Author: "Karami M" / Publisher: elsevier - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Karami M"' showing total 38 results

Start Over Author "Karami M" Publisher elsevier

38 results on '"Karami M"'

1. Piezoelectric Energy Systems

Author: Anton, Steven R., primary and Karami, M. Amin, additional
Published: 2017
Full Text: View/download PDF

2. Global, regional, and national burden of meningitis, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016

Author: Zunt, JR, Kassebaum, NJ, Blake, N, Glennie, L, Wright, C, Nichols, E, Abd-Allah, F, Abdela, J, Abdelalim, A, Adamu, AA, Adib, MG, Ahmadi, A, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Akseer, N, Al-Raddadi, RM, Alahdab, F, Alene, KA, Aljunid, SM, AlMazora, MA, Khalid, Alvis-Guzman, N, Animut, MD, Anjomshoa, M, Ansha, MG, Asghar, RJ, Avokpaho, EFGA, Awasthi, A, Badali, H, Barac, A, Baernighausen, TW, Bassat, Q, Bedi, N, Belachew, AB, Bhattacharyya, K, Bhutta, ZA, Bijani, A, Butt, ZA, Carvalho, F, Castaneda-Orjuela, CA, Chitheer, A, Choi, J-YJ, Christopher, DJ, Dang, AK, Daryani, A, Demoz, GT, Djalalinia, S, Huyen, PD, Dubey, M, Dubljanin, E, Duken, EE, Zaki, MES, Elyazar, IR, Fakhim, H, Fernandes, E, Fischer, F, Fukumoto, T, Ganji, M, Gebre, AK, Gebremeskel, A, Gessner, BD, Gopalani, SV, Guo, Y, Gupta, R, Hailu, GB, Haj-Mirzaian, A, Hamidi, S, Hay, S, Henok, A, Irvani, SSN, Jha, RP, Jurisson, M, Kahsay, A, Karami, M, Karch, A, Kasaeian, A, Kassa, TD, Kefale, AT, Khader, YS, Khalil, IA, Khan, EA, Khang, Y-H, Khubchandani, J, Kimokoti, RW, Kisa, A, Lami, FH, Levi, M, Li, S, Loy, CT, Majdan, M, Majeed, A, Mantovani, LG, Martins-Melo, FR, McAlinden, C, Mehta, V, Melese, A, Memish, ZA, Mengistu, G, Mestrovic, T, Mezgebe, HB, Miazgowski, B, Milosevic, B, Mokdad, AH, Monasta, L, Moradi, G, Moraga, P, Mousavi, SM, Mueller, UO, Murthy, S, Mustafa, G, Naheed, A, Naik, G, Newton, CRJ, Nirayo, YL, Nixon, MR, Ofori-Asenso, R, Ogbo, FA, Olagunju, TO, Olusanya, BO, Ortiz, JR, Owolabi, MO, Patel, S, Pinilla-Monsalve, GD, Postma, MJ, Qorbani, M, Rafiei, A, Rahimi-Movaghar, V, Reiner, RC, Renzaho, AMN, Rezai, MS, Roba, KT, Ronfani, L, Roshandel, G, Rostami, A, Safari, S, Safiri, S, Sagar, R, Samy, AM, Milicevic, MMS, Sartorius, B, Sarvi, S, Sawhney, M, Saxena, S, Shafieesabet, A, Shaikh, MA, Sharif, M, Shigematsu, M, Si, S, Skiadaresi, E, Smith, M, Somayaji, R, Sufiyan, MB, Tawye, NY, Temsah, M-H, Tortajada-Girbes, M, Khanh, BT, Ukwaja, KN, Ullah, I, Vujcic, IS, Wagnew, F, Waheed, Y, Weldegwergs, KG, Winkler, AS, Wiyeh, AB, Wiysonge, CS, Wyper, GMA, Yimer, EM, Yonemoto, N, Zaidi, Z, Zenebe, ZM, Feigin, VL, Vos, T, and Murray, CJL
Subjects: BACTERIAL-MENINGITIS, RISK, AFRICA, Science & Technology, Neurology & Neurosurgery, IMPACT, Clinical Neurology, UNITED-STATES, CHILDREN, 1103 Clinical Sciences, GBD 2016 Meningitis Collaborators, PNEUMOCOCCAL CONJUGATE VACCINE, HEALTH-CARE, EPIDEMIOLOGY, Neurosciences & Neurology, MENINGOCOCCAL DISEASE, 1109 Neurosciences, Life Sciences & Biomedicine
Abstract: Background Acute meningitis has a high case-fatality rate and survivors can have severe lifelong disability. We aimed to provide a comprehensive assessment of the levels and trends of global meningitis burden that could help to guide introduction, continuation, and ongoing development of vaccines and treatment programmes. Methods The Global Burden of Diseases, Injuries, and Risk Factors (GBD) 2016 study estimated meningitis burden due to one of four types of cause: pneumococcal, meningococcal, Haemophilus influenzae type b, and a residual category of other causes. Cause-specific mortality estimates were generated via cause of death ensemble modelling of vital registration and verbal autopsy data that were subject to standardised data processing algorithms. Deaths were multiplied by the GBD standard life expectancy at age of death to estimate years of life lost, the mortality component of disability-adjusted life-years (DALYs). A systematic analysis of relevant publications and hospital and claims data was used to estimate meningitis incidence via a Bayesian meta-regression tool. Meningitis deaths and cases were split between causes with meta-regressions of aetiological proportions of mortality and incidence, respectively. Probabilities of long-term impairment by cause of meningitis were applied to survivors and used to estimate years of life lived with disability (YLDs). We assessed the relationship between burden metrics and Socio-demographic Index (SDI), a composite measure of development based on fertility, income, and education. Findings Global meningitis deaths decreased by 21·0% from 1990 to 2016, from 403 012 (95% uncertainty interval [UI] 319 426–458 514) to 318 400 (265 218–408 705). Incident cases globally increased from 2·50 million (95% UI 2·19–2·91) in 1990 to 2·82 million (2·46–3·31) in 2016. Meningitis mortality and incidence were closely related to SDI. The highest mortality rates and incidence rates were found in the peri-Sahelian countries that comprise the African meningitis belt, with six of the ten countries with the largest number of cases and deaths being located within this region. Haemophilus influenzae type b was the most common cause of incident meningitis in 1990, at 780 070 cases (95% UI 613 585–978 219) globally, but decreased the most (–49·1%) to become the least common cause in 2016, with 397 297 cases (291 076–533 662). Meningococcus was the leading cause of meningitis mortality in 1990 (192 833 deaths [95% UI 153 358–221 503] globally), whereas other meningitis was the leading cause for both deaths (136 423 [112 682–178 022]) and incident cases (1·25 million [1·06–1·49]) in 2016. Pneumococcus caused the largest number of YLDs (634 458 [444 787–839 749]) in 2016, owing to its more severe long-term effects on survivors. Globally in 2016, 1·48 million (1·04—1·96) YLDs were due to meningitis compared with 21·87 million (18·20—28·28) DALYs, indicating that the contribution of mortality to meningitis burden is far greater than the contribution of disabling outcomes. Interpretation Meningitis burden remains high and progress lags substantially behind that of other vaccine-preventable diseases. Particular attention should be given to developing vaccines with broader coverage against the causes of meningitis, making these vaccines affordable in the most affected countries, improving vaccine uptake, improving access to low-cost diagnostics and therapeutics, and improving support for disabled survivors. Substantial uncertainty remains around pathogenic causes and risk factors for meningitis. Ongoing, active cause-specific surveillance of meningitis is crucial to continue and to improve monitoring of meningitis burdens and trends throughout the world.
