas, Ausra, Lukosiute-Urboniene, Almantas, Maleckas, Romualdas, Riauka, Vygintas, Šlenfuktas, Saulius, Svagzdys, Kestutis, Urbonas, Tomas, Vanagas, Linas, Venclauskas, Donatas, Venskutonis, Justas, Zilinskas, Marijus, Ambrazevicius, Vilius, Syminas, Oleg, Aliosin, Agne, Cizauskaite, Vitalijus, Eismontas, Jonas, Jurgaitis, Vytenis, Mikutaitis, Donatas, Petrauskas, Narimantas Evaldas Samalavicius, Dainius, Simcikas, Algirdas, Slepavicius, Albinas, Tamosiunas, Vaidotas, Turskis, Bernardas, Vasiliauskas, Laura, Aniukstyte, Audrius, Dulskas, Gediminas, Januška, Justas, Kuliavas, Margarita, Montrimaite, Albertas, Cekauskas, Azuolas Algimantas Kaminskas, Sarunas, Kozenevskis, Tomas, Poskus, Kestutis, Strupas, Arunas, Zelvys, Georges, Decker, Audrey, Noël, Maheriandrianina Fanambinana Voahary Rajaonarivony, Randimbinirina Zakarimanana Lucas, Herimampionona, E Andriantsoa, Mamisoa, B Rasamoelina, Rakotonarivo Aina Andrianina Vatosoa, Liantsoa, Andriamanana, Casimir Fp Rahantasoa Finaritra, Aristide Romain Raherison, Tsirimalala, Rajaobelison, Haritiana, Rakotoarisoa, Maharo, Ramifehiarivo, Solonirina Harinarindra Ranaivoson, Hajamihamina Marina Parfaite Randriantsoa, Narindra Njarasoa Mihaja Razafimanjato, Jeannie Ba Razafindrahita, Solofoarimanana, Solofoarimanana, Guillaume Odilon Tsiambanizafy, Rakotonaivo Mamisoa Judicaël, Corinne Eulalie Solo, Tojomamy Herinjaka Ralaizafindraibe, Fanonjomahasoa Safiry Andofenohasina, Hanta, Rasataharifetra, Vanona Barijaona Razafindraibe, Angelin Sablon Herinirina, Andre, Das, Thanga, Ganapathy, Yohesuwary, Gunarasa, Kalyani, Mariapan, Maathichsudhaar, Muniandy, Mohd Firdauss Osman, Mohd Azem Fathi Mohammad Azmi, Jien Yen Soh, Wan Zainira Wan Zain, Andee Dzulkarnaen Zakaria, Zaidi, Zakaria, Kheng Hooi Chan, Firdaus, Hayati, Syamim, Johan, Jin Jiun Mah, Ratha Krishnan Sriram, Sentilnathan, Subramaniam, Nur Shahirah Binti Muhammad Shahimi, Khairul Hazim Hamdan, Mohd Razali Ibrahim, Guhan, Muthkumaran, Ju Ann Tan, Mohana Raj Thanapal, Dayang Anita Abdul Aziz, Mae-Lynn, Bastion, Mohd Hairul Nizam Harun, Farrah-Hani, Imran, Norshamsiah Md Din, Ayesha Mohd Zain, Mushawiahti, Mustapha, Syed, Nabil, Ainal Adlin Naffi, Abd Jabar Nazimi, Roszalina, Ramli, Bee Hong Soon, Pavin, Bal, Joo Qing Cheng, Mohd Rusdi Draman, Ahmad Nazran Fadzli, Sakina, Ghauth, Weipin, Hung, Suniza, Jamaris, Peng Soon Koh, Sivakumar, Krishnasamy, Yeong Sing Lee, Yew Toong Liew, Jeffery Zk Lim, Raymond Chung Siang Lim, Hiong Chin Lim, Siti Farhan Moh Pauzi, Shireen Anne Nah, Ashvin Krishna Nair, Doris Sin Wen Ng, Yuki Julius Ng We Yong, Kamarajan, Ramayah, Rahmah, Saaid, Anand, Sanmugam, Mee Hoong See, Kanesh Kumaran Seevalingam, Neha, Sethi, Srihari, Singaravel, Syeda Nureena Syed Jafer Hussain Zaidi, Chu Yik Tang, Li Ying Teoh, Kian Boon Wong, Ruben, Xavier, Ahmad Shuib Yahaya, Norhafiza, Ab, Rahman, Islah Munjih Ab, Rashid, Mohd Fahmi Abd Aziz, Mohd Norhisham Azmi Abdul Rahman, Nasser Muhammad Amjad, Shahidah Che Alhadi, Faisal, Elagili, Mohd Nazli Kamarulzaman, Azmi Md Nor, Ahmad Faidzal Othman, Mohd Yusof Sainal, Mat Salleh Sarif, Richelle, Chua, Aini Fahriza Ibrahim, Abdul Rauf Bin Ahmad, Yii Ling Lau, Ka Yee Liew, Kandasami, Palayan, Hui Yu Wong, Jasiah, Zakaria, Nor Azimah Abd Aziz, Norazila Abdul Rahim, Noorneza Abdul Rahman, Bahiyah, Abdullah, Mohamed Faizal Bin Sikkandar, Fatin, Firman, Nik Mohd Hazleigh, Intan Kartika Kamarudin, Shahril, Khalid, Mohd Fairudz Mohd Miswan, Akmal Zulayla Mohd Zahid, Norasyikin, Mustafa, Kadhim, Obaid, Mohd Yusoff Bin Yahaya Yahaya, Ahmad Ramzi Yusoff, Predrag, Andrejevic, Jeremy Borg Myatt, Svetlana, Brincat, Clifford, Caruana, Kurt Lee Chircop, Francesca, Chircop, Daniel Mario Chircop, Charles, Cini, Viktoria, Czok, Melanie, Farrugia, Gabriella, Grima, Fatimah Mj Hassan Almukhariq, Esther, Muscat, Darryl, Pisani, Mohsin Hassan Khan Roshan, Nicholas, Schembri, Mohamed, Shoukry, Neville, Spiteri, Gilbert, Tanti, Ziad Aboharp Hasan, Alberto Bazan Soto, Cedillo Rodrìguez Jonathan Rubén, Gustavo Esteban Lugo Zamudio, David Gerardo Miranda Gómez, José Alfredo Pérez Meave, Victor Pinto Angulo, Antonio Rojas Aguilar, Erik Efrain Sosa Duran, Alejandro Vàzquez Perez, Diego, Arroyo, Jose, Corona-Cruz, Hector, Martinez-Said, Heriberto, Medina-Franco, Manuel, Mendez, Teresa Moreno, Y Suárez Ma, Dolores Orta Díaz, Martha Patricia Pérez de León Vázquez, Angélica Azucena Soto Carvajal, Fidel Soto Hernández, Juan Ignacio Stenner, Adriana Valenzuela López, Mauricio Rene Hernandez, Rey Jesus Romero, Aldo Bernal Hernandez, Ana Olivia Cortes-Flores, Carlos Jose Zuloaga Fernandez Del Valle, Carlos Andres Colunga Tinajero, Jaime, Orozco-Perez, Laura Gabriela Peña Balboa, Guillermo, Yanowsky-Reyes, Víctor Hugo Alcalá Torres, Francisco José Barbosa Camacho, Irma Valeria Brancaccio Perez, Miguel Angel Calderon-Llamas, Aldo Camacho Gomez, Edgar-Joaquin, Cortes-Torres, Esteban, Cueva-Martinez, Oscar Durán Anguiano, Isaac Esparza Estrada, Roberto, Fierro-Rizo, Paola Flores Becerril, Clotilde Fuentes Orozco, Miguel García Castillo, Alejandro Gonzalez Ojeda, Ana Sofia Gonzalez Rubio, Erick Alonso González-García de Rojas, Luis Humberto Govea-Camacho, Jose Aldo Guzman Barba, Juan Carlos Ibarrola Peña, César Íñiguez Martínez, Francisco Javier Llamas-Macias, Ruben Eduardo Morán Galaviz, Cesar, Nuño-Escobar, Oscar-Everardo, Olvera-Flores, Jaqueline, Osuna-Rubio, Jose Victor Pérez-Navarro, Rodrigo, Prieto-Aldape, Luis Ricardo Ramirez Gonzalez, Marco Vinicio Ramírez Sánchez, Emilio, Reyes, Olaya Moramay Romero-Limón, Carlos Beas Ruiz-Velasco, José Antonio Sánchez Martínez, Guillermo, Sanchez-Villaseñor, Ismael, Sedano-Portillo, Gabriela Ambriz Gonzalez, Pedro Berrones Moreno Berrones Moreno, Ana Beatriz Calderon Alvarado, José de Jesús Cárdenas Barón, Araceli, Hernández, Francisco Javier León Frutos, Edgard Efren Lozada Hernandez, Eduardo Morales Valencia, Karla Daniela Pérez, Francina, Bolanos-Morales, Julio, Herrera-Zamora, Patricio, Santillan-Doherty, David Omar Arriaga Zavala, Salvador Francisco Campos Campos, Lizette, Carmona, Antonio Galindo Nava, Blanca, Santos, Juan Roberto Torres Cisneros, Katya, Bozada-Gutiérrez, Jose Jesus Herrera, Mucio Moreno Portillo, Francisco Pérez López, Mario, Trejo-Avila, Carlos Valenzuela Salazar, Maria Regina Alvarez, Fernando, Cordera, Marco Antonio de la Rosa Abaroa, Antonio, Gómez-Pedraza, Roberto, Hernandez, Antonio, Maffuz-Aziz, José Antonio Posada, Carlos Robles Vidal, Jose-Luis, Beristain-Hernandez, Blas Eduardo Quintero Sada, Nicolás Uxmal Soruco Heredia, Ernesto, Anaya, Ricardo, Arceo-Olaiz, Bernardo Gutiérrez Sougarret, Sonia Lopez Flores, Francisco, J Ochoa Carrillo, Raymundo Alfredo Pérez Uribe, Ludwigvan Adriano Bustamante Silva, Arianne, I Lupián-Angulo, Christian Enrique Soulé Martínez, Francisco César Becerra García, Ernesto Javier Guerrero Casillas, Edgar, Uribe, Nereida Esparza Arias, J Mindy Hernández-Nava, David, Isla-Ortiz, Javier, Melchor-Ruan, Esmeralda Romero Bañuelos, Rosa, Salcedo-Hernández, Diana, Vilar-Compte, Paola Zinser Peniche, Jorge, L Aguilar 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Rodriguez, Manuel Saez Barba, Vanessa Sanchez Torrents, Berta, Serrano, María Sol Siliato Robles, Christian Siso Raber, Iñigo, Soler, Maria Carmen Suescun López, Jordi Teixidor Serra, Cristina, Tello-Díaz, Marta Utrilla Pérez, Ramon, Vilallonga, Irene Vives Roselló, Vázquez García César, Mireia, Gili-Bueno, Carlos Gomez Roig, Gloria, González, Aamer, Malik, Maria Del Mar Martí-Ejarque, Mario Rafael Medina Hernández, Carolina, Pozo, Laura, Ruiz-Villa, Nuria, Sierra, Antonio, Valle, Susana, Velasquez, Mirentxu, Arrieta, Daniel, Escobar, Arkaitz, Lara, Alicia, Rojo, Idoia, Villamor, Javier Aitor Zabala Lopez-Maturana, Begoña, Estraviz, Laura Fernández Gómez Cruzado, Melania González de Miguel, David, Lecumberri, Victor Luis Gomez Corujo, Silvia, Bleda, Joaquin De Haro, Carlos, Martínez-Pinedo, David, Padilla-Valverde, Rafael Picón Rodríguez, Francisco Javier Redondo Calvo, Daniel, Sanchez-Pelaez, Alfonso Espinosa Ruiz, Eduardo, Rodriguez, Ignacio Alcalá Rueda, Gonzalo Díaz Tapia, Alvaro Sanchez Barrueco, Maria Dieguez López, Isabel López García, Pilar López-Toribio López, Marina, Bosch, Carolina Curtis Martínez, Alba Fernández Candela, Sandra Lario Pérez, Cristina Lillo García, Clara López de Lerma Martínez de Carneros, Francisco López Rodríguez-Arias, Inmaculada, Oller, Saray, Quinto, Luis, Sánchez-Guillén, Antonio Sanchís López, Álvaro, Soler-Silva, Daniel Triguero Cánovas, Victoria Garcia Peces, Estefania, Hernández-García, Guillermo, Plaza, Rafael Abad Alonso, Francisca Aranda Lozano, Marta Calvo Fernández, Eneko Del Pozo Andres, Josune Etxabe Gurrutxaga, Francisco Javier Fernández Pablos, Francisco Javier Ibáñez-Aguirre, Begoña Ochoa Villalabeitia, Natalia, Ortega, Amaia Sanz Larrainzar, Irune Vicente Rodriguez, Javier Jimenez Miramón, Jose, M Jover, Tamara, Llamero, Jose Luis Ramos Rodriguez, Ainhoa Valle Rubio, Maria Teresa Albiol, Codina, Antonio, Clara, Codony, Ramon Farres Coll, Cristina Farrés Pla, Nuria Gomez Romeu, Santiago, Lopez-Ben, Marcel, Pujadas, Berta, Tió, Francesc, Tuca, Elizabeth, Bárcena, Esther Brea Gómez, Antonio Francisco Guisado Calderón, Elisabeth Fernandez Castro, Montserrat Mairal Fraile, Oscar, Pastor, Cristina García Amador, Raquel Latorre Fragua, Marta, Allue, Pablo, Colsa, Teresa Gimenez Maurel, Raúl Latorre Tomey, Luis Francisco Martín Anoro, Luca, Ponchietti, Alejandra Utrilla Fornals, Carlo, Brugiotti, Carlos Jiménez Viñas, Fátima, Sena-Ruiz, Julio, Plata-Bello, Octavio, Arencibia, Daniel Gonzalez Garcia-Cano, Maria, Laseca, Antonio, Navarro-Sánchez, David Ortiz López, Maria, Pelloni, Sara, Berrocal, Elima Pilar Cagigal Ortega, Jorge, Calvera, Iria, Cervera, Patricia Díaz Peña, Silvia Verónica Domínguez Ovejas, Francisco, Donis, Patricia Fernández Bernabé, Gema, Fraga, Raúl Garcés García, Antonio, García, Elena Garcia De Castro, Antonio García Domínguez, Miguel Angel García García, Judit, Gonzalez, Carles Heredia Llinàs, Inés, Hernández, Norberto Herrera Merino, Carmen Lucero León Gámez, Ana Luis Siles, Maria Marqueta De Salas, Paula Martinez Pascual, Almudena Ortiz Simón, Laura, Osuna, Marta Perez Gonzalez, Antonio Ramos Bonilla, Lorena Rodríguez Gómez, Jose Luis Uquillas, Miguel Angel Alonso Prieto, Marta, Ballesteros-Pomar, Sara Busto Suarez, Mario, Castaño, Laura Castillo Pardo, Mario De Arriba Alonso, Fernando Diez Burón, Anna Farrés Rabanal, Diego Fernández-Samos Fernández, David, Garcia, Cristina García Pérez, Miguel García Sanz, Cesar, Gracia, Javier, Gualis, Gonzalo Gutiérrez Carrillo, Mateo, Hevia, Eva Higuera Miguélez, Gregorio, Laguna, Cristina Lombardia Gonzalez de Lera, Pablo López Martínez, Pasquale, Maiorano, Sergio Marcos Contreras, Elio Martín Gutiérrez, Marta, Molina, Irene, Perez, Teresa Renedo Villar, Luis Angel Suarez Gonzalez, Eva Alvarez Torres, Luz Bueno Rey, Zulay Adriana Calderon Barajas, Ana, M Castaño-Leon, Aida, Fernández, Pablo Garcia Pimentel, Luis, Jimenez-Roldan, Meta, Levstek, Rosalia Navarro Casado, Claudia Olea Vielba, Ana Maria Parada Rodriguez, Igor, Paredes, Patricia, Pascual-Cambero, Miguel Saiz Sánchez-Buitrago, Claudia Sarrais Polo, Victor Zarza Fernandez de Alegria, Mireya, Bonet, Alberto Encinas Vicente, Jose Miguel Villacampa Auba, Maria Dolores Burgueño, Patricia Serrano Méndez, Thomas, W Jorgensen, Mercedes, Martin, Mónica, Huecas, Felipe, Velasco, Verónica Alen Villamayor, Santiago Alonso Bartolomé, Miguel Alonso Juarranz, Luis Felipe Ávila-Ramírez, Javier Buendia Pérez, Barbara, Burgos-Blasco, Enrique Camarero Rodríguez, Paula Campelos Fernández, Claudia, Celotti, Marco Ciappara Paniagua, Ignacio, Cristobal, Alejandro Encinas Bascones, Elena, Fernandez-Martin, Inés Gil Prados, Jesus Gimeno Hernandez, Maria Alejandra Giraldo, Manuel Gomez Cervantes, Noemi Guemes Villahoz, Araceli Hernández Ramos, Maria Cruz Iglesias Moreno, Bibiana Lasses Martínez, Francisco Leyva Rodríguez, Leyre López Antoñanzas, Javier Martín Monterrubio, Jose Maria Martinez-de-la-Casa, Lourdes Montero Cruces, Jesús Moreno Sierra, Jose Maria Muguerza, Alexia Victoria Paluso Montero, Natalia Pérez Romero, Adriana, Poch, Jaime Rodríguez de Alarcón, Adriana Ruano Campos, Rebeca Ruiz Roman, Patricia Saez Carlin, Cristina Sánchez Del Pueblo, Lidia Sotillo Valenzuela, Armando, Galvan, Miguel Ángel García Ureña, Enrique, González, Manuel Medina Pedrique, Alvaro Robin Valle de Lersundi, Carlos San Miguel, Juan, Aragón-Chamizo, Monica, Ballon, Jose, M Barrio, Beatriz Castro Catalan, Laura Cebolla Rojas, Renan Carlo Colombari, Miguel Cuende Diez, Paula Dujovne Lindenbaum, Oscar, Gil-de-Sagredo, Silvia Kayser Mata, Pablo Lozano Lominchar, Yeniffer Tatiana Moreno Salazar, Angela, Moreno-Gutierrez, Melanie, Morote, Uxue, Murgoitio, Nikolas Palma Caucig, Lucía Polanco Pujol, Alejandro, Prosperi, María Belén Ramírez Senent, Diego Fernando Ruiz Chiriboga, Mercedes, Sanz, Maria, Tudela, María Adrien Lara, Raquel, Alarza, Isabel Alonso Sebastian, Mariano Artés Caselles, Laura, Calles-Sastre, Marcos Casas Sánchez, Aritz Equisoain Azcona, Lucía, Fuentes, Lucia Gil Cidoncha, Sofía Herrero Gámiz, Eva Iglesias Garcia, Jose Luis Lucena De La Poza, Mª Eugenia Marín Martínez, Pilar Martín Rodrigo, Joaquin Manuel Muñoz Rodríguez, Verónica, Polaino, Alberto Pueyo Rabanal, Xabier Remirez Arriaga, Xiana, Rial, Miguel Sánchez Suárez, Maria Eugenia Torguet Muñoz, Borja Bazán Inostroza, Lara Blanco Terés, Alba Correa Bonito, Angela de la Hoz, Gustavo, Eisenberg, Javier Garcia Septiem, Jose Maria Lopesino, Rocio, Maqueda, Elena, Martin-Perez, Jose Luis Muñoz de Nova, Julia Revuelta Ramírez, Ana Rodríguez Sánchez, Álvaro Valdés de Anca, Laura Colao García, David Díaz Pérez, Enrique Esteban Agustí, Pablo Galindo Jara, Ana Belén Gallardo, Cesar, Garcia, Maria Gutierrez Samaniego, Miguel Angel Hernandez Bartolome, Lorenzo, Rabadan, Barriga Sánchez Raquel, Beatriz, Diéguez, Miguel, Hernández-García, Carolina Alfonso Carrillo, María Belén Alonso Bartolomé, Estibaliz, Alvarez, Mario, Alvarez-Gallego, Paula, Aragón-Ramos, Pablo Cesar Arteaga Asensio, Luis Asensio Gomez, Diego, Carrion, Nuria, Chavarrias, Alvaro de Arriba, Jose Antonio Gazo Martínez