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1. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H. House, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F. Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G. Scott, Mauricio Tec, Ajitesh Srivastava, Glover E. George, Jeffrey C. Cegan, Ian D. Dettwiller, William P. England, Matthew W. Farthing, Robert H. Hunter, Brandon Lafferty, Igor Linkov, Michael L. Mayo, Matthew D. Parno, Michael A. Rowland, Benjamin D. Trump, Yanli Zhang-James, Samuel Chen, Stephen V. Faraone, Jonathan Hess, Christopher P. Morley, Asif Salekin, Dongliang Wang, Sabrina M. Corsetti, Thomas M. Baer, Marisa C. Eisenberg, Karl Falb, Yitao Huang, Emily T. Martin, Ella McCauley, Robert L. Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, V. P. Nagraj, Stephanie L. Guertin, Christopher Hulme-Lowe, Stephen D. Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A. Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A. Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C. Lee, Juan Dent, Kyra H. Grantz, Alison L. Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T. Keegan, Stephen A. Lauer, Joseph C. Lemaitre, Justin Lessler, Hannah R. Meredith, Javier Perez-Saez, Sam Shah, Claire P. Smith, Shaun A. Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C. Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C. Reiner, Ryan Barber, Emmanuela Gakidou, Simon I. Hay, Steve Lim, Chris Murray, David Pigott, Heidi L. Gurung, Prasith Baccam, Steven A. Stage, Bradley T. Suchoski, B. Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O. Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T. Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I. Bosse, Sebastian Funk, Joel Hellewell, Sophie R. Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K. Yamana, Sen Pei, Jeffrey Shaman, Michael L. Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J. Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J. Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B. O’Dea, John M. Drake, Robert Pagano, Quoc T. Tran, Lam Si Tung Ho, Huong Huynh, Jo W. Walker, Rachel B. Slayton, Michael A. Johansson, Matthew Biggerstaff, Nicholas G. Reich, Cramer, Estee Y [0000-0003-1373-3177], Ray, Evan L [0000-0003-4035-0243], Lopez, Velma K [0000-0003-2926-4010], Bracher, Johannes [0000-0002-3777-1410], Gneiting, Tilmann [0000-0001-9397-3271], Niemi, Jarad [0000-0002-5079-158X], White, Jerome [0000-0003-4148-8834], Woody, Spencer [0000-0002-2882-3450], Fox, Spencer [0000-0003-1969-3778], Gaither, Kelly [0000-0002-4272-175X], Meyers, Lauren Ancel [0000-0002-5828-8874], Tec, Mauricio [0000-0002-1853-5842], George, Glover E [0000-0003-4779-8702], Cegan, Jeffrey C [0000-0002-3065-3403], Hunter, Robert H [0000-0002-2382-7938], Lafferty, Brandon [0000-0002-2618-3787], Mayo, Michael L [0000-0001-9014-1859], Rowland, Michael A [0000-0002-6759-8225], Chen, Samuel [0000-0002-1070-9801], Salekin, Asif [0000-0002-0807-8967], Corsetti, Sabrina M [0000-0003-2216-2492], Falb, Karl [0000-0002-3465-3988], Huang, Yitao [0000-0001-7846-2174], Sheldon, Daniel [0000-0002-4257-2432], Guo, Lihong [0000-0003-4804-4005], Gu, Quanquan [0000-0001-9830-793X], Xu, Pan [0000-0002-2559-8622], Lega, Joceline [0000-0003-2064-229X], McConnell, Steve [0000-0002-0294-3737], Turner, Stephen D [0000-0001-9140-9028], Shi, Yunfeng [0000-0003-1700-6049], Walraven, Robert [0000-0002-5755-4325], van de Walle, Axel [0000-0002-3415-1494], Turtle, James A [0000-0003-0735-7769], Ben-Nun, Michal [0000-0002-9164-0008], Riley, Steven [0000-0001-7904-4804], Koyluoglu, Ugur [0000-0002-6286-351X], Cavany, Sean [0000-0002-2559-797X], España, Guido [0000-0002-9915-8056], Moore, Sean [0000-0001-9062-6100], Oidtman, Rachel [0000-0003-1773-9533], Perkins, Alex [0000-0002-7518-4014], Kraus, David [0000-0003-4376-3932], Cao, Wei [0000-0001-5640-0917], Lavista Ferres, Juan [0000-0002-9654-3178], Vespignani, Alessandro [0000-0003-3419-4205], Sinha, Deeksha [0000-0002-9788-728X], Perakis, Georgia [0000-0002-0888-9030], Bennouna, Mohammed Amine [0000-0002-9123-8588], Spantidakis, Ioannis [0000-0002-5149-6320], Tsiourvas, Asterios [0000-0002-2979-6300], Sarker, Arnab [0000-0003-1680-9421], Jadbabaie, Ali [0000-0003-1122-3069], Shah, Devavrat [0000-0003-0737-3259], Celi, Leo A [0000-0001-6712-6626], Osthus, Dave [0000-0002-4681-091X], Fairchild, Geoffrey [0000-0001-5500-8120], Mullany, Luke C [0000-0003-4668-9803], Rainwater-Lovett, Kaitlin [0000-0002-8707-7339], Lee, Elizabeth C [0000-0002-4156-9637], Dent, Juan [0000-0003-3154-0731], Hill, Alison L [0000-0002-6583-3623], Keegan, Lindsay T [0000-0002-8526-3007], Lemaitre, Joseph C [0000-0002-2677-6574], Truelove, Shaun A [0000-0003-0538-0607], Wills, Josh [0000-0001-7285-9349], Gao, Lei [0000-0002-4707-0933], Gu, Zhiling [0000-0002-8052-7608], Yu, Shan [0000-0002-0271-5726], Hay, Simon I [0000-0002-0611-7272], Murray, Chris [0000-0002-4930-9450], Stage, Steven A [0000-0001-5361-6464], Prakash, B Aditya [0000-0002-3252-455X], Rodríguez, Alexander [0000-0002-4313-9913], Xie, Jiajia [0000-0001-6530-2489], Keskinocak, Pinar [0000-0003-2686-546X], Baxter, Arden [0000-0002-6345-2229], Oruc, Buse Eylul [0000-0003-2431-3864], Sinha, Rajarishi [0000-0001-9157-674X], Yoder, Nate [0000-0003-4153-4722], Zhang, Leyou [0000-0002-2454-0082], Funk, Sebastian [0000-0002-2842-3406], Meakin, Sophie R [0000-0002-6385-2652], Sherratt, Katharine [0000-0003-2049-3423], Yamana, Teresa K [0000-0001-8349-3151], Pei, Sen [0000-0002-7072-2995], Shaman, Jeffrey [0000-0002-7216-7809], Li, Michael L [0000-0002-2456-4834], Bertsimas, Dimitris [0000-0002-1985-1003], Skali Lami, Omar [0000-0002-8208-3035], Soni, Saksham [0000-0002-8898-5726], Tazi Bouardi, Hamza [0000-0002-7871-325X], Wang, Yuanjia [0000-0002-1510-3315], McDonald, Daniel [0000-0002-0443-4282], Politsch, Collin [0000-0003-3727-9167], Rajanala, Samyak [0000-0002-5791-3789], Rumack, Aaron [0000-0002-9181-1794], Tibshirani, Ryan J [0000-0002-2158-8304], Drake, John M [0000-0003-4646-1235], Ho, Lam Si Tung [0000-0002-0453-8444], Slayton, Rachel B [0000-0003-4699-8040], Johansson, Michael A [0000-0002-5090-7722], Biggerstaff, Matthew [0000-0001-5108-8311], Reich, Nicholas G [0000-0003-3503-9899], and Apollo - University of Cambridge Repository