Published: 2018

3. Population and fertility by age and sex for 195 countries and territories, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017

Author: Murray, Christopher J L, Callender, Charlton S K H, Kulikoff, Xie Rachel, Srinivasan, Vinay, Abate, Degu, Abate, Kalkidan Hassen, Abay, Solomon M, Abbasi, Nooshin, Abbastabar, Hedayat, Abdela, Jemal, Abdelalim, Ahmed, Abdel-Rahman, Omar, Abdi, Alireza, Abdoli, Nasrin, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Haftom Temesgen, Abebe, Molla, Abebe, Zegeye, Abebo, Teshome Abuka, Abejie, Ayenew Negesse, Aboyans, Victor, Abraha, Haftom Niguse, Abreu, Daisy Maria Xavier, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen M E, Accrombessi, Manfred Mario Kokou, Acharya, Pawan, Adamu, Abdu A, Adebayo, Oladimeji M, Adedeji, Isaac Akinkunmi, Adekanmbi, Victor, Adetokunboh, Olatunji O, Adhena, Beyene Meressa, Adhikari, Tara Ballav, Adib, Mina G, Adou, Arsène Kouablan, Adsuar, Jose C, Afarideh, Mohsen, Afshin, Ashkan, Agarwal, Gina, Agesa, Kareha M, Aghayan, Sargis Aghasi, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmed, Muktar Beshir, Ahmed, Sayem, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akanda, Ali S, Akbari, Mohammad Esmaeil, Akibu, Mohammed, Akinyemi, Rufus Olusola, Akinyemiju, Tomi, Akseer, Nadia, Alahdab, Fares, Al-Aly, Ziyad, Alam, Khurshid, Alebel, Animut, Aleman, Alicia V, Alene, Kefyalew Addis, Al-Eyadhy, Ayman, Ali, Raghib, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Almasi, Ali, Alonso, Jordi, Al-Raddadi, Rajaa M, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Ammar, Walid, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Ansari, Hossein, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T, Appiah, Seth Christopher Yaw, Aremu, Olatunde, Areri, Habtamu Abera, Arian, Nicholas, Ärnlöv, Johan, Artaman, Al, Aryal, Krishna K, Asayesh, Hamid, Asfaw, Ephrem Tsegay, Asgedom, Solomon Weldegebreal, Assadi, Reza, Atey, Tesfay Mehari Mehari, Atique, Suleman, Atteraya, Madhu Sudhan, Ausloos, Marcel, Avokpaho, Euripide F G A, Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayele, Yohanes, Ayer, Rakesh, Ayuk, Tambe B, Azzopardi, Peter S, Babalola, Tesleem Kayode, Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Bali, Ayele Geleto, Banach, Maciej, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H, Basaleem, Huda, Bassat, Quique, Basu, Arindam, Baune, Bernhard T, Baynes, Habtamu Wondifraw, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Bekele, Bayu Begashaw, Belachew, Abate Bekele, Belay, Aregawi Gebreyesus, Belay, Ezra, Belay, Saba Abraham, Belay, Yihalem Abebe, Bell, Michelle L, Bello, Aminu K, Bennett, Derrick A, Bensenor, Isabela M, Bergeron, Gilles, Berhane, Adugnaw, Berman, Adam E, Bernabe, Eduardo, Bernstein, Robert S, Bertolacci, Gregory J, Beuran, Mircea, Bhattarai, Suraj, Bhaumik, Soumyadeep, Bhutta, Zulfiqar A, Biadgo, Belete, Bijani, Ali, Bikbov, Boris, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Birlik, Sait Mentes, Birungi, Charles, Biswas, Tuhin, Bizuneh, Hailemichael, Bleyer, Archie, Basara, Berrak Bora, Bosetti, Cristina, Boufous, Soufiane, Brady, Oliver J, Bragazzi, Nicola Luigi, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J K, Brenner, Hermann, Brewer, Jerry D, Briant, Paul Svitil, Britton, Gabrielle, Burstein, Roy, Busse, Reinhard, Butt, Zahid A, Cahuana-Hurtado, Lucero, Campos-Nonato, Ismael R, Campuzano Rincon, Julio Cesar, Cano, Jorge, Car, Mate, Cárdenas, Rosario, Carrero, Juan J, Carvalho, Félix, Castañeda-Orjuela, Carlos A, Castillo Rivas, Jacqueline, Castro, Franz, Catalá-López, Ferrán, Çavlin, Alanur, Cerin, Ester, Chalek, Julian, Chang, Hsing-Yi, Chang, Jung-Chen, Chattopadhyay, Aparajita, Chaturvedi, Pankaj, Chiang, Peggy Pei-Chia, Chin, Ken Lee, Chisumpa, Vesper Hichilombwe, Chitheer, Abdulaal, Choi, Jee-Young J, Chowdhury, Rajiv, Christopher, Devasahayam J, Cicuttini, Flavia M, Ciobanu, Liliana G, Cirillo, Massimo, Claro, Rafael M, Collado-Mateo, Daniel, Constantin, Maria-Magdalena, Conti, Sara, Cooper, Cyrus, Cooper, Leslie Trumbull, Cornaby, Leslie, Cortesi, Paolo Angelo, Cortinovis, Monica, Costa, Megan, Cromwell, Elizabeth, Crowe, Christopher Stephen, Cukelj, Petra, Cunningham, Matthew, Daba, Alemneh Kabeta, Dachew, Berihun Assefa, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I, Daryani, Ahmad, Das Gupta, Rajat, Das Neves, José, Dasa, Tamirat Tesfaye, Dash, Aditya Prasad, Weaver, Nicole Davis, Davitoiu, Dragos Virgil, Davletov, Kairat, De Leo, Diego, De Neve, Jan-Walter, Degefa, Meaza Girma, Degenhardt, Louisa, Degfie, Tizta Tilahun, Deiparine, Selina, Demoz, Gebre Teklemariam, Demtsu, Balem, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolaos, Des Jarlais, Don C, Dessie, Getenet Ayalew, Dharmaratne, Samath D, Dhimal, Meghnath, Dicker, Daniel, Ding, Eric L, Dinsa, Girmaye Deye, Djalalinia, Shirin, Do, Huyen Phuc, Dokova, Klara, Doku, David Teye, Dolan, Kate A, Doyle, Kerrie E, Driscoll, Tim R, Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Duraes, Andre R, Ebrahimpour, Soheil, Edvardsson, David, El Bcheraoui, Charbel, El-Khatib, Ziad, Elyazar, Iqbal Rf, Enayati, Ahmadali, Endries, Aman Yesuf, Ermakov, Sergey Petrovich, Eshrati, Babak, Eskandarieh, Sharareh, Esmaeili, Reza, Esteghamati, Alireza, Esteghamati, Sadaf, Estep, Kara, Fakhim, Hamed, Farag, Tamer, Faramarzi, Mahbobeh, Fareed, Mohammad, Farinha, Carla Sofia E Sá, Faro, Andre, Farvid, Maryam S, Farzadfar, Farshad, Farzaei, Mohammad Hosein, Fay, Kairsten A, Fazeli, Mir Sohail, Feigin, Valery L, Feigl, Andrea B, Feizy, Fariba, Fenny, Ama P, Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, Eduarda, Feyissa, Garumma Tolu, Filip, Irina, Finegold, Samuel, Fischer, Florian, Flor, Luisa Sorio, Foigt, Nataliya A, Foreman, Kyle