Gazo, Alberto Gegúndez Simón, Juan Gómez Rivas, Natalia Gonzalez Alcolea, Carolina, Gonzalez-Gomez, Sara Gortázar de Las Casas, María Alexandra Heras Garceau, Alicia Hernández Gutierrez, Luis, Lassaletta, Fuad Samir Lopez Fernández, Marta, Mancheño, Blanca, Mateos-Serrano, Isabel Pascual Miguelañez, Maria Fernanda Pedrero Escalas, Julio, Peñarrocha, Yolanda, Perez, Maria Isabel Prieto-Nieto, Teresa Rivera Schmitz, Laura, Rodrigáñez, Ines, Rubio-Perez, Isabel Sanchez Cuadrado, José Ignacio Sánchez Méndez, Jorge Fernando Tone, Carlos Toribio Vazquez, Aitor, Urbieta, Diego Córdova García, Francisca Garcia-Moreno Nisa, Laura, Jiménez, Inmaculada, Lasa, Belen, Matías-García, Fernando, Mendoza-Moreno, Marina Pérez González, Ana Sánchez Gollarte, Alejandro Sanchez Pellejero, Cristina Vera Mansilla, Rafael, Barberá, Lourdes, Montes-Jovellar, Sara Fra Fernández, Nicolas Moreno Mata, Barbara, Algar-Yañez, Gutiérrez, Bérénice, Olga De La Varga-Martínez, Paula, Fernandez-Valdes-Bango, Sandra Maria Gadin-Lopez, José María Martínez-Gómiz, Elena, Nieto-Moreno, Ana, Nieto-Moreno, Montserrat Ocampos Hernandez, Maria Laura Pelegrina-Lopez, Alicia Ruiz Escobar, Elena, Sáez-Ruiz, Beatriz, Vazquez-Rivero, Antonio, Lara, Belén Vielva Del Campo, Fernando Alcaide Matas, Simbad, Costas-Ochoa, José María García Pérez, Elizabeth, Marmol, Paula Troncoso Pereira, Maria Del Pilar Concejo Cutoli, Juan Ramón Gómez López, Juan Carlos Martín Del Olmo, Paloma Guillamot Ruano, Javier Páramo Zunzunegui, Antonio Albarracín Marín Blázquez, Alfonso, Aliaga-Sanchez, Marta María Arroyo Domingo, Miriam Artés Artés, Ana Isabel Avellaneda Camarena, Aida, Blaya, Emny Rochell Bobadilla Romero, Pedro Vicente Fernández-Fernández, Lorena Galindo Iñiguez, Víctor Javier García Porcel, Clara Giménez Francés, Antonio Javier Gomez Poveda, Rebeca Gonzalez Celdran, Francisco Miguel González Valverde, Laura Guillamon Vivancos, Elena, Gurrea-Almela, Alejandra Jara Maquilón, Jose David Jimenez Parra, Abd Al Aziz Lanagrán Torres, Raquel Lax Perez, Carmen Maria Lopez Lopez, Francisco, Machado, Alfonso Marco Garrido, Florencio Manuel Marin Martinez, Victoria Martínez Muñoz, Esther, Medina, Olimpia, Molina, José Manuel Muñoz Camarena, Julian, Onate, Pedro Antonio Parra Baños, Emilio Peña Ros, Enrique, Rubio, Marina Sánchez Robles, Carlos Sanchez Rodriguez, María Valero Soriano, María Teresa Yepes García, Felipe, Alconchel, Andrés Balaguer Román, Valentín, Cayuela, Ana, Conesa, Ana Delegido García, Pedro José Gil Vázquez, Paula Gómez Valles, Francisco, Gómez-Bosch, Vicente, J León-Muñoz, Francisco, Martínez, Alvaro, Navarro-Barrios, Tatiana, Nicolás-López, Pablo Ramirez Romero, Emiliano, Cano-Trigueros, María Concepcion Alonso González, Leticia Gómez Viana, Ana Pastor Zapata, Elisa Contreras Saiz, Tamara Díaz Vico, Maria, Fernandez-Hevia, Luis Joaquín García Flórez, Raquel, Rodríguez-Uria, Sandra, Sanz, Lorena, Solar-Garcia, Aida Suárez Sánchez, Jaume, Gelonch, Dietmar, Reinaldo, Miriam Abdulkarim Polo, Asier, Aguirre, Marta Alomar Bofill, Santiago, Baena, Ashish, Bartakke, Carmen Cabeza Oliver, Marta Castro Suárez, José Andrés Cifuentes Rodenas, Olga Claramonte Bellmunt, Ant Onia Crespí Mir, Anabel De La Llave Serralvo, Mar, Escales, Isabel Rosa Fernández Burgos, Pablo Gandia Gonzalez, Juan Lliteras Jorge, María Cristina Martínez Canto, Javier, Mata, Andreu, Morell, Naila, Pagès, Sara Pérez Palao, Cristian Plaza Valiente, Catalina Ramon Barcelo, Carmen Reyero Fernández, Albert Reyes Claret, Leticia Rodriguez Vaquero, Maialen Saez de Vicuña Salinas, Pilar Santos Cidon, Concepcion, Segura, Aina, Serra, Maria Serra Guivernau, Maria Soler Pedrola, Marta, Tasende, Marta Torrent Lluch, María Inmaculada Valldeperas Hernández, Rosa-Maria, Yañez-Lpez, Andrea, Craus-Miguel, Laura Fernández Vega, Natalia Pujol Cano, Juan José Segura-Sampedro, Laura Colet Oliver, Ivan Dot Pascuet, Mireia, Duart, Isabel Tello Galindo, Eduardo Arrea Salto, Jaume Cámara Cabrera, Marta Camats Terré, Juan Gabriel Castro Ríos, Marta de la Rosa-Estadella, Anna, Fernandez-Colorado, Anna, Gasulla-Rodriguez, Antonio, Gimenez-Gaibar, Elena, Gonzalez, Yeray Maldonado Sotoca, Matilde, Molina-Corbacho, Montserrat Monfort Mira, Josep, Rodoreda, Marta Santos Espí, Arnau Verdaguer Figuerola, Elisa Angela Diego-Alonso, Jaime, López-Sánchez, Luis, Muñoz-Bellvis, Jacobo, Trebol, Borja, Aguinagalde, Cristina Gabriela Alzate Arsuaga, Ainhoa Andres Imaz, Iñigo, Arana, Coro Aranzabal Urrutia, Mikel, Armendariz, Lorena, Arrabal, Iñigo, Augusto, Adolfo, Beguiristain, Laura, Busto, Ainhoa, Cuevas, Itziar De Ariño, Alejandro, Elúa, Leticia, Fernandez, Arantza, Fernandez-Monge, Lander, Gallego, Alba, Garcia, Amaia Garcia Dominguez, Gregorio, Garmendia, Carmen, Grañén, Pelayo Hevia Rodríguez, Maria, Iraola, José Miguel Izquierdo, Aintzane, Lizarazu, Jon Ander Lizarbe, Claudia Cristina Lopes Moreira, Iker, López, Ruth Marquina González, Tania, Pastor, Adrián Recio Ayesa, Ana Paula Riverola Aso, Carballo, Rodríguez, Araceli, Rodrìguez, Maria Inmaculada Ruiz Montesinos, Patricia, Torres, Jose, Undabeitia, Ainara, Villafruela, Maria Barrionuevo Ramos, David, Blanco, Marta, Calderón, Eva Corral Rubio, Angel, Cuadrado-García, Daniela, Cubek, Ignacio, Fariña, Maria Fernandez LLorente, Natalia Flores Amador, Mariana García Virosta, Luis Garcia-Sancho Tellez, Adelina Gonzalez Martinez, María, Hernandez, Rubén Herreros Ruiz-Valdepeñas, Carmen Jimenez Sanchez, Eduardo Llamazares Cobo, María, López, Javier, López-Martin, Teresa Martínez Marivela, Luis Miguel Martinez Parra, Paloma Maté Mate, Paloma, Medrano, Manuel Moriche Carretero, Sara Núñez O'Sullivan, Manuel Ortega Oria de Rueda, María Begoña Pastor Nieto, Clara, Pérez, Antonio, L Picardo, Remedios Revilla Amores, Carmen Rodríguez Haro, Jose Alberto Rojo López, Fatima Sanchez Cabezudo Noguera, Cristina, Sanz, Daniel Serralta de Colsa, Cristina Valor Garcia, Marta Velasco Martinez, Iván Andújar Lara, Aranzazu, Calero-Lillo, Enrique, López-Ruiz, Alberto Díaz García, Alejandro Menéndez Moreno, Luis Eduardo Pérez-Sánchez, Ángela, Pascual, Miguel Adeba García, Marta Alonso Fernández, Javier Alvarez Gama, Roberto, Ballestero, Loreto Berjon De La Vega, Paola Calleja Hermosa, Mariana, Carrillo-Rivas, Alfonso, Casado, Andrea, Cerveró, Bonifacio Cimadevilla Calvo, Patricia Corriols Noval, Paloma de la Dehesa Cueto-Felgueroso, Miren Jasone Diez Zapirain, José Estevez Tesouro, José Antonio Fernández-Dívar Sánchez, Lucia Garcia Alcalde, Juan García Cardo, Tito García Moreno, Daniel, García-López, Belen Garcia-Montesinos Perea, Miren Gonzalez Benito, Rocio, Gonzalez-Aguado, Miguel Ángel Gordo Vega Gordo-Vega, Jose, Gutierrez-Banos, Jaime Jimeno Fraile, Yolanda Jubete Castañeda, Patricia Lopez Gomez, Antonia Jesús López López, David Mato Mañas, Carmelo, Morales-Angulo, Dieter, Morales-Garcia, Sara, Naranjo, Jose Luis Rabago, Jose Manuel Rabanal, Rocio Revuelta Zorrilla, Adriana Reyes Echeverría, Juan Carlos Rodríguez-Sanjuán, Juan Ramón Sanz, Isabel Simal Badiola, Guillermo, Tejón, Enrique Toledo Martínez, Raquel Varea Malo, Begoña, Fadrique, Tania, Funes, Elena González Revilla, Isaias, Alarcón, Daniel Aparicio Sánchez, Pablo Beltran Miranda, Carmen, Cepeda-Franco, María Josefa Cuevas López, Sandra, Dios-Barbeito, Virginia María Durán Muñoz-Cruzado, Carlos Gonzalez De Pedro, Rosa, M Jimenez-Rodriguez, Francisco, Manresa-Manresa, Gabriel, Marin, Cristobalina, Martin-Garcia, Sara, Martínez-Núñez, Juan Manuel Martos Martinez, Lucas, Mengíbar, Felipe Pareja Ciuro, Eduardo Perea Del Pozo, Veronica Pino Diaz, José, Pintor-Tortolero, Claudia, Quintero-Pérez, Maria Luisa Reyes Diaz, Mercedes Rubio Manzanares Dorado, Alejandro Sanchez Arteaga, Ana, Senent-Boza, Luis, Tallon-Aguilar, Jose, Tinoco-Gonzalez, Luis-Cristobal, Capitan-Morales, Juan Cintas Catena, Javier, Valdes-Hernandez, María Del Campo Lavilla, Alina López De Fernández, Miriam Abellan Lucas, Alba Diaz Padillo, Maria Gómez Romero, Linda Grace Puerto Tamayo, El Mostafa El Yaqine Er Raoudi, Melody García Domínguez, María Ángeles Gascón Domínguez, Marta González Pérez, Alba Hernáez Arzoz, Joana San Anton, Cristian, Ruminot, Hander Guillermo Acosta Diaz, Daniella, Laguado, Kristina, Aghababyan, Alba, Jr, Enrique, Artigues, Rocío, Belda, Miguel, Beltran, Jose Ignacio Blanes, Elena Campos Carot, Miriam, Cantos, Laura, Costa, Carolina Ferrer Gomez, José María Gallego Sánchez, Oscar, Gil-Albarova, Juan, Gilabert-Estellés, Emilio Lopez Alcina, Isabel López Sánchez, Severiano Marin Bertolin, Carolina, Martinez-Perez, Antonio Melero Abellán, Claudia Mulas Fernández, Beatriz, Novoa, Ruth, Nuñez, Pablo Renovell Ferrer, Guijarro-Jorge, Ricardo, Carolina Soledad Romero Garcia, Miguel Sanfeliu Giner, Silvia, M West, Enrique, Zapater, Ignacio, Miranda, Ana, Monís, Alejandro Roselló Añón, María José Sangüesa, Jorge Sanz Romera, Marcos, Adrianzén, Maria Barrios Carvajal, Mireia, Bauzá, Ana Benítez Riesco, Jose Antonio Carbonell Lopez, María-Carmen, Fernández-Moreno, Ricardo Gadea Mateo, Francisco, Garcia, Maris Luisa García-Pérez, Luisa Paola Garzon, Ana, Izquierdo, Maria Lapeña Rodríguez, Carlos, León-Espinoza, Fernando, Lopez, Rosa Martí Fernández, Sara Martínez Castro, Berta Monleón López, Isabel Mora Oliver, David, Moro-Valdezate, Ernesto Muñoz Sornosa, Sara Palomares Casasús, Leticia Pérez Santiago, Mara Albert Fort, Reyes, Balanzá, Lorena Bermell Marco, Carla Carratalá Pérez, Claudia Pilar Clemente Tomas, Marta, Córcoles, Jose Ángel Diez Ares, Carlos Domingo Del Pozo, Nuria Estellés Vidagany, Gonzalo, Garrigos, Mar Gimeno Gimeno Vicente, Blanca Alastrue Giner, Segundo, Gomez-Abril, Paloma, Gonzalez, Paula Gonzálvez Guardiola, Guillermo Sebastian Martínez Fernández, Charo Martínez García, Lucía, Martínez-Costa, Luz María Moratalla Charcos, Sergio Navarro Martínez, Carmen, Payá-Llorente, Álvaro Pérez Rubio, Nuria, Peris, Eduardo Picazo Pineda, Jordi Planelles Gómez, Francisco Ripoll Vidal, Andrea, Roca, Enrique, Salmerón-González, Raquel Segura Roselló, Ramon Trullenque Juan, Cristina Verdejo Gimeno, Jose Miguel Zaragozá García, Gema Zomeño Bravo, Marcos Bruna Esteban, Hanna, Cholewa, Milton de Jesus, Matteo, Frasson, Luis Oliver García, Héctor, J Aguado, Antonio Jose Alonso Villalba, Francisco, Ardura, Carmen Elena Badillo Bercebal, María Bedate Núnez, Alvarez-Ramos Begoña Aranzazu, Juan Beltrán de Heredia, Hugo, Bermejo, Juan Berrocal Cuadrado, Maria Bragado González, Juan, Bustamante-Munguira, Raul Calvo Gonzalez, Yolanda, Carrascal, Ana Belén Casas Marcos, María Carmen Cervera, Sergio Chávez Valladares, Ángel Cilleruelo Ramos, Susana, Cortes, Jesús, Crespo-Sanjuán, María, Cuaresma, Luis Antonio Cuellar Martin, Carlos Ferreras García, Alvaro, Fuentes-Martín, Abel, Ganso, Elena García García, Luis, Garcia-Florez, Virginia, Garcia-Virto, Juan, Gatón, Julio Alberto Gobernado Tejedor, Tania Gómez Sanz, Jose Ignacio González Martín, María Begoña Gregorio Crespo, Ana Herranz Arriero, Jose, Herreros, Marta Ibáñez Nieto, César, Infante, Carlos, Jezieniecki, Moises, Juarez, Gregorio de Jesus Labrador Hernandez, Ana Patricia Legido Morán, Ricardo León Fernández, Alejandro, Leon-Andrino, Almudena, Llorente, Maria Lopez Pais, Mauricio Loucel Bellino, Sergio, Martin, Miguel Angel Martin-Ferrero, Gonzalo Martinez Municio, Elvira Mateos Alvarez, José María Matilla, Luis María Merino Peñacoba, Mario, Montes-Manrique, Fernando Natal Álvarez, David, C Noriega, Henar Nuñez Del Barrio, Francisco Javier Ortiz de Solórzano Aurusa, Lucía, Pañeda, Victoria Pascual Escudero, Miguel Pascual Samaniego, Adela, Pereda, Marta Pérez Febles, Laura, Pesquera, Maria, Plata, Emma Puertas Ruiz, Mario, Rodriguez-Lopez, Alejandro Romero de Diego, Ana, Ruano, María Ruiz Soriano, Cristina Sánchez Torralvo, Silvia Santiago Maniega, Bárbara, Segura-Méndez, Clarisa Simon Perez, José, Soro-García, Paula Suárez Mansilla Suárez-Mansilla, Jeancarlos Trujillo Díaz, Mª Esther Valsero Herguedas, Andrea Vazquez Fernandez, Eduardo Velasco García, Ana, Zabalza, Álvaro Zamora Horcajada, Fernando Acebes García, Enrique Asensio Díaz, Martín, Bailón, Alejandro David Bueno Cañones, Alvaro Centeno Velasco, Ekta Choolani Bhojwani, Fernando Labarga Rodríguez, Sara, Mambrilla, Pablo, Marcos-Santos, Toledano, Miguel, David Pacheco Sánchez, Baltasar, Pérez-Saborido, Pilar, Pinto, Katherine, Plua, Javier Sanchez Gonzalez, Vicente, Simo, Rosalía Velasco López, Sandra Veleda Belanche, Fabiola Eugenia Michel Campos, Iciar Muñoz Lindez, Maria, Balluerca, Maite, Camuera, Pablo, Helguera, Imanol, Herrero, Patricia Paunero Vazquez, María, Sánchez-Rubio, Isami, Soeda, María Dolores Arribas Del Amo, Vicente Manuel Borrego Estella, Estefania, Casas, Néstor Castán Villanueva, Daniel Delfau Lafuente, Elena Delgado Blanco, María, Domingo, Ismael Gil Romea, Azucena, Gonzalo, Emilio Lagunas Lostao, Noelia Lete Aguirre, Yaiza Martinez Lahoz, Jesús Víctor Pérez-Tierra Ruiz, Maria Del Mar Soriano, Isabel Valero Lázaro, Esmeralda Carnicer Escusol, Guillermo Pola Bandres, Juan Luis Blas Laina, Beatriz, Cros, Patricio Andrés Freile Pazmiño, David, Garcia-Aguilera, Ana Mejía Casado, Ana, Nogués, Issa Talal El-Abur, Carlos, Yánez, Isabel Gascon Ferrer, Carlos, Gracia-Roche, Ursula María Jariod Ferrer, María Victoria Simón Sanz, Tomas, Uson, Waseem, Ahamed, Sumudu, Jayasinghe, Eranda, Karunadasa, Prathibha, Nelihela, Sujeewa Priyantha Bandara Thalgaspitiya, Dickson, Wickramarathna, Nagenthiram, Harivallavan, Pirashanthan, Nagarasa, Eranga, Perera, Kanapathipillai, Piratheep, Vithranage Srimantha Dewsiri Rodrigo, Meera, Thayalan, Rajendram, Thayaparren, Arulprashanth, Arulanantham, Kanishka De Silva, Gagana, Ganga, Anuja Nadeeshan Kumarasinghe, Mahanada, Udukala, Koculen, Vimaleswaran, Prashanthan, Yogarajah, Ravindri, Jayasinghe, Probhodana, Ranaweera, Mohamed, Rishard, Dharmabandhu, N Samarasekera, Sanjeewa, Seneviratne, Sivasuriya, Sivaganesh, Chamal, Fernando, Nishantha, Mendis, Balasingam, Balagobi, Balasupramaniam, Sathesan, Lanka, Dasanayake, Malitha, Patabendige, Wickramasooriya, Dj, Kugarajh, Ganeshapillai, Sathyamoorthy, Prasanna, Dileepa, Ediriweera, Sumudu, Kumarage, Asanka, Jayawardane, Athula, Kaluarachchi, Pituwala Liyanage Adithya