Subjects
model evaluation, Multidisciplinary, COVID-19, prediction, United States, Data Accuracy, 510 Mathematics, 360 Social problems & social services, weather, Humans, Public Health, ddc:510, ensemble forecast, Pandemics, Mathematics, Forecasting, Probability
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Published
2022
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2. Global impact of COVID-19 on stroke care

Author

Nogueira, Raul G., Abdalkader, Mohamad, Qureshi, Muhammed M., Frankel, Michael R., Mansour, Ossama Yassin, Yamagami, Hiroshi, Qiu, Zhongming, Farhoudi, Mehdi, Siegler, James E, Yaghi, Shadi, Raz, Eytan, Sakai, Nobuyuki, Ohara, Nobuyuki, Piotin, Michel, Mechtouff, Laura, Eker, Omer, Chalumeau, Vanessa, Kleinig, Timothy J., Pop, Raoul, Liu, Jianmin, Winters, Hugh S., Shang, Xianjin, Rodriguez Vasquez, Alejandro, Blasco, Jordi, Arenillas, Juan F, Martinez-Galdamez, Mario, Brehm, Alex, Psychogios, Marios-Nikos, Lylyk, Pedro, Haussen, Diogo C., Al-Bayati, Alhamza R., Mohammaden, Mahmoud H., Fonseca, Luísa, Luís Silva, M., Montalverne, Francisco, Renieri, Leonardo, Mangiafico, Salvatore, Fischer, Urs, Gralla, Jan, Frei, Donald, Chugh, Chandril, Mehta, Brijesh P., Nagel, Simon, Mohlenbruch, Markus, Ortega-Gutierrez, Santiago, Farooqui, Mudassir, Hassan, Ameer E, Taylor, Allan, Lapergue, Bertrand, Consoli, Arturo, Campbell, Bruce C.V., Sharma, Malveeka, Walker, Melanie, Van Horn, Noel, Fiehler, Jens, Nguyen, Huy Thang, Nguyen, Quoc T., Watanabe, Daisuke, Zhang, Hao, Le, Huynh V., Nguyen, Viet Q., Shah, Ruchir, Devlin, Thomas, Khandelwal, Priyank, Linfante, Italo, Izzath, Wazim, Lavados, Pablo M., Olavarría, Veronica V., Sampaio Silva, Gisele, de Carvalho Sousa, Anna Verena, Kirmani, Jawad, Bendszus, Martin, Amano, Tatsuo, Yamamoto, Ryoo, Doijiri, Ryosuke, Tokuda, Naoki, Yamada, Takehiro, Terasaki, Tadashi, Yazawa, Yukako, Morris, Jane G., Griffin, Emma, Thornton, John, Lavoie, Pascale, Matouk, Charles, Hill, M. D, Demchuk, Andrew M., Killer-Oberpfalzer, Monika, Nahab, Fadi, Altschul, Dorothea, Ramos-Pachón, Anna, Pérez de la Ossa, Natalia, Kikano, Raghid, Boisseau, William, Walker, Gregory, Cordina, Steve M., Puri, Ajit, Luisa Kuhn, Anna, Gandhi, Dheeraj, Ramakrishnan, Pankajavalli, Novakovic-White, Roberta, Chebl, Alex, Kargiotis, Odysseas, Czap, Alexandra L, Zha, Alicia M, Masoud, Hesham E., Lopez, Carlos, Ozretic, David, Al-Mufti, Fawaz, Zie, Wenjie, Duan, Zhenhui, Yuan, Zhengzhou, Huang, Wenguo, Hao, Yonggang, Luo, Jun, Kalousek, Vladimir, Bourcier, Romain, Guile, Romain, Hetts, Steven, Al-Jehani, Hosam M., AlHazzani, Adel, Sadeghi-Hokmabadi, Elyar, Teleb, Mohamed, Payne, Jeremy, Lee, Jin