J, Fornari, Carla, Fürst, Thomas, Fukumoto, Takeshi, Fuller, John E, Fullman, Nancy, Gakidou, Emmanuela, Gallus, Silvano, Gamkrelidze, Amiran, Ganji, Morsaleh, Gankpe, Fortune Gbetoho, Garcia, Gregory M, Garcia-Gordillo, Miguel Á, Gebre, Abadi Kahsu, Gebre, Teshome, Gebregergs, Gebremedhin Berhe, Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Gelano, Tilayie Feto, Gelaw, Yalemzewod Assefa, Geleijnse, Johanna M, Genova-Maleras, Ricard, Gething, Peter, Gezae, Kebede Embaye, Ghadami, Mohammad Rasoul, Ghadimi, Reza, Ghadiri, Keyghobad, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Ghiasvand, Hesam, Ghimire, Mamata, Ghoshal, Aloke Gopal, Gill, Paramjit Singh, Gill, Tiffany K, Giussani, Giorgia, Gnedovskaya, Elena V, Goli, Srinivas, Gomez, Ricardo Santiago, Gómez-Dantés, Hector, Gona, Philimon N, Goodridge, Amador, Gopalani, Sameer Vali, Goulart, Alessandra C, Goulart, Bárbara Niegia Garcia, Grada, Ayman, Grosso, Giuseppe, Gugnani, Harish Chander C, Guo, Jingwen, Guo, Yuming, Gupta, Prakash C, Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Haagsma, Juanita A, Hachinski, Vladimir, Hafezi-Nejad, Nima, Hagos, Tekleberhan B, Hailegiyorgis, Tewodros Tesfa, Hailu, Gessessew Bugssa, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hamadeh, Randah R, Hamidi, Samer, Handal, Alexis J, Hankey, Graeme J, Hao, Yuantao, Harb, Hilda L, Haririan, Hamidreza, Haro, Josep Maria, Hasan, Mehedi, Hassankhani, Hadi, Hassen, Hamid Yimam, Havmoeller, Rasmus, Hay, Simon I, He, Yihua, Hedayatizadeh-Omran, Akbar, Hegazy, Mohamed I, Heibati, Behzad, Heidari, Behnam, Hendrie, Delia, Henok, Andualem, Henry, Nathaniel J, Herteliu, Claudiu, Heydarpour, Fatemeh, Hibstu, Desalegn T, Hole, Michael K, Homaie Rad, Enayatollah, Hoogar, Praveen, Hosgood, H Dean, Hosseini, Seyed Mostafa, Hosseini Chavoshi, Meimanat M, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hsairi, Mohamed, Hsiao, Thomas, Hu, Guoqing, Huang, John J, Iburg, Kim Moesgaard, Igumbor, Ehimario U, Ikeda, Chad Thomas, Ilesanmi, Olayinka Stephen, Iqbal, Usman, Irenso, Asnake Ararsa, Irvani, Seyed Sina Naghibi, Isehunwa, Oluwaseyi Oluwakemi, Islam, Sheikh Mohammed Shariful, Jahangiry, Leila, Jahanmehr, Nader, Jain, Sudhir Kumar, Jakovljevic, Mihajlo, Jalu, Moti Tolera, James, Spencer L, Jassal, Simerjot K, Javanbakht, Mehdi, Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Ji, John S, Jonas, Jost B, Jozwiak, Jacek Jerzy, Jungari, Suresh Banayya, Jürisson, Mikk, Kabir, Zubair, Kadel, Rajendra, Kahsay, Amaha, Kalani, Rizwan, Kapil, Umesh, Karami, Manoochehr, Matin, Behzad Karami, Karch, André, Karema, Corine, Karimi, Seyed M, Kasaeian, Amir, Kassa, Dessalegn H, Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassa, Zemenu Yohannes, Kassebaum, Nicholas J, Kastor, Anshul, Katikireddi, Srinivasa Vittal, Kaul, Anil, Kawakami, Norito, Karyani, Ali Kazemi, Kebede, Seifu, Keiyoro, Peter Njenga, Kemp, Grant Rodgers, Kengne, Andre Pascal, Keren, Andre, Kereselidze, Maia, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalid, Nauman, Khalil, Ibrahim A, Khan, Ejaz Ahmad, Khan, Muhammad Shahzeb, Khang, Young-Ho, Khanna, Tripti, Khater, Mona M, Khatony, Alireza, Khazaeipour, Zahra, Khazaie, Habibolah, Khoja, Abdullah T, Khosravi, Ardeshir, Khosravi, Mohammad Hossein, Kibret, Getiye D, Kidanemariam, Zelalem Teklemariam, Kiirithio, Daniel N, Kilgore, Paul Evan, Kim, Daniel, Kim, Jun Y, Kim, Young-Eun, Kim, Yun Jin, Kimokoti, Ruth W, Kinfu, Yohannes, Kinra, Sanjay, Kisa, Adnan, Kivimäki, Mika, Kochhar, Sonali, Kokubo, Yoshihiro, Kolola, Tufa, Kopec, Jacek A, Kosek, Margaret N, Kosen, Soewarta, Koul, Parvaiz A, Koyanagi, Ai, Krishan, Kewal, Krishnaswami, Sanjay, Krohn, Kristopher J, Defo, Barthelemy Kuate, Bicer, Burcu Kucuk, Kumar, G Anil, Kumar, Manasi, Kumar, Pushpendra, Kumsa, Fekede Asefa, Kutz, Michael J, Lad, Sheetal D, Lafranconi, Alessandra, Lal, Dharmesh Kumar, Lalloo, Ratilal, Lam, Hilton, Lami, Faris Hasan, Lang, Justin J, Lanksy, Sonia, Lansingh, Van C, Laryea, Dennis Odai, Lassi, Zohra S, Latifi, Arman, Laxmaiah, Avula, Lazarus, Jeffrey V, Lee, James B, Lee, Paul H, Leigh, James, Leshargie, Cheru Tesema, Leta, Samson, Levi, Miriam, Li, Shanshan, Li, Xiaohong, Li, Yichong, Liang, Juan, Liang, Xiaofeng, Liben, Misgan Legesse, Lim, Lee-Ling, Limenih, Miteku Andualem, Linn, Shai, Liu, Shiwei, Lorkowski, Stefan, Lotufo, Paulo A, Lozano, Rafael, Lunevicius, Raimundas, Mabika, Crispin Mabika, Macarayan, Erlyn Rachelle King, Mackay, Mark T, Madotto, Fabiana, Mahmood, Tarek Abd Elaziz, Mahotra, Narayan Bahadur, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malik, Manzoor Ahmad, Mamun, Abdullah A, Manamo, Wondimu Ayele, Manda, Ana-Laura, Mangalam, Srikanth, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Mapoma, Chabila Christopher, Marami, Dadi, Maravilla, Joemer C, Marcenes, Wagner, Marina, Shakhnazarova, Martins-Melo, Francisco Rogerlândio, März, Winfried, Marzan, Melvin B, Mashamba-Thompson, Tivani Phosa, Masiye, Felix, Mason-Jones, Amanda J, Massenburg, Benjamin Ballard, Mathur, Manu Raj, Maulik, Pallab K, Mazidi, Mohsen, McGrath, John J, Mehata, Suresh, Mehendale, Sanjay Madhav, Mehndiratta, Man Mohan, Mehrotra, Ravi, Mehrzadi, Saeed, Mehta, Kala M, Mehta, Varshil, Mekonnen, Tefera C, Meles, Hagazi Gebre, Meles, Kidanu Gebremariam, Melese, Addisu, Melku, Mulugeta, Memiah, Peter T N, Memish, Ziad A, Mendoza, Walter, Mengesha, Melkamu Merid, Mengistu, Desalegn Tadese, Mengistu, Getnet, Mensah, George A, Mereta, Seid Tiku, Meretoja, Atte, Meretoja, Tuomo J, Mestrovic, Tomislav, Mezgebe, Haftay Berhane, Miangotar, Yode, Miazgowski, Bartosz, Miazgowski, Tomasz, Miller, Ted R, Miller-Petrie, Molly Katherine, Mini, G K, Mirabi, Parvaneh, Mirica, Andreea, Mirrakhimov, Erkin M, Misganaw, Awoke