Sirisena, Abubakr, Abubakr, Hozifa, Mohamed, Ismail, Mustafa, Mogahid, Sharfeldein, Samir, Ismail, Reem, Musa, Monira, Sarih, Mussab, Abaker, Eman Adam Abdalla, Mohamad, Abdelbagi, Alsafe, Adlan, Ahmed, Alnaeem, Safa, Atyah, Yasir, Awad, Rsheeda, Dawelbait, Mohamed El Hag, Ibrahim, Elbashir, Sami, Eldirdiri, Samwal, Eldirdiri, Mohammed Eltahier Abdalla Omer, Lana Abdalgadir Ahmed Mohamed, Hiba Hassan Rehmtallah Ahmed, Mohammed Balla Yousif Balla, Abdulmoiz, Aljafari, Abdulmalek, Aljafari, Elsagad, Mohamed, Abdulkader, Mohammad, Abdulkader, Shaar, Mohamed Nafea Shaar, Maab Hussein Yousif Elhafiz, Mohammed Husien Yosif Elhafiz, Abubakr Ali Mohammed Alhassan Humidan, Almigdad, Ali, Hisham, Eljack, Mohamedyasin, Elrashid, Manhal, Ahmed, Albrra, Alhag, Douaa, Ali, Mohammed Ahmed Babikir, Imad, Bakheit, Hytham K, S Hamid, Sami, Mohamed, Walid, Shaban, Nidhal, Siddig, Mohamed, Suliman, Rihab Yasir Abdelmagid, Abubaker, Yassin, Omer, Abbas, Eltahir, Abdelrahim, Muhammed, Ahmed, Mohamed, Elobaid, Adil, Hashim, Ali, Mohamed, Omer, Mohamed, Khalid, Osman, Abdallah, Yousif, Safaa, Abdulaal, Marwa, Elsadig, Mohammed, Hussain, Malaz Amir Ataalmanan, Doha Amir AtaAlmanan, Tebyan Amir AtaAlmanan, Alaa, Elgaili, Maria, Mohamed, Usra, Omara, Alkhansa, Osman, Amna, Osman, Safaa, Saad, Abdelkareem, Ahmed, Ali Adil Ali Karar, Aya, Alhassan, Mohanad, Alsidig, Mohamed, Eljack, Mohammed Ibrahim Mohammed Ali, Awad Kamal Awad Osman, Liena, Mohamed, Samah Ibrahim Omer Mohamed Osman Mohamed Osman, Altayeb, Mohammed, Ayman, Mohammed, Khadija, Muhmmed, Eltahir, Musa, Egbal Sahal Abdelmajed, Khalid Z M, Y Hamid, Omnia M, M Yousif, Mahmoud, Saleh, Osman, Bashir, Yassein, Elhussein, Omar, Madani, Eva, Angenete, Jennifer, Park, Lars, Gillberg, Monir, Jawad, Linas, Pieteris, Yousif, Saeed, Sergej, Safonov, Martin, Spångfors, Henrik, Andersson, Hans, Bahlmann, Karin Björnström Karlsson, Michelle, Chew, Lara, Daham, Lina De Geer, Helen, Didriksson, Gunilla, Gagnö, Carina, Jonsson, Anna, Oscarsson, Pamela, Buchwald, Mathilde, Delorme, Marie-Louise, Lydrup, Shahin, Mohseni, Arvid, Pourlotfi, Souheil, Reda, Kajsa, Anderin, Nina, Blomme, Josefin, Segelman, Malin, Simlund, Anders, Thorell, Fredrik, Wogensen, Daniel, Danielsson, Peter, Elbe, Rebecka, Hultgren, Boel, Hynning, Fredrik, Klevebro, Karin, Lind, Ebba, K Lindqvist, Carl, Montan, Ernesto, Sparrelid, Clara Svenberg Lind, Jeremy, Wales, Andreas, Älgå, Martin, Nordberg, Emil, Pieniowski, Ioannis, Gkekas, Niklas, Löfgren, Karolina, Niska, Robert, Zürner, Silvio, Däster, Otto, Kollmar, Savas, Soysal, Stephanie, Taha-Mehlitz, Athanasios, Tampakis, Dominik Valentin Flury, Marie, Heyne-Pietschmann, 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Travis, Grotz, James, Jakub, Rahul, Kanade, David, Larson, Sherief, Shawki, Dennis, Wigle, Haradeen, Dhillon, Avni, Suri, Leonardo Weber Graeff, Ahmed, Elshabrawy, Grace Ihsiu Todd, Mark, Davies, Yan, Qi, Walter, Biffl, Kathryn, Schaffer, Sarah, Averbach, Terry, Curry, Alexandra, Schwartz, Mary Kathryn Abel, Veronica, Andaya, Kristina, Benirschke, Katherine, Bigay, Marissa, Boeck, Lee-Lynn, Chen, Lee-May, Chen, Huey-Lan, Chern, Amanda, Compadre, Patricia, Conroy, Alexa, Glencer, Patrick, Ha, Barbara Cs Hamilton, Johannes, R Kratz, Rondall, Lane, Rex, H Lee, Brenda, Nunez-Garcia, Richard, O'Donnell, Doruk, Ozgediz, Paul, Park, Ankit, Sarin, Ipsit, Shah, Bonnie, Sheu, Madhulika, Varma, Katherine, Wai, Rosanna, Wustrack, Mary Jue Xu, Melissa, Zimel, Pedro, Escobar, Meredid Maldonado Santiago, Mohammad, Azam, Asad, Choudhry, Eric, Hammond, William, Marx, Jihad, Abbas, Somya, Al-Embideen, Carol, Angel, Bailea, Bobich, Raul, Bosio, Gregory, Georgiadis, Tahir, Jamil, Maria, Kenner, Meghan, Lark, Tamara, Maghathe, Sonia, Masih, Gabriel, Naimy, Kristin, O'Mara-Gardner, Emily, Rady, Roberta, Redfern, Jack, Sample, Joseph, Sferra, Kaitlyn, Sharp, Jessica, Shoemaker, Stephen, Stanek, John, Stengle, Hibba, Sumra, Jeffrey, M Sutton, Faris, Azar, Anne, Fischer, Jaymie, Henry, Lawrence, Lottenberg, Mario, Rueda, Sasha, Thiel, Adel, Bozorgzadeh, John, Kelly, Armando Salim Munoz Abraham, Erin, Scott, Camila, Haro, Andrés, Pouy, Gastón, Acuña, Sofia, Álvarez, Ana Carmen Carbajal, Fabiola, Castedo, Diego, Pereiranuñez, Helena, Sobrero, Josefina, Tarigo, Rafat, Al-Saban, Fatima, Al-Eryani, Siham Al Maqtari, Sahar, Al-Maqtari, Mahmmoud, Abdualqader, Majed, Aeed, Ghadeer Al Sanany, Anwaar Al Dhafif, Buthina Al Jarmozi, Saba, Al-Ameri, Awsan, Al-Dhaheri, Dhyia Alhuq Al-Surimi, Amira, Alakhdury, Sabreen, Alashmali, Zainab, Alattas, Manar, Alduba'Ai, Hamdan, Aldumaini, Wafa, Alhaddad, Abdelrahman, Alharazi, Nawal, Alhemyari, Adnan, Alhumaida, Maniee, Aljabri, Hassan, Almarashi, Zuhoor, Almohanady, Marwa, Alodine, Nooraldin, Alqasemi, Basma, Alqudaimi, Ansam Mohammed Ghaleb Alrobaiee, Mohammed, Alsabri, Radfan, Alsalal, Ramzi, Alsayadi, Musaed, Alsayadi, Reem, Alshaipani, Ebrahim, Alsharabi, Nora, Atiah, Hadeel, Bajjah, Muhib, Esmael, Asma, Ghallab, Rana, Ghannam, Adwa'A, Hamid, Lina, Hashem, Sara, Saif, Shaima, Sayad, Nawras, Shaban, Amatallah Hadi Shamsan, Sarah, Shream, Amal, Shukri, Shahd, Zeid, Nabila, Abdullah, Abdulrahman Faisal Al-Garadi, Ahmed, M Ali Al-Radhi, Norhan, Naji, Shaima'A, Nasser, Areej Taha Al Anaib, Ebrahim, Abdurab, Fadhl Al Muhtadi, Amer, Al-Hebbah, Ahmed, Al-Madhrahi, Shehab, Al-Mahdi, Sakhr Abdulhakeem Al-Maswari, Ayman, Al-Oqabi, Zakarya Abdullah Al-Zaazaai, Karim, Al-Zazay, Ali, Alafif, Asem, Albzzaz, Lina, Alqalisi, Mohammed, Alrezami, Haneen, Alshargabi, Mokhtar, Alshargabi, Hossam, Ghanem, Kareem, Saeed, Rasheed, Shalabi, Fareed, Showqi, Miriam, Maimbo, Catherine, Mkandawire, Mulaya, Mubambe, Theresa, Nkole, Vanessa, Savopoulos, Patricia, Shinondo, Chabwera, Shumba, Penias, Tembo, Shelton, Chivanga, Ngqabutho, Dube, Handsome, Dube, Daniel, Dzinotyiwei, Ronald, Makanda, Moses, Mangena, Partson, Maphosa, Derek, Matsika, Proud, Mawere, Nkosikhona, Moyo, Assel, Moyo, Arnold, Muguwu, Simbarashe, Mungazi, Garikai, Mwale, Gamuchirai, Ndabvonga, Mambote Crispin Olivier Ntoto, Hardlife, Ranganai, Ernesto Sánchez Castillo, Oltah, Tshuma, Grace, Gwini, Vimbai Moyana Muguto, Gilbert, Moyo, Trust, Mushawarima, Tinashe, Shoko, Rorisang, Jamela, Marvellous, Machiri, Eric, Shumba, Antony, Chengahomwe, Yemurai, Bikwa, Maxwell, Chimhina, Jeremy, Marume, Kudzayi, Munanzvi, Precious G, T Mutambanengwe, Federico, Ferrari, Marco, Fontanella, Franco, Odicino, Romano, Vito, Nepogodiev D, Emanuele Cammarata, Sofia Campanella, Daniela Canzonieri, Adriana Cordova, Federico De Michele, Ettore Dinoto, Mara Franza, Leo Licari, Daniele Matta, Domenico Mirabella, Felice Pecoraro, Roberto Pirrello, Pierfrancesco Pugliese, Fernando Rosatti, Giuseppe Salamone, Francesca Toia, Massimiliano Tripoli, Nepogodiev, D, Rosati, R, Surgery, Medicine and Pharmacy academic/administration, Basic (bio-) Medical Sciences, Surgical clinical sciences, Faculty of Medicine and Pharmacy, Gastroenterology, Medical Oncology, Clinical sciences, Breda, João, ARD - Amsterdam Reproduction and Development, Paediatric Surgery, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, AGEM - Endocrinology, metabolism and nutrition, Graduate School, Tytgat Institute for Liver and Intestinal Research, Laura Alberici, Filippo Antonacci, Alessandro Arena, Angela Belvedere, Fabio Bernagozzi, Paolo Bernante, Pietro Bertoglio, Lorenzo Bianchi, Maria Bisulli, Barbara Bonfanti, Safia Boussedra, Jury Brandolini, Crescenzo Cacciapuoti, Stefano Cardelli, Riccardo Casadei, Matteo Cescon, Alessandro Cipolli, Riccardo Cipriani, Alfredo Conti, Luca Contu, Francesco Costa, Niccolo’ Daddi, Eugenia De Crescenzo, Pierandrea De Iaco, Alessandra De Palma, Massimo Del Gaudio, Anna Nunzia Della Gatta, Giampiero Dolci, Giulia Dondi, Matteo Droghetti, Sergio Nicola Forti Parri, Elena Garelli, Chiara Gelati, Giuliana Germinario, Federico A. Giorgini, Carlo Ingaldi, Elio Jovine, Kenji Kawamukai, Antonio Lanci Lanci, Raffaele Lombardi, Maria Elisa Lozano Miralles, Claudio Marchetti, Michele Masetti, Francesco Minni, Daniele Morezzi, Daniele Parlanti, Alice Pellegrini, Anna Myriam Perrone, Anna paola Pezzuto, Marco Pignatti, Gianluigi Pilu, Valentina Pinto, Gilberto Poggioli, Silvana Bernadetta Puglisi, Diego Raimondo, Matteo Ravaioli, Claudio Ricci, Sara Ricciardi, Francesco Ricotta, Roberta Rizzo, Angela Romano, Matteo Rottoli, Riccardo Schiavina, Renato Seracchioli, Matteo Serenari, Margherita Serra, Piergiorgio Solli, Gioia Sorbi, Mario Taffurelli, Marta Tanzanu, Achille Tarsitano, Marco Tesei, Gabriele Vago, Tommaso Violante, Simone Zanotti, Tommaso Frisoni, leonardo solaini, giorgio ercolani, Fruscio, R, Ornaghi, S, Collaborative, Covidsurg, Collaborative, Globalsurg, Palmisano, Silvia, de Manzini, Nicolo', Žundienė, Laura, Dulskas, Audrius, Kuliavas, Justas, Čekauskas, Albertas, Kielaitė-Gulla, Aistė, Poškus, Tomas, Strupas, Kęstutis, Želvys, Arūnas, COVIDSurg Collaborative, GlobalSurg Collaborative, Department of Surgery, HUS Abdominal Center, Simoes, Jff, Li, E, Picciochi, M, Glasbey, Jc, Baiocchi, G, Blanco-Colino, R, Chaudhry, D, Alameer, E, El-Boghdadly, K, Wuraola, F, Ghosh, D, Gujjuri, Rr, Harrison, Em, Lule, H, Kaafarani, H, Khosravi, M, Kronberger, I, Leventoglu, S, Mann, H, Mclean, Ka, Mengistu, Gm, Modolo, Mm, Ntirenganya, F, Norman, L, Outani, O, Pius, R, Pockney, P, Qureshi, Au, Roslani, Ac, Satoi, S, Shaw, C, Bhangu, A, Omar, Om, Ahmed, Wur, Argus, L, Ball, A, Bywater, Ep, Brar, A, Dawson, Be, Duran, I, Elhadi, M, Jones, C, Kamarajah, Sk, Keatley, Jm, Lawday, S, Marson, Ej, Ots, R, Santos, I, Taylor, Eh, Trout, Im, Varghese, C, Venn, Ml, Xu, W, Dajti, I, Gjata, A, Kacimi, Seo, Boccalatte, L, Cox, D, Townend, P, Aigner, F, Kronberger, Ie, Samadov, E, Alderazi, A, Hossain, K, Padmore, G, van Ramshorst, G, Lawani, I, Cerovac, A, Delibegovic, S, Gomes, Gma, Buarque, Il, Gohar, M, Slavchev, M, Nwegbu, C, Agarwal, A, Martin, J, Ng-Kamstra, J, Olivos, M, Lou, Wh, Ren, Dl, Calvache, Ja, Rivera, Cjp, Hadzibegovic, Ad, Kopjar, T, Mihanovic, J, Jimenez, Pma, Gouvas, N, Klat, J, Novysedlak, R, Amisi, N, Christensen, P, El-Hussuna, A, Batista, S, Lincango-Naranjo, E, Emile, S, Sandoval, Daa, Dhufera, H, Hailu, S, Mengesha, Mg, Kauppila, Jh, Arnaud, Ap, Demetrashvili, Z, Albertsmeier, M, Lederhuber, H, Loffler, Mw, Acquah, Dk, Ofori, B, Tabiri, S, Metallidis, S, Tsoulfas, G, Aguilera-Arevalo, Ml, Recinos, G, Mersich, T, Wettstein, D, Kembuan, G, Milan, Pb, Khosravi, Mh, Mozafari, M, Hilmi, A, Mohan, H, Zmora, O, Gallo, G, Pata, F, Pellino, G, Fujimoto, Y, Kuroda, N, Abou Chaar, Mk, Ayasra, F, Fakhradiyev, I, Hamdun, Ih, Jin-Young, J, Jamal, M, Karout, L, Gulla, A, Rasoaherinomenjanahary, F, Samison, Lh, Sanchez, Iid, Gonzalez, D, Martinez, L, Martinez, Mj, Nayen, A, and Ramos-De la Medina, A
Subjects: Male, Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], MULTICENTER, infectious diseases, 0302 clinical medicine, 030202 anesthesiology, Anesthesiology, Cirugía, 80 and over, Prospective Studies, Young adult, Child, Lung, Aged, 80 and over, COVID-19, delay, SARS-CoV-2, surgery, timing, SARS‐CoV‐2 infection, Operative, Child, Preschool, Pneumonia & Influenza, Infection, Cohort study, Human, medicine.medical_specialty, Clinical Sciences, Sars-cov-2, GlobalSurg Collaborative, Settore MED/28 - MALATTIE ODONTOSTOMATOLOGICHE, 03 medical and health sciences, Clinical Research, Biodefense, Correspondence, Humans, PULMONARY COMPLICATIONS, CRIANÇAS EM IDADE PRÉ-ESCOLAR, Aged, Science & Technology, Cirurgia, Prevention, Infant, Odds ratio, Pneumonia, Prospective Studie, Clinical research, Surgery, Human medicine, 610 Medizin und Gesundheit, 1109 Neurosciences, Internationality, Settore MED/29 - CHIRURGIA MAXILLOFACCIALE, Settore MED/18 - CHIRURGIA GENERALE, Adolescent, Adult, Cohort Studies, Female, Middle Aged, Surgical Procedures, Operative, Time, Young Adult, Practice Guidelines as Topic, gastroenterology, Medicine and Health Sciences, 030212 general & internal medicine, Prospective cohort study, Mortality rate, Covid19, Infectious Diseases, Patient Safety, medicine.symptom, 6.4 Surgery, Life Sciences & Biomedicine, 610 Medicine & health, COVID-19, SARS-CoV-2, delay, surgery, timing, Asymptomatic, NO, COVIDSurg Collaborative, Vaccine Related, All institutes and research themes of the Radboud University Medical Center, medicine, Mortalitat, ddc:610, Mortality, Preschool, LS7_4, Surgical Procedures, business.industry, Neurosciences, Evaluation of treatments and therapeutic interventions, 1103 Clinical Sciences, 3126 Surgery, anesthesiology, intensive care, radiology, Reconstructive and regenerative medicine Radboud Institute for Health Sciences [Radboudumc 10], Anesthesiology and Pain Medicine, Emerging Infectious Diseases, Good Health and Well Being, Mortalidad, Cohort Studie, business
Abstract: Peri-operative SARS-CoV-2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS-CoV-2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre-operative SARS-CoV-2 infection were compared with those without previous SARS-CoV-2 infection. The primary outcome measure was 30-day postoperative mortality. Logistic regression models were used to calculate adjusted 30-day mortality rates stratified by time from diagnosis of SARS-CoV-2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre-operative SARS-CoV-2 diagnosis. Adjusted 30-day mortality in patients without SARS-CoV-2 infection was 1.5% (95%CI 1.4-1.5). In patients with a pre-operative SARS-CoV-2 diagnosis, mortality was increased in patients having surgery within 0-2 weeks, 3-4 weeks and 5-6 weeks of the diagnosis (odds ratio (95%CI) 4.1 (3.3-4.8), 3.9 (2.6-5.1) and 3.6 (2.0-5.2), respectively). Surgery performed ≥ 7 weeks after SARS-CoV-2 diagnosis was associated with a similar mortality risk to baseline (odds ratio (95%CI) 1.5 (0.9-2.1)). After a ≥ 7 week delay in undertaking surgery following SARS-CoV-2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2-8.7) vs. 2.4% (95%CI 1.4-3.4) vs. 1.3% (95%CI 0.6-2.0), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS-CoV-2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay. ispartof: ANAESTHESIA vol:76 issue:6 pages:748-758 ispartof: location:England status: published
Published: 2021

8. Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions

Author: Yves A. Lussier, Hao Helen Zhang, Wesley Chiu, Samir Rachid Zaim, Colleen Kenost, Liam Wilson, and Joanne Berghout
Subjects: Computer science, Applied Mathematics, Correction, Computational Biology, Breast Neoplasms, Computational biology, lcsh:Computer applications to medicine. Medical informatics, Biochemistry, Kidney Neoplasms, Computer Science Applications, Random forest, lcsh:Biology (General), Structural Biology, Biomarkers, Tumor, Humans, lcsh:R858-859.7, Biomarker (medicine), Female, lcsh:QH301-705.5, Molecular Biology, Algorithms, Biomarkers
Abstract: In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are more features (i.e., transcripts) than samples (i.e., mice or human samples) in a study, it poses major statistical challenges in biomarker detection tasks as traditional statistical techniques are underpowered in high dimension. Second and third order interactions of these features pose a substantial combinatoric dimensional challenge. In computational biology, random forest (RF) classifiers are widely used due to their flexibility, powerful performance, their ability to rank features, and their robustness to the "P N" high-dimensional limitation that many matrix regression algorithms face. We propose binomialRF, a feature selection technique in RFs that provides an alternative interpretation for features using a correlated binomial distribution and scales efficiently to analyze multiway interactions.In both simulations and validation studies using datasets from the TCGA and UCI repositories, binomialRF showed computational gains (up to 5 to 300 times faster) while maintaining competitive variable precision and recall in identifying biomarkers' main effects and interactions. In two clinical studies, the binomialRF algorithm prioritizes previously-published relevant pathological molecular mechanisms (features) with high classification precision and recall using features alone, as well as with their statistical interactions alone.binomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers' main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide pathway-level feature selection from gene expression input data.
Published: 2020

9. Developing, Integrating, and Implementing Evidence-Informed Practice Curricula Throughout a Chinese Medicine Degree Program

Author: Olga Reznikova, Marianne Fuenmayor, Belinda J. Anderson, Roni Evans, Paul R. Marantz, Kathleen A. Greenough, Robert L. Saronson, Helen Zhang, Benjamin Kligler, Stacy L. Gomes, William J. Casalaina, and Jason Ginsberg
Subjects: Complementary Therapies, Faculty, Medical, Inservice Training, curriculum development, education, New York, Traditional Chinese medicine, Evidence informed, 03 medical and health sciences, 0302 clinical medicine, Degree program, ComputingMilieux_COMPUTERSANDEDUCATION, Acupuncture, Curriculum development, Medicine, Humans, Medicine, Chinese Traditional, Program Development, Curriculum, Medical education, Evidence-Based Medicine, ComputingMilieux_THECOMPUTINGPROFESSION, Chinese medicine, business.industry, Evidence-based medicine, Original Articles, 030205 complementary & alternative medicine, Complementary and alternative medicine, faculty training, business, acupuncture, evidence informed practice, Education, Medical, Undergraduate
Abstract: Objectives: To train faculty and develop curricula in evidence-informed practice (EIP) within a Chinese medicine degree program. Setting: Pacific College of Oriental Medicine (New York). Design: Faculty EIP training was undertaken through utilization of online EIP modules, and development and completion of a 3-credit (45 h) online Foundations of EIP course. This was supplemented by faculty meetings and one-on-one support from department chairs. Curriculum development was undertaken by an EIP Curriculum Committee. The committee followed a modified Delphi process to develop EIP course learning outcomes (CLOs), and to make changes to the College's clinic policies and procedures. EIP assignments were developed for each course in accordance with the CLO. Results: Ninety-one percent of the faculty and 97% of clinical supervisors received formal EIP training. Thirty-five percent of all didactic faculty, 38% of faculty teaching courses with EIP incorporated, and 30% of clinical supervisors completed 10 or more h of EIP training during this project. Faculty also received informal EIP training through participation in department and general faculty meetings. Seventy-three percent of the Master's degree curriculum, inclusive of 40 didactic courses and fifteen 60-h clinic shifts, were modified to incorporate EIP. EIP CLOs and corresponding assignments were developed. Clinic intake forms were modified to facilitate undertaking EIP in the College clinic. Issues related to how EIP is defined in conjunction with the nature of available scientific research in Chinese medicine required discussion and resolution. Conclusions: Training faculty and developing curricula in EIP within Chinese medicine colleges has unique challenges that must be factored into the strategies and processes. Factors that contributed to the success of this project were having faculty drive the process, integrating EIP content within existing curricula, gradual exposure, identifying champions, relating EIP to practice building, and openly discussing opposing perspectives.
Published: 2020

10. Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study

Author: Wei-Hsuan Lo-Ciganic, Walid F. Gellad, Hao Helen Zhang, Gerald Cochran, James L. Huang, Courtney C. Kuza, Jeremy C. Weiss, Adam J. Gordon, C. Kent Kwoh, Julie M. Donohue, and Daniel C. Malone
Subjects: Male, Social Sciences, 030204 cardiovascular system & hematology, Proxy (climate), Decile, Machine Learning, 0302 clinical medicine, Primary outcome, Mathematical and Statistical Techniques, Cognition, Learning and Memory, Medicine and Health Sciences, 030212 general & internal medicine, Fee-for-service, Analgesics, Multidisciplinary, Applied Mathematics, Simulation and Modeling, Statistics, Medicare beneficiary, Drugs, Opioid use disorder, Fee-for-Service Plans, Middle Aged, Prognosis, Physical Sciences, Memory Recall, Medicine, Female, Algorithms, medicine.drug, Research Article, Computer and Information Sciences, Neural Networks, Political Science, Science, Public Policy, Research and Analysis Methods, Medicare, Risk Assessment, 03 medical and health sciences, Artificial Intelligence, Diagnostic Medicine, Memory, medicine, Pain Management, Humans, Statistical Methods, Aged, Pharmacology, business.industry, Biology and Life Sciences, Computational Biology, medicine.disease, Opioid-Related Disorders, United States, Opioids, Cognitive Science, business, Mathematics, Buprenorphine, Demography, Methadone, Forecasting, Neuroscience
Abstract: ObjectiveTo develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions.MethodsThis prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling ≥1 opioid prescriptions from 2011-2016. We randomly divided beneficiaries into training, testing, and validation samples. We measured 269 potential predictors including socio-demographics, health status, patterns of opioid use, and provider-level and regional-level factors in 3-month periods, starting from three months before initiating opioids until development of OUD, loss of follow-up or end of 2016. The primary outcome was a recorded OUD diagnosis or initiating methadone or buprenorphine for OUD as proxy of incident OUD. We applied elastic net, random forests, gradient boosting machine, and deep neural network to predict OUD in the subsequent three months. We assessed prediction performance using C-statistics and other metrics (e.g., number needed to evaluate to identify an individual with OUD [NNE]). Beneficiaries were stratified into subgroups by risk-score decile.ResultsThe training (n = 120,474), testing (n = 120,556), and validation (n = 120,497) samples had similar characteristics (age ≥65 years = 81.1%; female = 61.3%; white = 83.5%; with disability eligibility = 25.5%; 1.5% had incident OUD). In the validation sample, the four approaches had similar prediction performances (C-statistic ranged from 0.874 to 0.882); elastic net required the fewest predictors (n = 48). Using the elastic net algorithm, individuals in the top decile of risk (15.8% [n = 19,047] of validation cohort) had a positive predictive value of 0.96%, negative predictive value of 99.7%, and NNE of 104. Nearly 70% of individuals with incident OUD were in the top two deciles (n = 37,078), having highest incident OUD (36 to 301 per 10,000 beneficiaries). Individuals in the bottom eight deciles (n = 83,419) had minimal incident OUD (3 to 28 per 10,000).ConclusionsMachine-learning algorithms improve risk prediction and risk stratification of incident OUD in Medicare beneficiaries.
Published: 2020

11. Channelopathy as a SUDEP Biomarker in Dravet Syndrome Patient-Derived Cardiac Myocytes

Author: Helen Zhang, David S. Auerbach, Louis T. Dang, Jack M. Parent, Chunling Chen, Vassilios J. Bezzerides, Chad R. Frasier, Lee L. Eckhardt, Alica M. Goldman, James Offord, Lori L. Isom, and Huilin Shi
Subjects: 0301 basic medicine, Male, medicine.medical_specialty, Induced Pluripotent Stem Cells, Epilepsies, Myoclonic, Biology, Biochemistry, 03 medical and health sciences, Epilepsy, Death, Sudden, 0302 clinical medicine, Channelopathy, Dravet syndrome, Internal medicine, Genetics, medicine, Myocyte, Humans, Myocytes, Cardiac, Child, lcsh:QH301-705.5, Cells, Cultured, lcsh:R5-920, Sodium channel, Cardiac arrhythmia, Arrhythmias, Cardiac, Cell Biology, medicine.disease, NAV1.1 Voltage-Gated Sodium Channel, 030104 developmental biology, lcsh:Biology (General), Child, Preschool, Cardiology, Biomarker (medicine), Channelopathies, Female, CRISPR-Cas Systems, Haploinsufficiency, lcsh:Medicine (General), 030217 neurology & neurosurgery, Developmental Biology
Abstract: Summary: Dravet syndrome (DS) is a severe developmental and epileptic encephalopathy with a high incidence of sudden unexpected death in epilepsy (SUDEP). Most DS patients carry de novo variants in SCN1A, resulting in Nav1.1 haploinsufficiency. Because SCN1A is expressed in heart and in brain, we proposed that cardiac arrhythmia contributes to SUDEP in DS. We generated DS patient and control induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs). We observed increased sodium current (INa) and spontaneous contraction rates in DS patient iPSC-CMs versus controls. For the subject with the largest increase in INa, cardiac abnormalities were revealed upon clinical evaluation. Generation of a CRISPR gene-edited heterozygous SCN1A deletion in control iPSCs increased INa density in iPSC-CMs similar to that seen in patient cells. Thus, the high risk of SUDEP in DS may result from a predisposition to cardiac arrhythmias in addition to seizures, reflecting expression of SCN1A in heart and brain. : In this article, Isom, Parent, and colleagues show that the high risk of SUDEP in the developmental and epileptic encephalopathy, Dravet syndrome, may result from a predisposition to cardiac arrhythmias in addition to neuronal hyperexcitability, reflecting haploinsufficiency of SCN1A in heart and brain causing potential compensatory overexpression of other sodium-channel genes in those tissues. Keywords: epilepsy, cardiac arrhythmia, sodium channel, SUDEP, induced pluripotent stem cell (iPSC), developmental and epileptic encephalopathy
Published: 2018

12. Sharpening the focus on ophthalmology teaching: perceptions of medical students and junior medical officers

Author: Ian C. Francis, Sascha K.R. Spencer, Jenny L Hepschke, Ashish Agar, Minas T. Coroneo, Helen Zhang, and Boaz Shulruf
Subjects: Male, medicine.medical_specialty, Students, Medical, Teaching method, media_common.quotation_subject, education, Likert scale, 03 medical and health sciences, 0302 clinical medicine, Ophthalmology, Perception, Medical Staff, Hospital, medicine, Humans, 030212 general & internal medicine, Curriculum, media_common, business.industry, Teaching, Medical school, Outcome measures, Confidence interval, Cross-Sectional Studies, 030221 ophthalmology & optometry, Female, Clinical Competence, business, Clinical skills, Education, Medical, Undergraduate
Abstract: Importance Worldwide, ophthalmology teaching is being reduced or eliminated from medical school curricula. The current state of ophthalmic teaching in Australia is unknown. Background To evaluate the perceptions of junior medical officers (JMOs) and medical students on ophthalmology teaching in Australian medical schools. Design Survey-based cross-sectional study. Participants A total of 838 JMOs and medical students from across Australia. Methods Fifty-six hospitals and 20 medical schools across Australia were contacted. Hardcopy and online surveys were distributed to participants at consenting institutions, evaluating the characteristics of ophthalmology teaching received during medical school and participant confidence in basic ophthalmological clinical skills and knowledge. Factor analysis was performed on confidence scores. Main outcome measures Likert scale confidence ratings, teaching methods encountered versus preferred. Results Four hundred and thirty-two (51.6%) surveys were received from JMOs and 406 (48.4%) from medical students. The most common form of teaching received were lectures (71.3% JMOs, 65.5% medical students), while the most preferred type were hospital tutorials (37.7% JMOs, 61.6% medical students). Mean confidence in ophthalmology-specific skills and knowledge topics were not high for medical students (skills: 2.66/5, 95% confidence interval [CI] = 2.55-2.76; knowledge: 2.88/5, 95% CI = 2.80-2.96) and JMOs (skills: 2.52/5, 95% CI = 2.43-2.60; knowledge: 2.84/5, 95% CI = 2.77-2.91). Many participants voiced the need for more ophthalmology teaching, particularly clinically oriented opportunities. Conclusions and relevance JMOs and medical students do not show high levels of confidence in basic ophthalmological clinical skills and knowledge, and report inadequate emphasis on ophthalmology during medical school.
Published: 2018

13. Effect of intermittent versus continuous low dose aspirin on nasal epithelium gene expression in current smokers: A randomized, double-blinded trial

Author: Carter Merenstein, Eva Szabo, Jose M. Guillen-Rodriguez, Lisa E. Davis, Malgorzata Wojtowicz, Chiu Hsieh Hsu, H-H. Sherry Chow, Avrum Spira, Xiaohui Zhang, David S. Alberts, Jennifer Beane, Michael Yozwiak, Julian Lel, Hao Helen Zhang, Linda L. Garland, Hanqiao Liu, and Gang Liu
Subjects: 0301 basic medicine, Male, Cancer Research, medicine.medical_specialty, Urinary system, Placebo, Systemic inflammation, Gastroenterology, Article, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Double-Blind Method, Internal medicine, medicine, Humans, Lung cancer, Inflammation, Aspirin, Creatinine, Smokers, Dose-Response Relationship, Drug, business.industry, Gene Expression Profiling, Anti-Inflammatory Agents, Non-Steroidal, Smoking, Gene signature, Middle Aged, medicine.disease, Prognosis, Gene expression profiling, Nasal Mucosa, 030104 developmental biology, Oncology, chemistry, Gene Expression Regulation, 030220 oncology & carcinogenesis, Female, medicine.symptom, business, Biomarkers, medicine.drug, Follow-Up Studies
Abstract: A chemopreventive effect of aspirin (ASA) on lung cancer risk is supported by epidemiologic and preclinical studies. We conducted a randomized, double-blinded study in current heavy smokers to compare modulating effects of intermittent versus continuous low-dose ASA on nasal epithelium gene expression and arachidonic acid (ARA) metabolism. Fifty-four participants were randomized to intermittent (ASA 81 mg daily for one week/placebo for one week) or continuous (ASA 81 mg daily) for 12 weeks. Low-dose ASA suppressed urinary prostaglandin E2 metabolite (PGEM; change of −4.55 ± 11.52 from baseline 15.44 ± 13.79 ng/mg creatinine for arms combined, P = 0.02), a surrogate of COX-mediated ARA metabolism, but had minimal effects on nasal gene expression of nasal or bronchial gene-expression signatures associated with smoking, lung cancer, and chronic obstructive pulmonary disease. Suppression of urinary PGEM correlated with favorable changes in a smoking-associated gene signature (P < 0.01). Gene set enrichment analysis (GSEA) showed that ASA intervention led to 1,079 enriched gene sets from the Canonical Pathways within the Molecular Signatures Database. In conclusion, low-dose ASA had minimal effects on known carcinogenesis gene signatures in nasal epithelium of current smokers but results in wide-ranging genomic changes in the nasal epithelium, demonstrating utility of nasal brushings as a surrogate to measure gene-expression responses to chemoprevention. PGEM may serve as a marker for smoking-associated gene-expression changes and systemic inflammation. Future studies should focus on NSAIDs or agent combinations with broader inhibition of pro-inflammatory ARA metabolism to shift gene signatures in an anti-carcinogenic direction.
Published: 2019

14. Burn patients with history of kidney transplant experience increased incidence of wound infection

Author: Jorge Ortiz, Helen Zhang, Weikai Qu, and Munier Nazzal
Subjects: Adult, Graft Rejection, Male, Burn injury, medicine.medical_specialty, medicine.medical_treatment, Critical Care and Intensive Care Medicine, Malignancy, Risk Factors, Internal medicine, Medicine, Humans, Hospital Mortality, Risk factor, Adverse effect, Healthcare Cost and Utilization Project, Aged, business.industry, Incidence (epidemiology), Incidence, Immunosuppression, General Medicine, Acute Kidney Injury, Length of Stay, Middle Aged, medicine.disease, Hospital Charges, Kidney Transplantation, Transplantation, Hospitalization, Case-Control Studies, Multivariate Analysis, Emergency Medicine, Wound Infection, Surgery, Female, business, Burns, Immunosuppressive Agents
Abstract: To determine if history of kidney transplant is an independent risk factor for increased incidence of wound infection and other morbidities in burn patients.While the goal of immunosuppression post-organ transplantation is to prevent graft rejection, it is often associated with significant adverse effects such as increased susceptibility to infection, drug toxicity, and malignancy. Burn injuries lead to a dysregulated hypermetabolic state and a compromised cutaneous barrier, which predisposes to infection and delayed wound healing. We surmise that a history of kidney transplant increases the risk of wound infection in in-hospital burn victims.A retrospective analysis was performed on 57,948 adults diagnosed in-hospital with a burn injury between 2008-2014, obtained from the Nationwide Inpatient Sample (NIS) by Healthcare Cost and Utilization Project (HCUP).103 burn victims (0.2%) with a history of kidney transplant (KTX) were identified. Compared to burn patients without a history of transplant (No-KTX), they were older (54.3 ± 13.8 vs 49.8 ± 18.7; p = 0.001), more likely be insured under Medicare (69.9% vs 31.1%; p 0.001), and less likely to have Medicaid (5.8% vs 17.2%; p = 0.002). Higher in-hospital mortality index scores were observed in KTX compared to no-KTX with p 0.001. The incidence rates of complications such as wound infection (33.0 vs 16.3; p 0.001) and acute renal failure (18.4 vs 7.7; p 0.001) were significantly higher in the KTX group. After adjusting for confounding factors in multivariable analysis, the incidence of wound infection remained significantly higher. Burn patients with history of KTX were not more likely to be treated at a transplant (TX) center. TX centers were determined to have higher mortality rate, longer length of stay, and higher total hospital charges.History of kidney transplant is an independent risk factor for increased incidence of wound infection in burn patients.
Published: 2019

15. Occurrence, Impact, Analysis and Treatment of Metformin and Guanylurea in Coastal Aquatic Environments of Canada, USA and Europe

Author: Yunwen, Tao, Bing, Chen, Baiyu Helen, Zhang, Zhiwen Joy, Zhu, and Qinhong, Cai
Subjects: Europe, Canada, Diabetes Mellitus, Humans, Urea, Metformin, United States, Water Pollutants, Chemical
Abstract: This review discusses the occurrence, impact, analysis and treatment of metformin and guanylurea in coastal aquatic environments of Canada, USA and Europe. Metformin, a biguanide in chemical classification, is widely used as one of the most effective first-line oral drugs for type 2 diabetes. It is difficult to be metabolized by the human body and exists in both urine and faeces samples in these regions. Guanylurea is metformin's biotransformation product. Consequently, significant concentrations of metformin and guanylurea have been reported in wastewater treatment plants (WWTPs) and coastal aquatic environments. The maximum concentrations of metformin and guanylurea in surface water samples were as high as 59,000 and 4502ngL
Published: 2018