Soo, Hong, Ji Man, Sohn, Sung-Il, Hwang, Yang-Ha, Shin, Dong Hoon, Roh, Hong Gee, Edgell, Randy, Khatri, Rakesh, Smith, Ainsley, Malik, Amer, Liebeskind, David, Herial, Nabeel, Jabbour, Pascal, Magalhaes, Pedro, Ozdemir, Atilla Ozcan, Aykac, Ozlem, Uwatoko, Takeshi, Dembo, Tomohisa, Shimizu, Hisao, Sugiura, Yuri, Miyashita, Fumio, Fukuda, Hiroki, Miyake, Kosuke, Shimbo, Junsuke, Sugimura, Yusuke, Beer-Furlan, Andre, Joshi, Krishna, Catanese, Luciana, Abud, Daniel Giansante, Neto, Octavio Giansante, Mehrpour, Masoud, Al Hashmi, Amal, Saqqur, Mahar, Mostafa, Abdulrahman, Fifi, Johanna T., Hussain, Syed, John, Seby, Gupta, Rishi, Sivan-Hoffmann, Rotem, Reznik, Anna, Sani, Achmad Fidaus, Geyik, Serdar, Akıl, Eşref, Churojana, Anchalee, Ghoreishi, Abdoreza, Saadatnia, Mohammad, Sharifipour, Ehsan, Ma, Alice, Faulder, Ken, Wu, Teddy, Leung, Lester, Malek, Adel, Voetsch, Barbara, Wakhloo, Ajay, Rivera, Rodrigo, Barrientos Iman, Danny Moises, Pikula, Aleksandra, Lioutas, Vasileios-Arsenios, Thomalla, Gotz, Birnbaum, Lee, Machi, Paolo, Bernava, Gianmarco, McDermott, Mollie, Kleindorfer, Dawn, Wong, Ken, Patterson, Mary S., Fiorot, Jose Antonio, Huded, Vikram, Mack, William, Tenser, Matthew, Eskey, Clifford, Multani, Sumeet, Kelly, Michael, Janardhan, Vallabh, Cornett, Oriana, Singh, Varsha, Murayama, Yuichi, Mokin, Maxim, Yang, Pengfei, Zhang, Xiaoxi, Yin, Congguo, Han, Hongxing, Peng, Ya, Chen, Wenhuo, Crosa, Roberto, Frudit, Michel E., Pandian, Jeyaraj D., Kulkarni, Anirudh, Yagita, Yoshiki, Takenobu, Yohei, Matsumaru, Yuji, Yamada, Satoshi, Kono, Ryuhei, Kanamaru, Takuya, Yamazaki, Hidekazu, Sakaguchi, Manabu, Todo, Kenichi, Yamamoto, Nobuaki, Sonoda, Kazutaka, Yoshida, Tomoko, Hashimoto, Hiroyuki, Nakahara, Ichiro, Cora, Elena, Volders, David, Ducroux, Celina, Shoamanesh, Ashkan, Ospel, Johanna, Kaliaev, Artem, Ahmed, Saima, Rashid, Umair, Rebello, Leticia C., Pereira, Vitor Mendes, Fahed, Robert, Chen, Michael, Sheth, Sunil A., Palaiodimou, Lina, Tsivgoulis, Georgios, Chandra, Ronil, Koyfman, Feliks, Leung, Thomas, Khosravani, Houman, Dharmadhikari, Sushrut, Frisullo, Giovanni, Calabresi, Paolo, Tsiskaridze, Alexander, Lobjanidze, Nino, Grigoryan, Mikayel, Czlonkowska, Anna, de Sousa, Diana A., Demeestere, Jelle, Liang, Conrad, Sangha, Navdeep, Lutsep, Helmi L., Ayo-Martín, Óscar, Cruz-Culebras, Antonio, Tran, Anh D., Young, Chang Y., Cordonnier, Charlotte, Caparros, Francois, De Lecinana, Maria Alonso, Fuentes, Blanca, Yavagal, Dileep, Jovin, Tudor, Spelle, Laurent, Moret, Jacques, Khatri, Pooja, Zaidat, Osama, Raymond, Jean, Martins, Sheila, Nguyen, Thanh N, Universitat Autònoma de Barcelona, and UAM. Departamento de Medicina