Temesgen, Moazen, Babak, Mohammad, Karzan Abdulmuhsin, Mohammadi, Moslem, Mohammadifard, Noushin, Mohammadi-Khanaposhtani, Maryam, Mohammed, Mohammed A, Mohammed, Shafiu, Mokdad, Ali H, Mola, Glen Dl, Molokhia, Mariam, Monasta, Lorenzo, Montañez, Julio Cesar, Moradi, Ghobad, Moradi, Mahmoudreza, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morgado-Da-Costa, Joana, Mori, Rintaro, Morrison, Shane Douglas, Mosapour, Abbas, Moschos, Marilita M, Mousavi, Seyyed Meysam, Muche, Achenef Asmamaw, Muchie, Kindie Fentahun, Mueller, Ulrich Otto, Mukhopadhyay, Satinath, Muller, Kate, Murphy, Tasha B, Murthy, G V S, Musa, Jonah, Musa, Kamarul Imran, Mustafa, Ghulam, Muthupandian, Saravanan, Nachega, Jean B, Nagel, Gabriele, Naghavi, Mohsen, Naheed, Aliya, Nahvijou, Azin, Naik, Gurudatta, Naik, Paulami, Najafi, Farid, Naldi, Luigi, Nangia, Vinay, Nansseu, Jobert Richie, Nascimento, Bruno Ramos, Nawaz, Haseeb, Ncama, Busisiwe P, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Neupane, Subas, Newton, Charles Richard James, Ngalesoni, Frida N, Ngunjiri, Josephine W, Nguyen, Grant, Nguyen, Long Hoang, Nguyen, Trang Huyen, Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nisar, Muhammad Imran, Nixon, Molly R, Nomura, Shuhei, Noroozi, Mehdi, Noubiap, Jean Jacques, Nouri, Hamid Reza, Shiadeh, Malihe Nourollahpour, Nowroozi, Mohammad Reza, Nyandwi, Alypio, Nyasulu, Peter S, Odell, Christopher M, Ofori-Asenso, Richard, Ogah, Okechukwu Samuel, Ogbo, Felix Akpojene, Oh, In-Hwan, Okoro, Anselm, Oladimeji, Olanrewaju, Olagunju, Andrew T, Olagunju, Tinuke O, Olivares, Pedro R, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Sok King, Ortiz, Alberto, Osgood-Zimmerman, Aaron, Ota, Erika, Otieno, Brenda Achieng, Otstavnov, Stanislav S, Owolabi, Mayowa Ojo, Oyekale, Abayomi Samuel, P A, Mahesh, Pakhale, Smita, Pakhare, Abhijit P, Pana, Adrian, Panda, Basant Kumar, Panda-Jonas, Songhomitra, Pandey, Achyut Raj, Park, Eun-Kee, Parsian, Hadi, Patel, Shanti, Patil, Snehal T, Patle, Ajay, Patton, George C, Paturi, Vishnupriya Rao, Paudel, Deepak, Pedroso, Marcel Moraes, Peprah, Emmanuel K, Pereira, David M, Perico, Norberto, Pesudovs, Konrad, Petri, William A, Petzold, Max, Pierce, Maxwell, Pigott, David M, Pillay, Julian David, Pirsaheb, Meghdad, Polanczyk, Guilherme V, Postma, Maarten J, Pourmalek, Farshad, Pourshams, Akram, Poustchi, Hossein, Prakash, Swayam, Prasad, Narayan, Purcell, Caroline A, Purwar, Manorama B, Qorbani, Mostafa, Quansah, Reginald, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Md Shafiur, Rahman, Mohammad Hifz Ur, Rahman, Muhammad Aziz, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Rajati, Fatemeh, Rajsic, Sasa, Ram, Usha, Ranabhat, Chhabi Lal, Ranjan, Prabhat, Rawaf, David Laith, Rawaf, Salman, Ray, Sarah E, Razo-García, Christian, Reiner, Robert C, Reis, Cesar, Remuzzi, Giuseppe, Renzaho, Andre M N, Resnikoff, Serge, Rezaei, Satar, Rezaeian, Shahab, Rezai, Mohammad Sadegh, Riahi, Seyed Mohammad, Rios-Blancas, Maria Jesus, Roba, Kedir Teji, Roberts, Nicholas L S, Roever, Leonardo, Ronfani, Luca, Roshandel, Gholamreza, Rostami, Ali, Rubagotti, Enrico, Ruhago, George Mugambage, Sabde, Yogesh Damodar, Sachdev, Perminder S, Saddik, Basema, Saeedi Moghaddam, Sahar, Safari, Hosein, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Safi, Sare, Safiri, Saeid, Sagar, Rajesh, Sahebkar, Amirhossein, Sahraian, Mohammad Ali, Sajadi, Haniye Sadat, Salahshoor, Mohamadreza, Salam, Nasir, Salama, Joseph S, Salamati, Payman, Saldanha, Raphael De Freitas, Saleem, Zikria, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A, Salvi, Sundeep Santosh, Salz, Inbal, Sambala, Evanson Zondani, Samy, Abdallah M, Sanabria, Juan, Sanchez-Niño, Maria Dolores, Santos, Itamar S, Santric Milicevic, Milena M, Sao Jose, Bruno Piassi, Sardana, Mayank, Sarker, Abdur Razzaque, Sarmiento-Suárez, Rodrigo, Saroshe, Satish, Sarrafzadegan, Nizal, Sartorius, Benn, Sarvi, Shahabeddin, Sathian, Brijesh, Satpathy, Maheswar, Sawant, Arundhati R, Sawhney, Monika, Saxena, Sonia, Schaeffner, Elke, Schelonka, Kathryn, Schneider, Ione J C, Schwebel, David C, Schwendicke, Falk, Seedat, Soraya, Sekerija, Mario, Sepanlou, Sadaf G, Serván-Mori, Edson, Shabaninejad, Hosein, Shackelford, Katya Anne, Shafieesabet, Azadeh, Shaheen, Amira A, Shaikh, Masood Ali, Shakir, Raad A, Shams-Beyranvand, Mehran, Shamsi, Mohammadbagher, Shamsizadeh, Morteza, Sharafi, Heidar, Sharafi, Kiomars, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharma, Jayendra, Sharma, Rajesh, She, Jun, Sheikh, Aziz, Shi, Peilin, Shibuya, Kenji, Shigematsu, Mika, Shiri, Rahman, Shirkoohi, Reza, Shiue, Ivy, Shokraneh, Farhad, Shukla, Sharvari Rahul, Si, Si, Siabani, Soraya, Sibai, Abla Mehio, Siddiqi, Tariq J, Sigfusdottir, Inga Dora, Sigurvinsdottir, Rannveig, Silpakit, Naris, Silva, Diego Augusto Santos, Silva, João Pedro, Silveira, Dayane Gabriele Alves, Singam, Narayana Sarma Venkata, Singh, Jasvinder A, Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Sliwa, Karen, Soares Filho, Adauto Martins, Sobaih, Badr Hasan, Sobhani, Soheila, Soofi, Moslem, Soriano, Joan B, Soyiri, Ireneous N, Sreeramareddy, Chandrashekhar T, Starodubov, Vladimir I, Steiner, Caitlyn, Stewart, Leo G, Stokes, Mark A, Strong, Mark, Subart, Michelle L, Sufiyan, Mu'awiyyah Babale, Sulo, Gerhard, Sunguya, Bruno F, Sur, Patrick John, Sutradhar, Ipsita, Sykes, Bryan L, Sylaja, P N, Sylte, Dillon O, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Tabb, Karen M, Tadakamadla, Santosh Kumar, Tandon, Nikhil, Tassew, Aberash Abay, Tassew, Segen Gebremeskel, Taveira, Nuno, Tawye, Nega Yimer, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekle, Merhawi Gebremedhin, Temsah, Mohamad-Hani, Terkawi, Abdullah Sulieman, Teshale, Manaye Yihune, Tessema, Belay, Teweldemedhin, Mebrahtu, Thakur, Jarnail