16. Preface

Author: Bing Chen, Zhiwen (Joy) Zhu, Baiyu (Helen) Zhang, and Kenneth Lee
Subjects: Conservation of Water Resources, Water Pollution, Animals, Humans, Water Pollutants, Chemical, Environmental Monitoring
Published: 2018

17. High Expression of Human Homologue of Murine Double Minute 4 and the Short Splicing Variant, HDM4-S, in Bone Marrow in Patients With Acute Myeloid Leukemia or Myelodysplastic Syndrome

Author: Xin Han, M. James You, Michael Andreeff, Carlos E. Bueso-Ramos, L. Jeffrey Medeiros, Yu Helen Zhang, and Marina Konopleva
Subjects: Male, 0301 basic medicine, Cancer Research, Small interfering RNA, Pathology, Gene Expression, Cell Cycle Proteins, 0302 clinical medicine, Bone Marrow, hemic and lymphatic diseases, RNA, Small Interfering, Child, Aged, 80 and over, Gene Expression Regulation, Leukemic, Nuclear Proteins, Myeloid leukemia, Hematology, Middle Aged, Immunohistochemistry, Leukemia, Myeloid, Acute, medicine.anatomical_structure, Oncology, Child, Preschool, 030220 oncology & carcinogenesis, RNA splicing, Female, Adult, medicine.medical_specialty, Adolescent, Bone Marrow Cells, Young Adult, 03 medical and health sciences, Downregulation and upregulation, Cell Line, Tumor, Proto-Oncogene Proteins, medicine, Humans, In patient, RNA, Messenger, Aged, Chromosome Aberrations, business.industry, Infant, Alternative Splicing, Genes, ras, 030104 developmental biology, fms-Like Tyrosine Kinase 3, Apoptosis, Myelodysplastic Syndromes, Mutation, Cancer research, Bone marrow, business
Abstract: The human homologue of murine double minute 2 (HDM2) and HDM4 negatively regulate p53. HDM4 has not been assessed in acute myeloid leukemia (AML) or myelodysplastic syndrome (MDS). We examined the expression of HDM4 and the short splicing variant, HDM4-S, in bone marrow samples obtained from 85 and 23 patients with AML and MDS, respectively, and 18 negative tumor staging bone marrow samples (used as the control). Immunohistochemical staining showed that HDM4 was overexpressed in 78 AML cases (92%) and 12 MDS cases (52%) compared with 1 stressed bone marrow sample (6%). Quantitative reverse transcriptase-polymerase chain reaction analysis of 8 AML and 11 low-grade (LG)-MDS cases confirmed that HDM4 and HDM4-S mRNA expression were also elevated in all AML cases. HDM4 and HDM4-S mRNA expression was elevated in 3 (27%) and 10 (91%) LG-MDS cases, respectively. HDM4 and HDM4-S mRNA levels were higher in those with AML than in those with LG-MDS. In leukemia cell lines, HEL and U937 predominantly expressed HDM4-S. In contrast, NALM6 expressed HDM4 and HDM4-S. Downregulation of HDM4 expression by treatment with small interfering RNA in NALM6 and HEL cells induced p21 expression but not increased apoptotic activity. Our results indicate that HDM4 is a potential therapeutic target in patients with AML or MDS.
Published: 2016

18. Phase II Trial of Chemopreventive Effects of Levonorgestrel on Ovarian and Fallopian Tube Epithelium in Women at High Risk for Ovarian Cancer: An NRG Oncology Group/GOG Study

Author: David S. Alberts, Peter H. Bartels, Hao Helen Zhang, Michael Yozwiak, Larry G. Thaete, Samantha J. Russell, William G. Watkin, James Kauderer, Oliver Zivanovic, Jack Basil, Gustavo C. Rodriguez, Susan C. Modesitt, Jean A. Hurteau, Shashikant Lele, and Jessica Hunn
Subjects: 0301 basic medicine, Oncology, Adult, Cancer Research, medicine.medical_specialty, endocrine system, Proliferation index, medicine.drug_class, Ovary, Apoptosis, Levonorgestrel, Article, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, Fallopian Tube Neoplasms, Humans, Aged, Cell Proliferation, Ovarian Neoplasms, business.industry, Cancer, Middle Aged, medicine.disease, Prognosis, 030104 developmental biology, medicine.anatomical_structure, Contraceptive Agents, Hormonal, 030220 oncology & carcinogenesis, Fallopian tube cancer, Female, Ovarian cancer, business, Progestin, Fallopian tube, medicine.drug, Follow-Up Studies
Abstract: A large body of epidemiologic evidence has shown that use of progestin-containing preparations lowers ovarian cancer risk. The purpose of the current study was to gather further preclinical evidence supporting progestins as cancer chemopreventives by demonstrating progestin-activation of surrogate endpoint biomarkers pertinent to cancer prevention in the genital tract of women at increased risk of ovarian cancer. There were 64 women enrolled in a multi-institutional randomized trial who chose to undergo risk-reducing bilateral salpingo-oophorectomy (BSO) and to receive the progestin levonorgestrel or placebo for 4 to 6 weeks prior to undergoing BSO. The ovarian and fallopian tube epithelia (FTE) were compared immunohistochemically for effects of levonorgestrel on apoptosis (primary endpoint). Secondary endpoints included TGFβ isoform expression, proliferation, and karyometric features of nuclear abnormality. In both the ovary and fallopian tube, levonorgestrel did not confer significant changes in apoptosis or expression of the TGFβ1, 2, or 3 isoforms. In the ovarian epithelium, treatment with levonorgestrel significantly decreased the proliferation index. The mean ovarian Ki-67 value in the placebo arm was 2.027 per 100 cells versus 0.775 per 100 cells in the levonorgestrel arm (two-sided P value via Mann–Whitney U test = 0.0114). The karyometric signature of nuclei in both the ovarian and FTE deviated significantly from normal controls (women at average risk of ovarian cancer), but was significantly less abnormal in women treated with levonorgestrel. These karyometric data further support the idea that progestins may clear genetically abnormal cells and act as chemopreventive agents against ovarian and fallopian tube cancer.
Published: 2018

19. Factors Affecting Adherence to Topical Glaucoma Therapy: A Quantitative and Qualitative Pilot Study Analysis in Sydney, Australia

Author: Sascha K R, Spencer, Boaz, Shulruf, Zachary E, McPherson, Helen, Zhang, Mitchell B, Lee, Ian C, Francis, Allan, Bank, Minas T, Coroneo, and Ashish, Agar
Subjects: Adult, Aged, 80 and over, Male, Australia, Glaucoma, Pilot Projects, Middle Aged, Medication Adherence, Cross-Sectional Studies, Surveys and Questionnaires, Humans, Female, Prospective Studies, Drug Monitoring, Ophthalmic Solutions, Antihypertensive Agents, Intraocular Pressure, Aged
Abstract: To assess which factors in the lives and disease of patients with glaucoma affect their adherence to topical glaucoma therapy and the quantitative significance of this effect. To assess qualitatively the most influential barriers to adherence from the perspective of the patient.Multicenter, prospective, cross-sectional pilot study.A total of 145 patients, attending outpatient metropolitan glaucoma clinics in Sydney, Australia, who were prescribed topical glaucoma medications.A structured interview-based questionnaire was conducted with 145 individuals using glaucoma eye drops that had been prescribed at least 2 weeks previously. The questionnaire involved 2 novel questions on adherence, 29 questions on factors identified or postulated in the literature as affecting adherence for quantitative analysis, and 1 open-response question on patient-identified causes of nonadherence for qualitative analysis. This questionnaire represents the broadest coverage of factors hypothesized to affect adherence in a single study in the glaucoma medication adherence literature to date.Adherence rate, risk factors for poor adherence, and patient-identified barriers to adherence.In response to the question "How many days have you missed a drop in the last 2 weeks," 69.7% of patients reported total adherence. Four factors were significantly related to an increased likelihood of reporting having missed drops in the last 2 weeks. These were difficulty applying drops (odds ratio [OR], 2.35; 95% confidence interval [CI], 1.02-5.44; P0.05), a past or current diagnosis of depression (OR, 3.61; 95% CI, 1.53-8.52; P0.01), patient self-rating of own memory ≤ 7 of 10 (OR, 3.15; 95% CI, 1.36-7.30; P0.01), and self-reported motivation score ≤ 6 of 10 (OR, 10.94; 95% CI, 3.00-39.81; P0.01). Patient understanding of glaucoma, ethnicity, and socioeconomic status were among the 25 factors found not to have a statistically significant correlation with adherence.There is a significant proportion of patients taking their topical glaucoma medications less often than prescribed. Adherence to topical glaucoma therapies is negatively correlated to several factors: difficulty applying drops, a past or current diagnosis of depression, poor self-rating of own memory, and poor self-rating of own motivation. These may prove useful in designing interventions to improve adherence in these patients.
Published: 2018

20. Neurosarcoidosis in a public safety net hospital: a study of 82 cases

Author: James, Dorman, Lakshmi, Warrior, Vishal, Pandya, Ying, Sun, Jacob, Ninan, William, Trick, Helen, Zhang, and Bichun, Ouyang
Subjects: Adult, Male, Databases, Factual, Sarcoidosis, Hospitals, Public, Biopsy, Middle Aged, Prognosis, Magnetic Resonance Imaging, Young Adult, Phenotype, Adrenal Cortex Hormones, Central Nervous System Diseases, Predictive Value of Tests, Humans, Female, Radiography, Thoracic, Illinois, Safety-net Providers, Aged, Retrospective Studies
Abstract: To characterize clinical presentation, laboratory and imaging data, and treatment outcomes for neurosarcoidosis in an urban safety net hospital.The research database of Cook County Health and Hospitals system was queried for all cases of sarcoidosis from 2006 to 2013. These cases plus those identified through a survey of neurology faculty were reviewed and flagged if suspected to be neurosarcoidosis. Data were extracted in a standardized fashion, upon review by two experienced neurologists; patients were classified as definite, probable or possible neurosarcoidosis. Disagreements on classification were resolved by consensus conference.1706 cases of sarcoidosis were identified, with 82 (4.8%) classified as neurosarcoidosis. The cohort was predominantly African American (89%). Six were classified as definite, 34 as probable, and 42 as possible neurosarcoidosis. Neurosarcoidosis was the presenting symptom of sarcoidosis in 74% of cases. The most common presenting phenotype was myelopathy (21.7%), followed by optic nerve/chiasm involvement (16.0%) and epilepsy (11.3%). The facial nerve was involved in only 2% of cases. Chest x-ray showed abnormalities of sarcoidosis in 43.3% of cases, while chest CT did so in 78.6%. Corticosteroids were the initial treatment in 91% of cases, and outcomes were good in 53% of cases.Neurosarcoidosis remains a challenging diagnosis with the majority of patients without a previous diagnosis of systemic sarcoidosis. Chest imaging was supportive of the diagnosis in a majority of patients. Our cohort differs from others in the literature due to a low prevalence of facial nerve involvement. Prospective registry studies are needed.
Published: 2018

21. N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes

Author: Qike Li, Colleen Kenost, Haiquan Li, A. Grant Schissler, Ikbel Achour, Yves A. Lussier, Vincent Gardeux, Joanne Berghout, and Hao Helen Zhang
Subjects: 0301 basic medicine, lcsh:Internal medicine, lcsh:QH426-470, Wilcoxon signed-rank test, RNA-Seq, Biology, Bioinformatics, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Head and neck squamous cell carcinomas (HNSCCs), Genetics, Humans, Neoplasms, Squamous Cell, Precision Medicine, lcsh:RC31-1245, Mixture Model, Genetics (clinical), Mahalanobis distance, Receiver operating characteristic, Research, Gene Expression Profiling, Precision medicine, Human genetics, 3. Good health, lcsh:Genetics, 030104 developmental biology, ROC Curve, Head and Neck Neoplasms, 030220 oncology & carcinogenesis, N-of-1-pathways, DNA microarray, Single-Subject Analysis
Abstract: Background Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. Results We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. Conclusion The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care. Electronic supplementary material The online version of this article (doi:10.1186/s12920-017-0263-4) contains supplementary material, which is available to authorized users.
Published: 2017

22. kMEn: analyzing noisy and bidirectional transcriptional pathway responses in single subjects

Author: Colleen Kenost, Qike Li, Haiquan Li, Yves A. Lussier, Hao Helen Zhang, Ikbel Achour, A. Grant Schissler, Vincent Gardeux, and Joanne Berghout
Subjects: 0301 basic medicine, Non cancer, Health Informatics, Computational biology, Stimulus (physiology), Biology, Genome, Article, Transcriptome, Background noise, 03 medical and health sciences, 0302 clinical medicine, False positive paradox, Cluster Analysis, Humans, RNA, Messenger, Precision Medicine, Gene, Genetics, Mahalanobis distance, Gene Expression Profiling, 3. Good health, Computer Science Applications, 030104 developmental biology, 030220 oncology & carcinogenesis, Data Interpretation, Statistical
Abstract: Motivation Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs). Methods We propose a new N-of-1-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus. Results In ∼9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: (i) improved precision-recall at various levels of bidirectional response and (ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment-specific pathways identified by kMEn correlate with therapeutic response (p-value Conclusion Through improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers.
Published: 2016

23. Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions

Author: C. Kent Kwoh, Wei-Hsuan Lo-Ciganic, Hao Helen Zhang, Julie M. Donohue, James L. Huang, Yonghui Wu, Adam J. Gordon, Gerald Cochran, Daniel C. Malone, Walid F. Gellad, Jeremy C. Weiss, and Courtney C. Kuza
Subjects: Male, Youden's J statistic, Medicare, Logistic regression, Risk Assessment, Cohort Studies, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Substance Use and Addiction, Humans, Medicine, Medical Informatics Applications, 030212 general & internal medicine, Medical prescription, Original Investigation, Aged, Aged, 80 and over, business.industry, Research, Medicare beneficiary, Correction, Opioid overdose, General Medicine, Emergency department, Middle Aged, medicine.disease, United States, 3. Good health, Analgesics, Opioid, Online Only, Prescriptions, Opioid, Cohort, Female, Other, Drug Overdose, business, Algorithm, Algorithms, 030217 neurology & neurosurgery, medicine.drug
Abstract: Key Points Question Can machine-learning approaches predict opioid overdose risk among fee-for-service Medicare beneficiaries? Findings In this prognostic study of the administrative claims data of 560 057 Medicare beneficiaries, the deep neural network and gradient boosting machine models outperformed other methods for identifying risk, although positive predictive values were low given the low prevalence of overdose episodes. Meaning Machine-learning algorithms using administrative data appear to be a valuable and feasible tool for more accurate identification of opioid overdose risk., Importance Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. Objective To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription. Design, Setting, and Participants A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples. Exposures Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation. Main Outcomes and Measures Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator–type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity. Results Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2% [142 180] of the cohort), medium-risk (18.6% [34 579] of the cohort), and high-risk (5.2% [9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome. Conclusions and Relevance Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose., This prognostic study evaluates the use of machine-learning methods, with prescription drug and claims data, in detecting opioid overdose risk in Medicare beneficiaries with at least 1 opioid prescription.
Published: 2019