Subjects
Gerontology, 2019-20 coronavirus outbreak, medicine.medical_specialty, acute ischemic stroke, Hospitals, Low-Volume, Time Factors, Coronavirus disease 2019 (COVID-19), Medicina, Clinical Neurology, Stroke care, stroke care, Global Health, mechanical thrombectomy, Pandemic, Epidemiology, Health care, Global health, Medicine, Humans, Registries, Stroke, Retrospective Studies, Thrombectomy, Science & Technology, business.industry, Research, COVID-19, medicine.disease, Hospitalization, Cross-Sectional Studies, Peripheral Vascular Disease, Neurology, Cardiovascular System & Cardiology, epidemiology, Neurosciences & Neurology, business, Life Sciences & Biomedicine, Intracranial Hemorrhages, Hospitals, High-Volume, intracranial hemorrhage
Abstract

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAM, Background: The COVID-19 pandemic led to profound changes in the organization of health care systems worldwide. Aims: We sought to measure the global impact of the COVID-19 pandemic on the volumes for mechanical thrombectomy, stroke, and intracranial hemorrhage hospitalizations over a three-month period at the height of the pandemic (1 March–31 May 2020) compared with two control three-month periods (immediately preceding and one year prior). Methods: Retrospective, observational, international study, across 6 continents, 40 countries, and 187 comprehensive stroke centers. The diagnoses were identified by their ICD-10 codes and/or classifications in stroke databases at participating centers. Results: The hospitalization volumes for any stroke, intracranial hemorrhage, and mechanical thrombectomy were 26,699, 4002, and 5191 in the three months immediately before versus 21,576, 3540, and 4533 during the first three pandemic months, representing declines of 19.2% (95%CI, −19.7 to −18.7), 11.5% (95%CI, −12.6 to −10.6), and 12.7% (95%CI, −13.6 to −11.8), respectively. The decreases were noted across centers with high, mid, and low COVID-19 hospitalization burden, and also across high, mid, and low volume stroke/mechanical thrombectomy centers. High-volume COVID-19 centers (−20.5%) had greater declines in mechanical thrombectomy volumes than mid- (−10.1%) and low-volume (−8.7%) centers (p < 0.0001). There was a 1.5% stroke rate across 54,366 COVID-19 hospitalizations. SARS-CoV-2 infection was noted in 3.9% (784/20,250) of all stroke admissions. Conclusion: The COVID-19 pandemic was associated with a global decline in the volume of overall stroke hospitalizations, mechanical thrombectomy procedures, and intracranial hemorrhage admission volumes. Despite geographic variations, these volume reductions were observed regardless of COVID-19 hospitalization burden and pre-pandemic stroke/mechanical thrombectomy volumes

Published
2021

3. Vascular endothelial growth factor: Biology and therapeutic applications

Author

Calvin J. Kuo and Quoc T. Ho

Subjects
Vascular Endothelial Growth Factor A, Sorafenib, Neovascularization, Pathologic, Bevacizumab, Angiogenesis, Sunitinib, Cancer, Angiogenesis Inhibitors, Antineoplastic Agents, Cell Biology, Biology, Pharmacology, Sunitinib malate, medicine.disease, Biochemistry, Article, Vascular endothelial growth factor, chemistry.chemical_compound, Vascular endothelial growth factor A, chemistry, Neoplasms, medicine, Cancer research, Humans, medicine.drug
Abstract