Singh, Thankappan, Kavumpurathu Raman, Thirunavukkarasu, Sathish, Thomas, Nihal, Thomson, Alan J, Tilahun, Binyam, To, Quyen G, Tonelli, Marcello, Topor-Madry, Roman, Torre, Anna E, Tortajada-Girbés, Miguel, Tovani-Palone, Marcos Roberto, Toyoshima, Hideaki, Tran, Bach Xuan, Tran, Khanh Bao, Tripathy, Srikanth Prasad, Truelsen, Thomas Clement, Truong, Nu Thi, Tsadik, Afewerki Gebremeskel, Tsegay, Amanuel, Tsilimparis, Nikolaos, Tudor Car, Lorainne, Ukwaja, Kingsley N, Ullah, Irfan, Usman, Muhammad Shariq, Uthman, Olalekan A, Uzun, Selen Begüm, Vaduganathan, Muthiah, Vaezi, Afsane, Vaidya, Gaurang, Valdez, Pascual R, Varavikova, Elena, Varughese, Santosh, Vasankari, Tommi Juhani, Vasconcelos, Ana Maria Nogales, Venketasubramanian, Narayanaswamy, Villafaina, Santos, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vujcic, Isidora S, Wagnew, Fasil Shiferaw, Waheed, Yasir, Walson, Judd L, Wang, Yanping, Wang, Yuan-Pang, Weiderpass, Elisabete, Weintraub, Robert G, Weldegwergs, Kidu Gidey, Werdecker, Andrea, Westerman, Ronny, Whiteford, Harvey, Widecka, Justyna, Widecka, Katarzyna, Wijeratne, Tissa, Winkler, Andrea Sylvia, Wiysonge, Charles Shey, Wolfe, Charles D A, Wu, Shouling, Wyper, Grant M A, Xu, Gelin, Yamada, Tomohide, Yano, Yuichiro, Yaseri, Mehdi, Yasin, Yasin Jemal, Ye, Pengpeng, Yentür, Gökalp Kadri, Yeshaneh, Alex, Yimer, Ebrahim M, Yip, Paul, Yisma, Engida, Yonemoto, Naohiro, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zadnik, Vesna, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zare, Zohreh, Zeleke, Mulugeta Molla, Zenebe, Zerihun Menlkalew, Zerfu, Taddese Alemu, Zhang, Xueying, Zhao, Xiu-Ju, Zhou, Maigeng, Zhu, Jun, Zimsen, Stephanie R M, Zodpey, Sanjay, Zoeckler, Leo, Lopez, Alan D, Lim, Stephen S, Halk Sağlığı, GBD 2017 Population Fertility Coll, Murray, Cjl, Callender, Cskh, Kulikoff, Xr, Srinivasan, V, Abate, D, Abate, Kh, Abay, Sm, Abbasi, N, Abbastabar, H, Abdela, J, Abdelalim, A, Abdel-Rahman, O, Abdi, A, Abdoli, N, Abdollahpour, I, Abdulkader, R, Abebe, Ht, Abebe, M, Abebe, Z, Abebo, Ta, Abejie, An, Aboyans, V, Abraha, Hn, Abreu, Dmx, Abrham, Ar, Abu-Raddad, Lj, Abu-Rmeileh, Nme, Accrombessi, Mmk, Acharya, P, Adamu, Aa, Adebayo, Om, Adedeji, Ia, Adekanmbi, V, Adetokunboh, Oo, Adhena, Bm, Adhikari, Tb, Adib, Mg, Adou, Ak, Adsuar, Jc, Afarideh, M, Afshin, A, Agarwal, G, Agesa, Km, Aghayan, Sa, Agrawal, S, Ahmadi, A, Ahmadi, M, Ahmed, Mb, Ahmed, S, Aichour, An, Aichour, I, Aichour, Mte, Akanda, A, Akbari, Me, Akibu, M, Akinyemi, Ro, Akinyemiju, T, Akseer, N, Alahdab, F, Al-Aly, Z, Alam, K, Alebel, A, Aleman, Av, Alene, Ka, Al-Eyadhy, A, Ali, R, Alijanzadeh, M, Alizadeh-Navaei, R, Aljunid, Sm, Alkerwi, A, Alla, F, Allebeck, P, Almasi, A, Alonso, J, Al-Raddadi, Rm, Alsharif, U, Altirkawi, K, Alvis-Guzman, N, Amare, At, Ammar, W, Anber, Nh, Andrei, Cl, Androudi, S, Animut, Md, Ansari, H, Ansha, Mg, Antonio, Cat, Appiah, Scy, Aremu, O, Areri, Ha, Arian, N, Ärnlöv, J, Artaman, A, Aryal, Kk, Asayesh, H, Asfaw, Et, Asgedom, Sw, Assadi, R, Atey, Tmm, Atique, S, Atteraya, M, Ausloos, M, Avokpaho, Efga, Awasthi, A, Ayala Quintanilla, Bp, Ayele, Y, Ayer, R, Ayuk, Tb, Azzopardi, P, Babalola, Tk, Babazadeh, A, Badali, H, Badawi, A, Bali, Ag, Banach, M, Barker-Collo, Sl, Bärnighausen, Tw, Barrero, Lh, Basaleem, H, Bassat, Q, Basu, A, Baune, Bt, Baynes, Hw, Beghi, E, Behzadifar, M, Bekele, Bb, Belachew, Ab, Belay, Ag, Belay, E, Belay, Sa, Belay, Ya, Bell, Ml, Bello, Ak, Bennett, Da, Bensenor, Im, Bergeron, G, Berhane, A, Berman, Ae, Bernabe, E, Bernstein, R, Bertolacci, Gj, Beuran, M, Bhattarai, S, Bhaumik, S, Bhutta, Za, Biadgo, B, Bijani, A, Bikbov, B, Bililign, N, Bin Sayeed, M, Birlik, Sm, Birungi, C, Biswas, T, Bizuneh, H, Bleyer, A, Basara, Bb, Bosetti, C, Boufous, S, Brady, Oj, Bragazzi, Nl, Brainin, M, Brazinova, A, Breitborde, Njk, Brenner, H, Brewer, Jd, Briant, P, Britton, G, Burstein, R, Busse, R, Butt, Za, Cahuana-Hurtado, L, Campos-Nonato, Ir, Campuzano Rincon, Jc, Cano, J, Car, M, Cárdenas, R, Carrero, Jj, Carvalho, F, Castañeda-Orjuela, Ca, Castillo Rivas, J, Castro, F, Catalá-López, F, Çavlin, A, Cerin, E, Chalek, J, Chang, Hy, Chang, Jc, Chattopadhyay, A, Chaturvedi, P, Chiang, Pp, Chin, Kl, Chisumpa, Vh, Chitheer, A, Choi, Jj, Chowdhury, R, Christopher, Dj, Cicuttini, Fm, Ciobanu, Lg, Cirillo, M, Claro, Rm, Collado-Mateo, D, Constantin, Mm, Conti, S, Cooper, C, Cooper, Lt, Cornaby, L, Cortesi, Pa, Cortinovis, M, Costa, M, Cromwell, E, Crowe, C, Cukelj, P, Cunningham, M, Daba, Ak, Dachew, Ba, Dandona, L, Dandona, R, Dargan, Pi, Daryani, A, Das Gupta, R, Das Neves, J, Dasa, Tt, Dash, Ap, Weaver, Nd, Davitoiu, Dv, Davletov, K, De Leo, D, De Neve, Jw, Degefa, Mg, Degenhardt, L, Degfie, Tt, Deiparine, S, Demoz, Gt, Demtsu, B, Denova-Gutiérrez, E, Deribe, K, Dervenis, N, Des Jarlais, Dc, Dessie, Ga, Dharmaratne, Sd, Dhimal, M, Dicker, D, Ding, El, Dinsa, Gd, Djalalinia, S, Do, Hp, Dokova, K, Doku, Dt, Dolan, Ka, Doyle, Ke, Driscoll, Tr, Dubey, M, Dubljanin, E, Duken, Ee, Duraes, Ar, Ebrahimpour, S, Edvardsson, D, El Bcheraoui, C, El-Khatib, Z, Elyazar, Ir, Enayati, A, Endries, Ay, Ermakov, Sp, Eshrati, B, Eskandarieh, S, Esmaeili, R, Esteghamati, A, Esteghamati, S, Estep, K, Fakhim, H, Farag, T, Faramarzi, M, Fareed, M, Farinha, Cse, Faro, A, Farvid, M, Farzadfar, F, Farzaei, Mh, Fay, Ka, Fazeli, M, Feigin, Vl, Feigl, Ab, Feizy, F, Fenny, Ap, Fentahun, N, Fereshtehnejad, Sm, Fernandes, E, Feyissa, Gt, Filip, I, Finegold, S, Fischer, F, Flor, L, Foigt, Na, Foreman, Kj, Fornari, C, Fürst, T, Fukumoto, T, Fuller, Je, Fullman, N, Gakidou, E, Gallus, S, Gamkrelidze, A, Ganji, M, Gankpe, Fg, Garcia, Gm, Garcia-Gordillo, Má, Gebre, Ak, Gebre, T, Gebregergs, Gb, Gebrehiwot, Tt, Gebremedhin, At, Gelano, Tf, Gelaw, Ya, Geleijnse, Jm, Genova-Maleras, R, Gething, P, Gezae, Ke, Ghadami, Mr, Ghadimi, R, Ghadiri, K, Ghasemi