24. Laforin Prevents Stress-Induced Polyglucosan Body Formation and Lafora Disease Progression in Neurons

Author: Berge A. Minassian, Yin Wang, Helen Zhang, Keli Ma, Pan Zheng, Peixiang Wang, Yan Liu, Jack M. Parent, Otto Baba, and Yang Liu
Subjects: Neuroscience (miscellaneous), Apoptosis, Biology, Neuroprotection, Article, Lafora disease, Mice, Cellular and Molecular Neuroscience, Glycogen phosphorylase, chemistry.chemical_compound, Allosteric Regulation, medicine, Animals, Humans, Phosphorylation, Glycogen synthase, Glucans, Inclusion Bodies, Mice, Knockout, Neurons, Glycogen, Hydrolysis, Glycogen Phosphorylase, Neurodegeneration, Hep G2 Cells, Endoplasmic Reticulum Stress, Protein Tyrosine Phosphatases, Non-Receptor, medicine.disease, Enzyme Activation, Mice, Inbred C57BL, Glycogen Synthase, HEK293 Cells, Lafora Disease, Neurology, Biochemistry, chemistry, Gene Knockdown Techniques, Nerve Degeneration, Disease Progression, Unfolded protein response, biology.protein, Dual-Specificity Phosphatases, alpha-Amylases, Laforin
Abstract: Glycogen, the largest cytosolic macromolecule, is soluble because of intricate construction generating perfect hydrophilic-surfaced spheres. Little is known about neuronal glycogen function and metabolism, though progress is accruing through the neurodegenerative epilepsy Lafora disease (LD) proteins laforin and malin. Neurons in LD exhibit Lafora bodies (LBs), large accumulations of malconstructed insoluble glycogen (polyglucosans). We demonstrated that the laforin-malin complex reduces LBs and protects neuronal cells against endoplasmic reticulum stress-induced apoptosis. We now show that stress induces polyglucosan formation in normal neurons in culture and in the brain. This is mediated by increased glucose-6-phosphate allosterically hyperactivating muscle glycogen synthase (GS1) and is followed by activation of the glycogen digesting enzyme glycogen phosphorylase. In the absence of laforin, stress-induced polyglucosans are undigested and accumulate into massive LBs, and in laforin-deficient mice, stress drastically accelerates LB accumulation and LD. The mechanism through which laforin-malin mediates polyglucosan degradation remains unclear but involves GS1 dephosphorylation by laforin. Our work uncovers the presence of rapid polyglucosan metabolism as part of the normal physiology of neuroprotection. We propose that deficiency in the degradative phase of this metabolism, leading to LB accumulation and resultant seizure predisposition and neurodegeneration, underlies LD.
Published: 2013

25. Improving performance of nanoscale ultrasound contrast agents using N,N-diethylacrylamide stabilization

Author: Pubudu M. Peiris, Christopher Hernandez, Hanping Wu, Alan Burke, Agata A. Exner, Reshani Perera, and Helen Zhang
Subjects: Materials science, Polymers, Biomedical Engineering, Pharmaceutical Science, Medicine (miscellaneous), Nanoparticle, Contrast Media, Bioengineering, Nanotechnology, 02 engineering and technology, 010402 general chemistry, 01 natural sciences, Article, Mice, Drug Delivery Systems, Pulmonary surfactant, Cell Line, Tumor, Animals, Humans, General Materials Science, Lipid bilayer, Ultrasonography, Acrylamides, Microbubbles, business.industry, Ultrasound, Echogenicity, 021001 nanoscience & nanotechnology, Xenograft Model Antitumor Assays, 0104 chemical sciences, Drug delivery, Molecular Medicine, Nanoparticles, Molecular imaging, 0210 nano-technology, business, Colorectal Neoplasms, Biomedical engineering
Abstract: The design of nanoscale yet highly echogenic agents for imaging outside of the vasculature and for ultrasound-mediated drug delivery remains a formidable challenge. We have previously reported on formulation of echogenic perfluoropropane gas nanobubbles stabilized by a lipid-Pluronic surfactant shell. In the current work we describe the development of a new generation of these nanoparticles which consist of perfluoropropane gas stabilized by a surfactant and lipid membrane and a crosslinked network of N, N-diethylacrylamide. The resulting crosslinked nanobubbles (CL-PEG-NB) were 95.2 ± 25.2 nm in diameter and showed significant improvement in stability and retention of echogenic signal over 24 h. In vivo analysis via ultrasound and fluorescence mediated tomography showed greater tumor extravasation and accumulation with CL-PEG-NB compared to microbubbles. Together these results demonstrate the capabilities and advantages of a new, more stable, nanometer-scale ultrasound contrast agent that can be utilized in future work for diagnostic scans and molecular imaging.
Published: 2016

26. Evaluating IPMN and pancreatic carcinoma utilizing quantitative histopathology

Author: Michael Yozwiak, Joseph C. Watkins, Evan S. Glazer, Kimberly A. Hill, Hao Helen Zhang, Charmi Patel, David S. Alberts, Stephanie T. Kha, Hubert G. Bartels, Robert S. Krouse, and Nellie N. Nafissi
Subjects: Adult, Male, Cancer Research, medicine.medical_specialty, quantitative histopathology, endocrine system diseases, Library science, Quantitative histopathology, Intraductal papillary mucinous neoplasms, Diagnosis, Differential, 03 medical and health sciences, 0302 clinical medicine, Medicine, Humans, Radiology, Nuclear Medicine and imaging, Pancreatic carcinoma, nuclear chromatin pattern, Aged, Original Research, Aged, 80 and over, pancreatic carcinoma, business.industry, Clinical Cancer Research, Middle Aged, Adenocarcinoma, Mucinous, Carcinoma, Papillary, 3. Good health, Surgery, Pancreatic Neoplasms, Oncology, 030220 oncology & carcinogenesis, karyometry, 030211 gastroenterology & hepatology, Female, business, Carcinoma, Pancreatic Ductal
Abstract: Intraductal papillary mucinous neoplasms (IPMN) are pancreatic lesions with uncertain biologic behavior. This study sought objective, accurate prediction tools, through the use of quantitative histopathological signatures of nuclear images, for classifying lesions as chronic pancreatitis (CP), IPMN, or pancreatic carcinoma (PC). Forty‐four pancreatic resection patients were retrospectively identified for this study (12 CP; 16 IPMN; 16 PC). Regularized multinomial regression quantitatively classified each specimen as CP, IPMN, or PC in an automated, blinded fashion. Classification certainty was determined by subtracting the smallest classification probability from the largest probability (of the three groups). The certainty function varied from 1.0 (perfectly classified) to 0.0 (random). From each lesion, 180 ± 22 nuclei were imaged. Overall classification accuracy was 89.6% with six unique nuclear features. No CP cases were misclassified, 1/16 IPMN cases were misclassified, and 4/16 PC cases were misclassified. Certainty function was 0.75 ± 0.16 for correctly classified lesions and 0.47 ± 0.10 for incorrectly classified lesions (P = 0.0005). Uncertainty was identified in four of the five misclassified lesions. Quantitative histopathology provides a robust, novel method to distinguish among CP, IPMN, and PC with a quantitative measure of uncertainty. This may be useful when there is uncertainty in diagnosis.
Published: 2016

27. Variable selection for covariate-adjusted semiparametric inference in randomized clinical trials

Author: Marie Davidian, Hao Helen Zhang, and Shuai Yuan
Subjects: Statistics and Probability, Selection bias, Analysis of covariance, Models, Statistical, Epidemiology, media_common.quotation_subject, Inference, Context (language use), Feature selection, Statistics, Nonparametric, Article, Standard error, Research Design, Covariate, Statistics, Econometrics, Humans, Algorithms, Selection Bias, Selection (genetic algorithm), Randomized Controlled Trials as Topic, media_common, Mathematics
Abstract: Extensive baseline covariate information is routinely collected on participants in randomized clinical trials, and it is well recognized that a proper covariate-adjusted analysis can improve the efficiency of inference on the treatment effect. However, such covariate adjustment has engendered considerable controversy, as post hoc selection of covariates may involve subjectivity and may lead to biased inference, whereas prior specification of the adjustment may exclude important variables from consideration. Accordingly, how to select covariates objectively to gain maximal efficiency is of broad interest. We propose and study the use of modern variable selection methods for this purpose in the context of a semiparametric framework, under which variable selection in modeling the relationship between outcome and covariates is separated from estimation of the treatment effect, circumventing the potential for selection bias associated with standard analysis of covariance methods. We demonstrate that such objective variable selection techniques combined with this framework can identify key variables and lead to unbiased and efficient inference on the treatment effect. A critical issue in finite samples is validity of estimators of uncertainty, such as standard errors and confidence intervals for the treatment effect. We propose an approach to estimation of sampling variation of estimated treatment effect and show its superior performance relative to that of existing methods. Copyright © 2012 John Wiley & Sons, Ltd.
Published: 2012

28. Role of LPA4/p2y9/GPR23 in Negative Regulation of Cell Motility

Author: Jinhua Wu, Helen Zhang, Jolene J. Windle, Zendra Lee, Xianjun Fang, Mark A. Subler, Ching-Kang Chen, Ching-Ting Cheng, and Abir Mukherjee
Subjects: Male, Biology, Endothelial cell differentiation, Mice, chemistry.chemical_compound, Cell Movement, Cell Line, Tumor, Lysophosphatidic acid, Animals, Humans, Protein Isoforms, Neoplasm Invasiveness, Tissue Distribution, Receptors, Lysophosphatidic Acid, Receptor, Molecular Biology, Protein kinase B, Cells, Cultured, Mice, Knockout, Purinergic receptor, Receptors, Purinergic, Cell migration, Articles, Cell Biology, Fibroblasts, rac GTP-Binding Proteins, Cell biology, Enzyme Activation, Rac GTP-Binding Proteins, chemistry, Colonic Neoplasms, Female, lipids (amino acids, peptides, and proteins), Lysophospholipids, biological phenomena, cell phenomena, and immunity, Signal transduction, Proto-Oncogene Proteins c-akt, Signal Transduction
Abstract: Lysophosphatidic acid (LPA) is a ligand of multiple G protein–coupled receptors. The LPA1–3receptors are members of the endothelial cell differentiation gene (Edg) family. LPA4/p2y9/GPR23, a member of the purinergic receptor family, and recently identified LPA5/GPR92 and p2y5 are structurally distant from the canonical Edg LPA receptors. Here we report targeted disruption of lpa4in mice. Although LPA4-deficient mice displayed no apparent abnormalities, LPA4-deficient mouse embryonic fibroblasts (MEFs) were hypersensitive to LPA-induced cell migration. Consistent with negative modulation of the phosphatidylinositol 3 kinase pathway by LPA4, LPA4deficiency potentiated Akt and Rac but decreased Rho activation induced by LPA. Reconstitution of LPA4converted LPA4-negative cells into a less motile phenotype. In support of the biological relevance of these observations, ectopic expression of LPA4strongly inhibited migration and invasion of human cancer cells. When coexpressed with LPA1in B103 neuroblastoma cells devoid of endogenous LPA receptors, LPA4attenuated LPA1-driven migration and invasion, indicating functional antagonism between the two subtypes of LPA receptors. These results provide genetic and biochemical evidence that LPA4is a suppressor of LPA-dependent cell migration and invasion in contrast to the motility-stimulating Edg LPA receptors.
Published: 2008

29. Sparse meta-analysis with high-dimensional data

Author: Christy L. Avery, Qianchuan He, Danyu Lin, and Hao Helen Zhang
Subjects: 0301 basic medicine, Statistics and Probability, Feature selection, Machine learning, computer.software_genre, 01 natural sciences, 010104 statistics & probability, 03 medical and health sciences, Meta-Analysis as Topic, Consistency (statistics), Covariate, Humans, Computer Simulation, 0101 mathematics, Selection (genetic algorithm), Mathematics, Models, Statistical, business.industry, Covariance matrix, Estimator, General Medicine, Genomics, Articles, Covariance, Random effects model, 030104 developmental biology, Data Interpretation, Statistical, Artificial intelligence, Statistics, Probability and Uncertainty, business, computer, Genome-Wide Association Study
Abstract: Meta-analysis plays an important role in summarizing and synthesizing scientific evidence derived from multiple studies. With high-dimensional data, the incorporation of variable selection into meta-analysis improves model interpretation and prediction. Existing variable selection methods require direct access to raw data, which may not be available in practical situations. We propose a new approach, sparse meta-analysis (SMA), in which variable selection for meta-analysis is based solely on summary statistics and the effect sizes of each covariate are allowed to vary among studies. We show that the SMA enjoys the oracle property if the estimated covariance matrix of the parameter estimators from each study is available. We also show that our approach achieves selection consistency and estimation consistency even when summary statistics include only the variance estimators or no variance/covariance information at all. Simulation studies and applications to high-throughput genomics studies demonstrate the usefulness of our approach.
Published: 2015

30. Optometry services in Ontario: supply - and demand-side factors from 2011 to 2036

Author: Philip Sj, Leonard, Arthur, Sweetman, and Xue Helen, Zhang
Subjects: Adult, Male, Ontario, Workforce, Humans, Female, Middle Aged, Health Services Accessibility, Aged, Optometry
Abstract: Optometric labour market projections are provided. First, population growth and ageing-based estimates of the rate of increase of eye-care services in Ontario from 2011 to 2$ are presented, holding the age-sex structure of utilization constant. Then, using data on the 2011 supply and working hours of Ontario's optometrists, the number of optometrists needed to keep the level of optometric services per age-sex-adjusted person comparable over time is estimated. The projections suggest that the number of Ontario optometrists should grow by approximately 30-40 full-time equivalents per year; to offset retirements and account for decreasing work hours, this suggests 77-90 new practitioners are required each year. However, in recent years, the number of Ontario optometrists has been growing faster than this, suggesting either that demand has exceeded supply and/or surpluses will accumulate if this trend continues.
Published: 2014

31. Gene selection using support vector machines with non-convex penalty

Author: Xiaodong Lin, Cheolwoo Park, Jeongyoun Ahn, and Hao Helen Zhang
Subjects: Statistics and Probability, Time Factors, Optimization problem, computer.software_genre, Biochemistry, Pattern Recognition, Automated, Quadratic equation, Neoplasms, Databases, Genetic, Hinge loss, Cluster Analysis, Humans, Molecular Biology, Oligonucleotide Array Sequence Analysis, Mathematics, Models, Statistical, Models, Genetic, Bayes Theorem, Sequence Analysis, DNA, Thresholding, Computer Science Applications, Support vector machine, Computational Mathematics, ComputingMethodologies_PATTERNRECOGNITION, Computational Theory and Mathematics, Sample size determination, Data mining, DNA microarray, Classifier (UML), computer, Algorithms, Software
Abstract: Motivation: With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes simultaneously in one single experiment. One current difficulty in interpreting microarray data comes from their innate nature of ‘high-dimensional low sample size’. Therefore, robust and accurate gene selection methods are required to identify differentially expressed group of genes across different samples, e.g. between cancerous and normal cells. Successful gene selection will help to classify different cancer types, lead to a better understanding of genetic signatures in cancers and improve treatment strategies. Although gene selection and cancer classification are two closely related problems, most existing approaches handle them separately by selecting genes prior to classification. We provide a unified procedure for simultaneous gene selection and cancer classification, achieving high accuracy in both aspects. Results: In this paper we develop a novel type of regularization in support vector machines (SVMs) to identify important genes for cancer classification. A special nonconvex penalty, called the smoothly clipped absolute deviation penalty, is imposed on the hinge loss function in the SVM. By systematically thresholding small estimates to zeros, the new procedure eliminates redundant genes automatically and yields a compact and accurate classifier. A successive quadratic algorithm is proposed to convert the non-differentiable and non-convex optimization problem into easily solved linear equation systems. The method is applied to two real datasets and has produced very promising results. Availability: MATLAB codes are available upon request from the authors. Contact: hzhang@stat.ncsu.edu Supplementary information
Published: 2005