While the development of anti-angiogenic therapy, as it pertains to cancer treatment, may still be in its infancy relative to well-established modalities such as chemotherapy, radiation, and surgery, major strides made in the past several decades have allowed translation of basic science discoveries in this field into clinical reality. The discovery of key molecular modulators of angiogenesis, notably vascular endothelial growth factor (VEGF), has catalyzed the development of numerous neutralizing therapeutic agents. The validity of VEGF inhibition as a therapeutic strategy has been well supported in randomized clinical trials, as well as U.S. Food and Drug Administration approval of the VEGF antagonists bevacizumab, sunitinib malate, sorafenib, pegaptinib and ranibizumab. Accordingly, this review will (1) briefly review the basic molecular biology of VEGF and (2) summarize recent progress in targeting the VEGF molecular pathway as therapy for angiogenic diseases such as cancer and age-related macular degeneration.

Published
2007
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4. The relative biologic effectiveness versus linear energy transfer curve as an output-input relation for linear cellular systems

Author

Quoc T. Luu and Paul DuChateau

Subjects
Current (mathematics), Toy model, Basis (linear algebra), DNA Repair, Differential equation, Applied Mathematics, Systems biology, Linear energy transfer, Dose-Response Relationship, Radiation, General Medicine, Type (model theory), Models, Biological, Cell Line, Computational Mathematics, Modeling and Simulation, Relative biological effectiveness, Humans, Linear Energy Transfer, General Agricultural and Biological Sciences, Biological system, Simulation, Relative Biological Effectiveness, Mathematics, DNA Damage
Abstract

Experiments have established that different radiation types have different magnitudes of biological response. When biological response is defined in terms of the Relative Biologic Effectiveness (RBE) and different radiation type is characterized by Linear Energy Transfer (LET), the plot of the RBE versus LET (RBE-LET) curve shows RBE to increase with increasing LET, to reach a maximum, and to decrease with further increasing LET. Perhaps due to the descriptive nature of biology, most quantitative models for the RBE-LET curve ignore the reality of the underlying molecular biology. On the other hand, the molecular basis for the RBE-LET curve is not completely known despite recent efforts. Here we introduce a differential equation formulation for a signal-and-system model that sees cells as systems, different radiation types as input, and cellular responses as output. Because of scant knowledge of the underlying biochemical network, the current version is necessarily a work in progress. It explains the RBE-LET curve using not just input parameters but also systems internal state parameters. These systems internal state parameters represent parts of a biochemical network within a cell. Although multiple biochemical parts may well be involved, the shape of the RBE-LET curve is reproduced when only three system parameters are related to three biochemical parts: the molecular machinery for DNA double strand break repair; the molecular pathways for handling oxidative stress; and the radiolytic products of the cellular water. Despite being a simplified ''toy model,'' changes in the systems state parameters lead to model curves that are refutable in a modern molecular biology laboratory. As the parts in the biochemical network of the radiation response are being further elucidated, this model can incorporate new systems state parameters to allow a more accurate fit.

Published
2009

5. Fractionated proton radiation treatment for pediatric craniopharyngioma: preliminary report

Author

Quoc T, Luu, Lilia N, Loredo, John O, Archambeau, Les T, Yonemoto, James M, Slater, and Jerry D, Slater

Subjects
Adult, Male, Salvage Therapy, Adolescent, California, Craniopharyngioma, Proton Therapy, Humans, Female, Pituitary Neoplasms, Radiotherapy, Adjuvant, Dose Fractionation, Radiation, Radiotherapy, Conformal, Child, Retrospective Studies
Abstract

This retrospective preliminary review evaluated the efficacy and toxicity of fractionated proton radiotherapy in the management of pediatric craniopharyngioma.Sixteen patients, aged 7-34 years, were treated with proton-beam radiation. All had undergone at least one tumor resection. Seven patients underwent repeat resection for recurrence; one had previous x-ray radiotherapy. A daily dose of 1.8 cobalt gray equivalent was used to give a total dose in the range of 50.4-59.4 cobalt gray equivalent.Local control was achieved in 14 of 15 patients. Twelve of 15 patients survived. There were few acute side effects. Long-term complications included newly diagnosed panhypopituitarism, a cerebrovascular accident from which the patient fully recovered, and an out-of-proton-field meningioma in the single patient who received previous radiotherapy.Fractionated proton radiotherapy is an effective treatment for children with craniopharyngioma. Longer follow-up is needed to evaluate late complications.