Falavarjani, K, Ghasemi-Kasman, M, Ghiasvand, H, Ghimire, M, Ghoshal, Ag, Gill, P, Gill, Tk, Giussani, G, Gnedovskaya, Ev, Goli, S, Gomez, R, Gómez-Dantés, H, Gona, Pn, Goodridge, A, Gopalani, Sv, Goulart, Ac, Goulart, Bng, Grada, A, Grosso, G, Gugnani, Hcc, Guo, J, Guo, Y, Gupta, Pc, Gupta, R, Gupta, T, Haagsma, Ja, Hachinski, V, Hafezi-Nejad, N, Hagos, Tb, Hailegiyorgis, Tt, Hailu, Gb, Haj-Mirzaian, A, Hamadeh, Rr, Hamidi, S, Handal, Aj, Hankey, Gj, Hao, Y, Harb, Hl, Haririan, H, Haro, Jm, Hasan, M, Hassankhani, H, Hassen, Hy, Havmoeller, R, Hay, Si, He, Y, Hedayatizadeh-Omran, A, Hegazy, Mi, Heibati, B, Heidari, B, Hendrie, D, Henok, A, Henry, Nj, Herteliu, C, Heydarpour, F, Hibstu, Dt, Hole, Mk, Homaie Rad, E, Hoogar, P, Hosgood, Hd, Hosseini, Sm, Hosseini Chavoshi, Mm, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hsairi, M, Hsiao, T, Hu, G, Huang, Jj, Iburg, Km, Igumbor, Eu, Ikeda, Ct, Ilesanmi, O, Iqbal, U, Irenso, Aa, Irvani, Ssn, Isehunwa, Oo, Islam, Sm, Jahangiry, L, Jahanmehr, N, Jain, Sk, Jakovljevic, M, Jalu, Mt, James, Sl, Jassal, Sk, Javanbakht, M, Jayatilleke, Au, Jeemon, P, Jha, Rp, Jha, V, Ji, J, Jonas, Jb, Jozwiak, Jj, Jungari, Sb, Jürisson, M, Kabir, Z, Kadel, R, Kahsay, A, Kalani, R, Kapil, U, Karami, M, Matin, Bk, Karch, A, Karema, C, Karimi, Sm, Kasaeian, A, Kassa, Dh, Kassa, Gm, Kassa, Td, Kassa, Zy, Kassebaum, Nj, Kastor, A, Katikireddi, Sv, Kaul, A, Kawakami, N, Karyani, Ak, Kebede, S, Keiyoro, Pn, Kemp, Gr, Kengne, Ap, Keren, A, Kereselidze, M, Khader, Y, Khafaie, Ma, Khajavi, A, Khalid, N, Khalil, Ia, Khan, Ea, Khan, M, Khang, Yh, Khanna, T, Khater, Mm, Khatony, A, Khazaeipour, Z, Khazaie, H, Khoja, At, Khosravi, A, Khosravi, Mh, Kibret, Gd, Kidanemariam, Zt, Kiirithio, Dn, Kilgore, Pe, Kim, D, Kim, Jy, Kim, Ye, Kim, Yj, Kimokoti, Rw, Kinfu, Y, Kinra, S, Kisa, A, Kivimäki, M, Kochhar, S, Kokubo, Y, Kolola, T, Kopec, Ja, Kosek, Mn, Kosen, S, Koul, Pa, Koyanagi, A, Krishan, K, Krishnaswami, S, Krohn, Kj, Defo, Bk, Bicer, Bk, Kumar, Ga, Kumar, M, Kumar, P, Kumsa, Fa, Kutz, Mj, Lad, Sd, Lafranconi, A, Lal, Dk, Lalloo, R, Lam, H, Lami, Fh, Lang, Jj, Lanksy, S, Lansingh, Vc, Laryea, Do, Lassi, Z, Latifi, A, Laxmaiah, A, Lazarus, Jv, Lee, Jb, Lee, Ph, Leigh, J, Leshargie, Ct, Leta, S, Levi, M, Li, S, Li, X, Li, Y, Liang, J, Liang, X, Liben, Ml, Lim, Ll, Limenih, Ma, Linn, S, Liu, S, Lorkowski, S, Lotufo, Pa, Lozano, R, Lunevicius, R, Mabika, Cm, Macarayan, Erk, Mackay, Mt, Madotto, F, Mahmood, Tae, Mahotra, Nb, Majdan, M, Majdzadeh, R, Majeed, A, Malekzadeh, R, Malik, Ma, Mamun, Aa, Manamo, Wa, Manda, Al, Mangalam, S, Mansournia, Ma, Mantovani, Lg, Mapoma, Cc, Marami, D, Maravilla, Jc, Marcenes, W, Marina, S, Martins-Melo, Fr, März, W, Marzan, Mb, Mashamba-Thompson, Tp, Masiye, F, Mason-Jones, Aj, Massenburg, Bb, Mathur, Mr, Maulik, Pk, Mazidi, M, Mcgrath, Jj, Mehata, S, Mehendale, Sm, Mehndiratta, Mm, Mehrotra, R, Mehrzadi, S, Mehta, Km, Mehta, V, Mekonnen, Tc, Meles, Hg, Meles, Kg, Melese, A, Melku, M, Memiah, Ptn, Memish, Za, Mendoza, W, Mengesha, Mm, Mengistu, Dt, Mengistu, G, Mensah, Ga, Mereta, St, Meretoja, A, Meretoja, Tj, Mestrovic, T, Mezgebe, Hb, Miangotar, Y, Miazgowski, B, Miazgowski, T, Miller, Tr, Miller-Petrie, Mk, Mini, Gk, Mirabi, P, Mirica, A, Mirrakhimov, Em, Misganaw, At, Moazen, B, Mohammad, Ka, Mohammadi, M, Mohammadifard, N, Mohammadi-Khanaposhtani, M, Mohammed, Ma, Mohammed, S, Mokdad, Ah, Mola, Gd, Molokhia, M, Monasta, L, Montañez, Jc, Moradi, G, Moradi, M, Moradi-Lakeh, M, Moradinazar, M, Moraga, P, Morgado-Da-Costa, J, Mori, R, Morrison, Sd, Mosapour, A, Moschos, Mm, Mousavi, Sm, Muche, Aa, Muchie, Kf, Mueller, Uo, Mukhopadhyay, S, Muller, K, Murphy, Tb, Murthy, Gv, Musa, J, Musa, Ki, Mustafa, G, Muthupandian, S, Nachega, Jb, Nagel, G, Naghavi, M, Naheed, A, Nahvijou, A, Naik, G, Naik, P, Najafi, F, Naldi, L, Nangia, V, Nansseu, Jr, Nascimento, Br, Nawaz, H, Ncama, Bp, Neamati, N, Negoi, I, Negoi, Ri, Neupane, S, Newton, Crj, Ngalesoni, Fn, Ngunjiri, Jw, Nguyen, G, Nguyen, Lh, Nguyen, Th, Ningrum, Dna, Nirayo, Yl, Nisar, Mi, Nixon, Mr, Nomura, S, Noroozi, M, Noubiap, Jj, Nouri, Hr, Shiadeh, Mn, Nowroozi, Mr, Nyandwi, A, Nyasulu, P, Odell, Cm, Ofori-Asenso, R, Ogah, O, Ogbo, Fa, Oh, Ih, Okoro, A, Oladimeji, O, Olagunju, At, Olagunju, To, Olivares, Pr, Olusanya, Bo, Olusanya, Jo, Ong, Sk, Ortiz, A, Osgood-Zimmerman, A, Ota, E, Otieno, Ba, Otstavnov, S, Owolabi, Mo, Oyekale, A, P A, M, Pakhale, S, Pakhare, Ap, Pana, A, Panda, Bk, Panda-Jonas, S, Pandey, Ar, Park, Ek, Parsian, H, Patel, S, Patil, St, Patle, A, Patton, Gc, Paturi, Vr, Paudel, D, Pedroso, Mm, Peprah, Ek, Pereira, Dm, Perico, N, Pesudovs, K, Petri, Wa, Petzold, M, Pierce, M, Pigott, Dm, Pillay, Jd, Pirsaheb, M, Polanczyk, Gv, Postma, Mj, Pourmalek, F, Pourshams, A, Poustchi, H, Prakash, S, Prasad, N, Purcell, Ca, Purwar, Mb, Qorbani, M, Quansah, R, Radfar, A, Rafay, A, Rafiei, A, Rahim, F, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rahman, Mhu, Rahman, Ma, Rahman, Su, Rai, Rk, Rajati, F, Rajsic, S, Ram, U, Ranabhat, Cl, Ranjan, P, Rawaf, Dl, Rawaf, S, Ray, Se, Razo-García, C, Reiner, Rc, Reis, C, Remuzzi, G, Renzaho, Amn, Resnikoff, S, Rezaei, S, Rezaeian, S, Rezai, M, Riahi, Sm, Rios-Blancas, Mj, Roba, Kt, Roberts, Nl, Roever, L, Ronfani, L, Roshandel, G, Rostami, A, Rubagotti, E, Ruhago, Gm, Sabde, Yd, Sachdev, P, Saddik, B, Saeedi Moghaddam, S, Safari, H, Safari, Y, Safari-Faramani, R, Safdarian, M, Safi, S, Safiri, S, Sagar, R, Sahebkar, A, Sahraian, Ma, Sajadi, H, Salahshoor, M, Salam, N, Salama, J, Salamati, P, Saldanha, Rf, Saleem, Z, Salimi, Y, Salimzadeh, H, Salomon, Ja, Salvi, S, Salz, I, Sambala, Ez, Samy, Am, Sanabria, J, Sanchez-Niño, Md, Santos, I, Santric