32. Two Different Mechanisms of Disinhibition Produced by GABAAReceptor Mutations Linked to Epilepsy in Humans

Author: Helen Zhang, Matt T. Bianchi, Robert L. Macdonald, and Luyan Song
Subjects: medicine.medical_specialty, medicine.drug_class, Mutant, Xenopus, Gating, Biology, GABAA-rho receptor, Internal medicine, medicine, Humans, Genetic Predisposition to Disease, ARTICLE, GABA Modulators, Receptor, Cells, Cultured, gamma-Aminobutyric Acid, Benzodiazepine, Diazepam, Epilepsy, Dose-Response Relationship, Drug, GABAA receptor, General Neuroscience, Electric Conductivity, Long-term potentiation, Receptors, GABA-A, biology.organism_classification, Kinetics, Protein Subunits, Endocrinology, Mutation
Abstract: The first mutations of the GABA(A) receptor channel linked to familial epilepsy in humans were reported recently (Baulac et al., 2001; Wallace et al., 2001). Preliminary functional analysis of alpha1beta2gamma2 GABA(A) receptors expressed in Xenopus oocytes suggested that the gamma2 subunit R43Q mutation abolished current enhancement by the benzodiazepine, diazepam, and that the gamma2 subunit K289M mutation decreased current amplitudes. We used single-channel recording and concentration jump techniques applied to outside out patches to evaluate the impact of these mutations on GABA(A) receptor channel function of the highly conserved rat ortholog subunits expressed in human embryonic kidney cells. When coexpressed with alpha1 and beta3 subunits, no differences were observed between wild-type and mutant GABA(A) receptor current activation rates or rates or extent of desensitization during prolonged (400 msec) GABA application (1 mm). Although deactivation after brief (5 msec) or prolonged (400 msec) GABA application was unaltered by the R43Q mutation, deactivation (a correlate of IPSC duration) was accelerated for the K289M mutation. Faster deactivation was likely a consequence of altered gating, because single-channel openings had shorter mean duration. Interestingly, the R43Q mutation did not alter diazepam potentiation. It did, however, substantially decrease current amplitude, which was not caused by decreased single-channel conductance or open time, suggesting reduced surface expression of functional receptors. The two gamma2 subunit mutations likely produce disinhibition and familial epilepsy by distinct mechanisms, suggesting that maintenance of neuronal inhibition depends not only on the peak amplitude of IPSCs, but also on their time course.
Published: 2002

33. On optimal treatment regimes selection for mean survival time

Author: Yuan, Geng, Hao Helen, Zhang, and Wenbin, Lu
Subjects: Acquired Immunodeficiency Syndrome, Clinical Trials as Topic, Models, Statistical, Anti-HIV Agents, Kaplan-Meier Estimate, Biostatistics, Statistics, Nonparametric, Article, Survival Rate, Humans, Regression Analysis, Computer Simulation, Least-Squares Analysis, Precision Medicine
Abstract: In clinical studies with time-to-event as a primary endpoint, one main interest is to find the best treatment strategy to maximize patients’ mean survival time. Due to patient’s heterogeneity in response to treatments, great efforts have been devoted to developing optimal treatment regimes by integrating individuals’ clinical and genetic information. A main challenge arises in the selection of important variables that can help to build reliable and interpretable optimal treatment regimes since the dimension of predictors may be high. In this paper, we propose a robust loss-based estimation framework that can be easily coupled with shrinkage penalties for both estimation of optimal treatment regimes and variable selection. The asymptotic properties of the proposed estimators are studied. Moreover, a model-free estimator of restricted mean survival time under the derived optimal treatment regime is developed and its asymptotic property is studied. Simulations are conducted to assess the empirical performance of the proposed method for parameter estimation, variable selection, and optimal treatment decision. An application to an AIDS clinical trial data set is given to illustrate the method.
Published: 2013

34. Baseline prevalence and type distribution of human papillomavirus in healthy Chinese women aged 18-25 years enrolled in a clinical trial

Author: Fang-Hui, Zhao, Feng-Cai, Zhu, Wen, Chen, Juan, Li, Yue-Mei, Hu, Ying, Hong, Yi-Ju, Zhang, Qin-Jing, Pan, Jia-Hong, Zhu, Xun, Zhang, Yong, Chen, Haiwen, Tang, Helen, Zhang, Christelle, Durand, Sanjoy K, Datta, Frank, Struyf, and Dan, Bi
Subjects: Adult, type distribution, China, Adolescent, prevalence, Uterine Cervical Neoplasms, Enzyme-Linked Immunosorbent Assay, Cervix Uteri, Polymerase Chain Reaction, Young Adult, Clinical Trials, Phase II as Topic, Double-Blind Method, Humans, Papillomavirus Vaccines, Prospective Studies, human papillomavirus, Papillomaviridae, Neoplasm Staging, Randomized Controlled Trials as Topic, Vaginal Smears, Papillomavirus Infections, virus diseases, Prognosis, Uterine Cervical Dysplasia, female genital diseases and pregnancy complications, Infectious Causes of Cancer, Clinical Trials, Phase III as Topic, DNA, Viral, Women's Health, Female, women, Follow-Up Studies
Abstract: Baseline human papillomavirus (HPV) prevalence and type distribution were evaluated in young Chinese women enrolled in a clinical trial of an HPV vaccine (ClinicalTrials.gov registration NCT00779766). Cervical specimens and blood samples were collected at baseline from women aged 18–25 years (n = 6,051) from four sites across Jiangsu province. Cervical specimens were tested for HPV DNA by SPF10 PCR-DEIA-LiPA25 version 1, and HPV-16/18 type-specific polymerase chain reaction. Anti-HPV-16 and anti-HPV-18 antibody titres were quantified by enzyme-linked immunosorbent assay. At baseline, 15.3% of women were DNA positive for any of 14 HPV high-risk (hr) types (HPV-16/18/31/33/35/39/45/51/52/56/58/59/66/68). The most commonly detected hrHPV types in cervical specimens were HPV-52 (4.0%) and HPV-16 (3.7%). High-risk HPV DNA-positivity increased with severity of cytological abnormalities: 39.3% in atypical squamous cells of undetermined significance, 85.0% in low-grade squamous intraepithelial lesions and 97.8% in high-grade squamous intraepithelial lesions (HSIL). The hrHPV types most frequently detected in HSIL were HPV-16 (63.0%), HPV-18 (17.4%), HPV-52 (17.4%), HPV-58 (15.2%) and HPV-33 (15.2%). The hrHPV types most frequently detected in cervical intraepithelial neoplasia 2+ were HPV-16 (66.1%), HPV-33 (16.1%), HPV-52 (16.1%), HPV-58 (14.5%) and HPV-51 (11.3%). Multiple hrHPV infections were reported for 24.4% of hrHPV DNA positive women. Regardless of baseline HPV DNA status, 30.5% and 16.0% of subjects were initially seropositive for anti-HPV-16 and anti-HPV-18, respectively. In conclusion, the high baseline seropositivity rate and intermediate prevalence of cervical hrHPV types in Chinese women aged 18–25 years underlines the importance of early HPV vaccination in this population. What's new? In China, cervical cancer is the second most frequent cancer among women aged 15–44 years. The authors collected baseline data on prevalence and type distribution of human papillomavirus (HPV) from more than 6,000 healthy Chinese women aged 18–25 years participating in a large vaccine efficacy trial. Regardless of cytology, 15.3% of women were positive for high-risk HPV types, with HPV-52 (4.0%), HPV-16 (3.7%), HPV-51 (1.7%) and HPV-58 (1.5%) being the most frequently detected. This high baseline prevalence of high-risk HPV types underscores the importance of early vaccination among Chinese women.
Published: 2013

35. Syringocystadenocarcinoma papilliferum with transition to areas of squamous differentiation: a case report and review of the literature

Author: Carlos A. Torres-Cabala, Wei Lien Wang, Yu Helen Zhang, Ronald P. Rapini, Victor G. Prieto, and Jonathan L. Curry
Subjects: Male, Pathology, medicine.medical_specialty, Squamous Differentiation, Biopsy, Dermatology, Pathology and Forensic Medicine, Carcinoembryonic antigen, medicine, Biomarkers, Tumor, Humans, Neoplasm Invasiveness, Cystadenocarcinoma, Aged, Aged, 80 and over, biology, medicine.diagnostic_test, business.industry, Nodule (medicine), Cell Differentiation, General Medicine, Middle Aged, medicine.disease, Immunohistochemistry, Treatment Outcome, biology.protein, Cystadenocarcinoma, Papillary, Female, Neoplasms, Adnexal and Skin Appendage, medicine.symptom, business, Syringocystadenoma papilliferum, Adnexal Carcinoma
Abstract: Syringocystadenocarcinoma papilliferum (SCACP) is an exceedingly rare cutaneous adnexal neoplasm, which is typically located in the head and neck, and perianal area. Very few cases have been reported in the literature. Here, we report a case of SCACP with evident transition to squamous differentiation. A 75-year-old white woman presented with 1-year history of a solitary tender nodule in the left upper arm. Physical examination revealed a single, 1.5 × 1.1-cm, erythematous ulcerated nodule within a background of red patch. Biopsy showed an adnexal carcinoma connected to the epidermis and composed of cystic papillary projections admixed with solid basaloid areas with marked cytologic atypia. The basaloid tumor cells appeared to blend with the squamous component that demonstrated ductal formation, which was highlighted by carcinoembryonic antigen. Tumor cells were reactive for both cytokeratins 5/6 and 7. This case represents SCACP arising from syringocystadenoma papilliferum in the upper arm, with distinct transition to areas of squamous differentiation.
Published: 2012

36. Cell-autonomous inactivation of the reelin pathway impairs adult neurogenesis in the hippocampus

Author: Amelia J. Eisch, Isabel Reillo, Víctor Borrell, Cátia M. Teixeira, Lluís Pujadas, Carles Bosch, Lucas Brunso, Xin Duan, Jack M. Parent, Albert Martínez, Hongjun Song, Michelle M. Kron, Helen Zhang, Brian W. Howell, Diane C. Lagace, Nuria Masachs, and Eduardo Soriano
Subjects: Male, Genetically modified mouse, Aging, Cell Adhesion Molecules, Neuronal, Neurogenesis, Mice, Transgenic, Nerve Tissue Proteins, Hippocampal formation, Hippocampus, Article, Cell Line, Rats, Sprague-Dawley, Mice, Animals, Humans, Gene Silencing, Reelin, Cells, Cultured, Extracellular Matrix Proteins, biology, General Neuroscience, Serine Endopeptidases, Age Factors, Signal transducing adaptor protein, DAB1, Rats, Mice, Inbred C57BL, Reelin Protein, nervous system, biology.protein, Phosphorylation, Signal transduction, Neuroscience, Signal Transduction
Abstract: Adult hippocampal neurogenesis is thought to be essential for learning and memory, and has been implicated in the pathogenesis of several disorders. Although recent studies have identified key factors regulating neuroprogenitor proliferation in the adult hippocampus, the mechanisms that control the migration and integration of adult-born neurons into circuits are largely unknown. Reelin is an extracellular matrix protein that is vital for neuronal development. Activation of the Reelin cascade leads to phosphorylation of Disabled-1, an adaptor protein required for Reelin signaling. Here we used transgenic mouse and retroviral reporters along with Reelin signaling gain-of-function and loss-of-function studies to show that the Reelin pathway regulates migration and dendritic development of adultgenerated hippocampal neurons. Whereas overexpression of Reelin accelerated dendritic maturation, inactivation of the Reelin signaling pathway specifically in adult neuroprogenitor cells resulted in aberrant migration, decreased dendrite development, formation of ectopic dendrites in the hilus, and the establishment of aberrant circuits. Our findings support a cell-autonomous and critical role for the Reelin pathway in regulating dendritic development and the integration of adult-generated granule cells and point to this pathway as a key regulator of adult neurogenesis. Moreover, our data reveal a novel role of the Reelin cascade in adult brain function with potential implications for the pathogenesis of several neurological and psychiatric disorders. © 2012 the authors., This project was supported by Grant BFU2008-3980 from the Ministerio de Ciencia e Innovación (MICINN), Spain; by grants from the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED) and Caixa Catalunya-Obra Social Foundations to E.S.; by grants from the Spanish Ministry of Science and Innovation (SAF2009-07367 and CONSOLIDER CSD2007-00023) to V.B.; by the Fred Annegers Fellowship from the Epilepsy Foundation (M.M.K.); and by NIH Grant NS058585 to J.M.P. I.R. was recipient of a Formación de Personal Universitario predoctoral fellowship from MINECO (Spain).
Published: 2012

37. Variable Selection for Optimal Treatment Decision

Author: Hao Helen Zhang, Wenbin Lu, and Donglin Zeng
Subjects: Statistics and Probability, Male, Epidemiology, business.industry, Computer science, Decision Making, Estimator, Feature selection, Function (mathematics), Decision rule, Models, Theoretical, Machine learning, computer.software_genre, Article, Software, Health Information Management, Robustness (computer science), Statistical inference, Humans, Female, Artificial intelligence, Precision Medicine, business, computer, Interpretability
Abstract: In decision-making on optimal treatment strategies, it is of great importance to identify variables that are involved in the decision rule, i.e. those interacting with the treatment. Effective variable selection helps to improve the prediction accuracy and enhance the interpretability of the decision rule. We propose a new penalized regression framework which can simultaneously estimate the optimal treatment strategy and identify important variables. The advantages of the new approach include: (i) it does not require the estimation of the baseline mean function of the response, which greatly improves the robustness of the estimator; (ii) the convenient loss-based framework makes it easier to adopt shrinkage methods for variable selection, which greatly facilitates implementation and statistical inferences for the estimator. The new procedure can be easily implemented by existing state-of-art software packages like LARS. Theoretical properties of the new estimator are studied. Its empirical performance is evaluated using simulation studies and further illustrated with an application to an AIDS clinical trial.
Published: 2011

38. eQTL networks unveil enriched mRNA master integrators downstream of complex disease-associated SNPs

Author: Haiquan Li, Yves A. Lussier, Vincent Gardeux, Ikbel Achour, Fernando D. Martinez, Qike Li, Hao Helen Zhang, Jianrong Li, Joe G. N. Garcia, and Nima Pouladi
Subjects: Linkage disequilibrium, mRNA, Quantitative Trait Loci, Single-nucleotide polymorphism, Genome-wide association study, Genomics, Health Informatics, Quantitative trait locus, Biology, eQTL, Polymorphism, Single Nucleotide, Article, Master integrator, Computational biology, Big data, Single Nucleotide Polymorphism (SNP), Genetics, Humans, Centrality, Signal integration, RNA, Messenger, Complex disease, Computational medicine, Genetic association, Genetic architecture, Computer Science Applications, Computational genomics, Expression quantitative trait loci, Systems medicine, Network biology, Translational bioinformatics, Transcriptome, Systems biology, Complex diseases
Abstract: Display Omitted We study the downstream transcriptome systems properties of 6432 SNPs of 574 complex diseases.eQTL network analyses unveil that downstream mRNAs are targeted by an excess number of SNPs.Thus multiple complex diseases associated SNPs tend to target the same group of mRNAs.These mRNA hubs display a novel systems property: master integrators.Our findings provide novel insights into the pleiotropy of mRNAs targeted by complex disease polymorphisms. The causal and interplay mechanisms of Single Nucleotide Polymorphisms (SNPs) associated with complex diseases (complex disease SNPs) investigated in genome-wide association studies (GWAS) at the transcriptional level (mRNA) are poorly understood despite recent advancements such as discoveries reported in the Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTex). Protein interaction network analyses have successfully improved our understanding of both single gene diseases (Mendelian diseases) and complex diseases. Whether the mRNAs downstream of complex disease genes are central or peripheral in the genetic information flow relating DNA to mRNA remains unclear and may be disease-specific. Using expression Quantitative Trait Loci (eQTL) that provide DNA to mRNA associations and network centrality metrics, we hypothesize that we can unveil the systems properties of information flow between SNPs and the transcriptomes of complex diseases. We compare different conditions such as nave SNP assignments and stringent linkage disequilibrium (LD) free assignments for transcripts to remove confounders from LD. Additionally, we compare the results from eQTL networks between lymphoblastoid cell lines and liver tissue. Empirical permutation resampling (p
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