Published
2006

6. A clinical interactive technique for MR-CT image registration for target delineation of intracranial tumors

Author

Daniel W. Miller, James M. Slater, K. Shahnazi, Richard P. Levy, Quoc T. Luu, Jerry D. Slater, and Leslie T. Yonemoto

Subjects
Cancer Research, business.product_category, Computer science, Image registration, Sensitivity and Specificity, 03 medical and health sciences, DICOM, 0302 clinical medicine, Imaging, Three-Dimensional, medicine, Humans, Radiation treatment planning, Retrospective Studies, Pixel, medicine.diagnostic_test, business.industry, Brain Neoplasms, Isocenter, Magnetic resonance imaging, Magnetic Resonance Imaging, Oncology, 030220 oncology & carcinogenesis, Tomography, Computer monitor, business, Nuclear medicine, Tomography, X-Ray Computed
Abstract

Replacement of current CT-based, three-dimensional (3D) treatment planning systems by newer versions capable of automated multi-modality image registration may be economically prohibitive for most radiation oncology clinics. We present a low-cost technique for MR-CT image registration on a "first generation" CT-based, 3D treatment planning system for intracranial tumors. The technique begins with fabrication of a standard treatment mask. A second truncated mask, the "minimask," is then made, using the standard mask as a mold. Two orthogonal leveling vials glued onto the minimask detect angular deviations in pitch and roll. Preservation of yaw is verified by referencing a line marked according to the CT laser on the craniocaudal axis. The treatment mask immobilizes the patient's head for CT. The minimask reproduces this CT-based angular treatment position, which is then maintained by taping the appropriately positioned head to the MR head coil for MR scanning. All CT and MR images, in DICOM 3.0 format, are entered into the treatment planning system via a computer network. Interactive registration of MR to CT images is controlled by real-time visual feedback on the computer monitor. Translational misalignments at the target are eliminated or minimized by iterative use of qualitative visual inspection. In this study, rotational errors were measured in a retrospective series of 20 consecutive patients who had undergone CT-MR image registration using this technique. Anatomic structures defined the three CT orthogonal axes from which angular errors on MR image were measured. Translational errors at the target isocenter were within pixel size, as judged by visual inspection. Clinical setup using the minimask resulted in overall average angular deviation of 3 degrees +/-2 degrees (mean +/- SD) and translational deviation within the edges of the target volume of typically less than 2 mm. The accuracy of this registration technique for target delineation of intracranial tumors is compatible with practice guidelines. This method, then, provides a cost-effective means to register MR and CT images for target delineation of intracranial tumors.

Published
2005

7. 3-D conformal radiotherapy procedure and registration of pre- and intra-radiotherapeutic data

Author

Bolla M, Cinquin P, Troccaz J, Vassal P, Laieb N, Menguy Y, Berland E, Dusserre A, Giraud Jean-Yves, and Dang Huy Quoc T

Subjects
Male, Radiotherapy Planning, Computer-Assisted, Image Processing, Computer-Assisted, Humans, Prostatic Neoplasms, Dose-Response Relationship, Radiation, Radiotherapy Dosage, Tomography, X-Ray Computed, Magnetic Resonance Imaging
Abstract

The authors describe the different steps of 3-D conformal external irradiation. They mention as well two procedures of isocentric repositioning which are mandatory for the matching of anatomical data: the first one is acquired before the simulation and the second one just before the radiotherapy session.

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