Milicevic, Mm, Sao Jose, Bp, Sardana, M, Sarker, Ar, Sarmiento-Suárez, R, Saroshe, S, Sarrafzadegan, N, Sartorius, B, Sarvi, S, Sathian, B, Satpathy, M, Sawant, Ar, Sawhney, M, Saxena, S, Schaeffner, E, Schelonka, K, Schneider, Ijc, Schwebel, Dc, Schwendicke, F, Seedat, S, Sekerija, M, Sepanlou, Sg, Serván-Mori, E, Shabaninejad, H, Shackelford, Ka, Shafieesabet, A, Shaheen, Aa, Shaikh, Ma, Shakir, Ra, Shams-Beyranvand, M, Shamsi, M, Shamsizadeh, M, Sharafi, H, Sharafi, K, Sharif, M, Sharif-Alhoseini, M, Sharma, J, Sharma, R, She, J, Sheikh, A, Shi, P, Shibuya, K, Shigematsu, M, Shiri, R, Shirkoohi, R, Shiue, I, Shokraneh, F, Shukla, Sr, Si, S, Siabani, S, Sibai, Am, Siddiqi, Tj, Sigfusdottir, Id, Sigurvinsdottir, R, Silpakit, N, Silva, Da, Silva, Jp, Silveira, Dga, Singam, Nsv, Singh, Ja, Singh, Np, Singh, V, Sinha, Dn, Sliwa, K, Soares Filho, Am, Sobaih, Bh, Sobhani, S, Soofi, M, Soriano, Jb, Soyiri, In, Sreeramareddy, Ct, Starodubov, Vi, Steiner, C, Stewart, Lg, Stokes, Ma, Strong, M, Subart, Ml, Sufiyan, Mb, Sulo, G, Sunguya, Bf, Sur, Pj, Sutradhar, I, Sykes, Bl, Sylaja, Pn, Sylte, Do, Szoeke, Cei, Tabarés-Seisdedos, R, Tabb, Km, Tadakamadla, Sk, Tandon, N, Tassew, Aa, Tassew, Sg, Taveira, N, Tawye, Ny, Tehrani-Banihashemi, A, Tekalign, Tg, Tekle, Mg, Temsah, Mh, Terkawi, A, Teshale, My, Tessema, B, Teweldemedhin, M, Thakur, J, Thankappan, Kr, Thirunavukkarasu, S, Thomas, N, Thomson, Aj, Tilahun, B, To, Qg, Tonelli, M, Topor-Madry, R, Torre, Ae, Tortajada-Girbés, M, Tovani-Palone, Mr, Toyoshima, H, Tran, Bx, Tran, Kb, Tripathy, Sp, Truelsen, Tc, Truong, Nt, Tsadik, Ag, Tsegay, A, Tsilimparis, N, Tudor Car, L, Ukwaja, Kn, Ullah, I, Usman, M, Uthman, Oa, Uzun, Sb, Vaduganathan, M, Vaezi, A, Vaidya, G, Valdez, Pr, Varavikova, E, Varughese, S, Vasankari, Tj, Vasconcelos, Amn, Venketasubramanian, N, Villafaina, S, Violante, F, Vladimirov, Sk, Vlassov, V, Vollset, Se, Vos, T, Vosoughi, K, Vujcic, I, Wagnew, F, Waheed, Y, Walson, Jl, Wang, Y, Wang, Yp, Weiderpass, E, Weintraub, Rg, Weldegwergs, Kg, Werdecker, A, Westerman, R, Whiteford, H, Widecka, J, Widecka, K, Wijeratne, T, Winkler, A, Wiysonge, C, Wolfe, Cda, Wu, S, Wyper, Gma, Xu, G, Yamada, T, Yano, Y, Yaseri, M, Yasin, Yj, Ye, P, Yentür, Gk, Yeshaneh, A, Yimer, Em, Yip, P, Yisma, E, Yonemoto, N, Yoon, Sj, Yotebieng, M, Younis, Mz, Yousefifard, M, Yu, C, Zadnik, V, Zaidi, Z, Zaman, Sb, Zamani, M, Zare, Z, Zeleke, Mm, Zenebe, Zm, Zerfu, Ta, Zhang, X, Zhao, Xj, Zhou, M, Zhu, J, Zimsen, Srm, Zodpey, S, Zoeckler, L, Lopez, Ad, Lim, Ss., Murray, Christopher J L, Callender, Charlton S K H, Kulikoff, Xie Rachel, Srinivasan, Vinay, Abate, Degu, Abate, Kalkidan Hassen, Abay, Solomon M, Abbasi, Nooshin, Abbastabar, Hedayat, Abdela, Jemal, Abdelalim, Ahmed, Abdel-Rahman, Omar, Abdi, Alireza, Abdoli, Nasrin, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Haftom Temesgen, Abebe, Molla, Abebe, Zegeye, Abebo, Teshome Abuka, Abejie, Ayenew Negesse, Aboyans, Victor, Abraha, Haftom Niguse, Abreu, Daisy Maria Xavier, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen M E, Accrombessi, Manfred Mario Kokou, Acharya, Pawan, Adamu, Abdu A, Adebayo, Oladimeji M, Adedeji, Isaac Akinkunmi, Adekanmbi, Victor, Adetokunboh, Olatunji O, Adhena, Beyene Meressa, Adhikari, Tara Ballav, Adib, Mina G, Adou, Arsène Kouablan, Adsuar, Jose C, Afarideh, Mohsen, Afshin, Ashkan, Agarwal, Gina, Agesa, Kareha M, Aghayan, Sargis Aghasi, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmed, Muktar Beshir, Ahmed, Sayem, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akanda, Ali S, Akbari, Mohammad Esmaeil, Akibu, Mohammed, Akinyemi, Rufus Olusola, Akinyemiju, Tomi, Akseer, Nadia, Alahdab, Fare, Al-Aly, Ziyad, Alam, Khurshid, Alebel, Animut, Aleman, Alicia V, Alene, Kefyalew Addi, Al-Eyadhy, Ayman, Ali, Raghib, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, Françoi, Allebeck, Peter, Almasi, Ali, Alonso, Jordi, Al-Raddadi, Rajaa M, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Ammar, Walid, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Ansari, Hossein, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T, Appiah, Seth Christopher Yaw, Aremu, Olatunde, Areri, Habtamu Abera, Arian, Nichola, Ärnlöv, Johan, Artaman, Al, Aryal, Krishna K, Asayesh, Hamid, Asfaw, Ephrem Tsegay, Asgedom, Solomon Weldegebreal, Assadi, Reza, Atey, Tesfay Mehari Mehari, Atique, Suleman, Atteraya, Madhu Sudhan, Ausloos, Marcel, Avokpaho, Euripide F G A, Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayele, Yohane, Ayer, Rakesh, Ayuk, Tambe B, Azzopardi, Peter S, Babalola, Tesleem Kayode, Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Bali, Ayele Geleto, Banach, Maciej, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H, Basaleem, Huda, Bassat, Quique, Basu, Arindam, Baune, Bernhard T, Baynes, Habtamu Wondifraw, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Bekele, Bayu Begashaw, Belachew, Abate Bekele, Belay, Aregawi Gebreyesu, Belay, Ezra, Belay, Saba Abraham, Belay, Yihalem Abebe, Bell, Michelle L, Bello, Aminu K, Bennett, Derrick A, Bensenor, Isabela M, Bergeron, Gille, Berhane, Adugnaw, Berman, Adam E, Bernabe, Eduardo, Bernstein, Robert S, Bertolacci, Gregory J, Beuran, Mircea, Bhattarai, Suraj, Bhaumik, Soumyadeep, Bhutta, Zulfiqar A, Biadgo, Belete, Bijani, Ali, Bikbov, Bori, Bililign, Nigu, Bin Sayeed, Muhammad Shahdaat, Birlik, Sait Mente, Birungi, Charle, Biswas, Tuhin, Bizuneh, Hailemichael, Bleyer, Archie, Basara, Berrak Bora, Bosetti, Cristina, Boufous, Soufiane, Brady, Oliver J, Bragazzi, Nicola Luigi, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J K, Brenner, Hermann, Brewer, Jerry D, Briant, Paul Svitil, Britton, Gabrielle, Burstein, Roy, Busse, Reinhard, Butt, Zahid A, Cahuana-Hurtado, Lucero, Campos-Nonato, Ismael R, Campuzano Rincon, Julio Cesar, Cano, Jorge, Car, Mate, Cárdenas, Rosario, Carrero, Juan J, Carvalho, Félix, Castañeda-Orjuela, Carlos A, Castillo Rivas, Jacqueline, Castro, Franz, Catalá-López, Ferrán, Çavlin, Alanur, Cerin, Ester, Chalek, Julian, Chang, Hsing-Yi, Chang, Jung-Chen, Chattopadhyay, Aparajita, Chaturvedi, Pankaj, Chiang, Peggy Pei-Chia, Chin, Ken Lee, Chisumpa, Vesper Hichilombwe, Chitheer, Abdulaal, Choi, Jee-Young J, Chowdhury, Rajiv, Christopher, Devasahayam J, Cicuttini, Flavia M, Ciobanu, Liliana G, Cirillo, Massimo, Claro, Rafael M, Collado-Mateo, Daniel, Constantin, Maria-Magdalena, Conti, Sara, Cooper, Cyru, Cooper, Leslie Trumbull, Cornaby, Leslie, Cortesi, Paolo Angelo, Cortinovis, Monica, Costa, Megan, Cromwell, Elizabeth, Crowe, Christopher Stephen, Cukelj, Petra, Cunningham, Matthew, Daba, Alemneh Kabeta, Dachew, Berihun Assefa, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I, Daryani, Ahmad, Das Gupta, Rajat, Das Neves, José, Dasa, Tamirat Tesfaye, Dash, Aditya Prasad, Weaver, Nicole Davi, Davitoiu, Dragos Virgil, Davletov, Kairat, De Leo, Diego, De Neve, Jan-Walter, Degefa, Meaza Girma, Degenhardt, Louisa, Degfie, Tizta Tilahun, Deiparine, Selina, Demoz, Gebre Teklemariam, Demtsu, Balem, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolao, Des Jarlais, Don C, Dessie, Getenet Ayalew, Dharmaratne, Samath D, Dhimal, Meghnath, Dicker, Daniel, Ding, Eric L, Dinsa, Girmaye Deye, Djalalinia, Shirin, Do, Huyen Phuc, Dokova, Klara, Doku, David Teye, Dolan, Kate A, Doyle, Kerrie E, Driscoll, Tim R, Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Duraes, Andre R, Ebrahimpour, Soheil, Edvardsson, David, El Bcheraoui, Charbel, El-Khatib, Ziad, Elyazar, Iqbal Rf, Enayati, Ahmadali, Endries, Aman Yesuf, Ermakov, Sergey Petrovich, Eshrati, Babak, Eskandarieh, Sharareh, Esmaeili, Reza, Esteghamati, Alireza, Esteghamati, Sadaf, Estep, Kara, Fakhim, Hamed, Farag, Tamer, Faramarzi, Mahbobeh, Fareed, Mohammad, Farinha, Carla Sofia E Sá, Faro, Andre, Farvid, Maryam S, Farzadfar, Farshad, Farzaei, Mohammad Hosein, Fay, Kairsten A, Fazeli, Mir Sohail, Feigin, Valery L, Feigl, Andrea B, Feizy, Fariba, Fenny, Ama P, Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, Eduarda, Feyissa, Garumma Tolu, Filip, Irina, Finegold, Samuel, Fischer, Florian, Flor, Luisa Sorio, Foigt, Nataliya A, Foreman, Kyle J, Fornari, Carla, Fürst, Thoma, Fukumoto, Takeshi, Fuller, John E, Fullman, Nancy, Gakidou, Emmanuela, Gallus, Silvano, Gamkrelidze, Amiran, Ganji, Morsaleh, Gankpe, Fortune Gbetoho, Garcia, Gregory M, Garcia-Gordillo, Miguel Á, Gebre, Abadi Kahsu, Gebre, Teshome, Gebregergs, Gebremedhin Berhe, Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Gelano, Tilayie Feto, Gelaw, Yalemzewod Assefa, Geleijnse, Johanna M, Genova-Maleras, Ricard, Gething, Peter, Gezae, Kebede Embaye, Ghadami, Mohammad Rasoul, Ghadimi, Reza, Ghadiri, Keyghobad, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Ghiasvand, Hesam, Ghimire, Mamata, Ghoshal, Aloke Gopal, Gill, Paramjit Singh, Gill, Tiffany K, Giussani, Giorgia, Gnedovskaya, Elena V, Goli, Sriniva, Gomez, Ricardo Santiago, Gómez-Dantés, Hector, Gona, Philimon N, Goodridge, Amador, Gopalani, Sameer Vali, Goulart, Alessandra C, Goulart, Bárbara Niegia Garcia, Grada, Ayman, Grosso, Giuseppe, Gugnani, Harish Chander C, Guo, Jingwen, Guo, Yuming, Gupta, Prakash C, Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Haagsma, Juanita A, Hachinski, Vladimir, Hafezi-Nejad, Nima, Hagos, Tekleberhan B, Hailegiyorgis, Tewodros Tesfa, Hailu, Gessessew Bugssa, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hamadeh, Randah R, Hamidi, Samer, Handal, Alexis J, Hankey, Graeme J, Hao, Yuantao, Harb, Hilda L, Haririan, Hamidreza, Haro, Josep Maria, Hasan, Mehedi, Hassankhani, Hadi, Hassen, Hamid Yimam, Havmoeller, Rasmu, Hay, Simon I, He, Yihua, Hedayatizadeh-Omran, Akbar, Hegazy, Mohamed I, Heibati, Behzad, Heidari, Behnam, Hendrie, Delia, Henok, Andualem, Henry, Nathaniel J, Herteliu, Claudiu, Heydarpour, Fatemeh, Hibstu, Desalegn T, Hole, Michael K, Homaie Rad, Enayatollah, Hoogar, Praveen, Hosgood, H Dean, Hosseini, Seyed Mostafa, Hosseini Chavoshi, Meimanat M, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hsairi, Mohamed, Hsiao, Thoma, Hu, Guoqing, Huang, John J, Iburg, Kim Moesgaard, Igumbor, Ehimario U, Ikeda, Chad Thoma, Ilesanmi, Olayinka Stephen, Iqbal, Usman, Irenso, Asnake Ararsa, Irvani, Seyed Sina Naghibi, Isehunwa, Oluwaseyi Oluwakemi, Islam, Sheikh Mohammed Shariful, Jahangiry, Leila, Jahanmehr, Nader, Jain, Sudhir Kumar, Jakovljevic, Mihajlo, Jalu, Moti Tolera, James, Spencer L, Jassal, Simerjot K, Javanbakht, Mehdi, Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Ji, John S, Jonas, Jost B, Jozwiak, Jacek Jerzy, Jungari, Suresh Banayya, Jürisson, Mikk, Kabir, Zubair, Kadel, Rajendra, Kahsay, Amaha, Kalani, Rizwan, Kapil, Umesh, Karami, Manoochehr, Matin, Behzad Karami, Karch, André, Karema, Corine, Karimi, Seyed M, Kasaeian, Amir, Kassa, Dessalegn H, Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassa, Zemenu Yohanne, Kassebaum, Nicholas J, Kastor, Anshul, Katikireddi, Srinivasa Vittal, Kaul, Anil, Kawakami, Norito, Karyani, Ali Kazemi, Kebede, Seifu, Keiyoro, Peter Njenga, Kemp, Grant Rodger, Kengne, Andre Pascal, Keren, Andre, Kereselidze, Maia, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalid, Nauman, Khalil, Ibrahim A, Khan, Ejaz Ahmad, Khan, Muhammad Shahzeb, Khang, Young-Ho, Khanna, Tripti, Khater, Mona M, Khatony, Alireza, Khazaeipour, Zahra, Khazaie, Habibolah, Khoja, Abdullah T, Khosravi, Ardeshir, Khosravi, Mohammad Hossein, Kibret, Getiye D, Kidanemariam, Zelalem Teklemariam, Kiirithio, Daniel N, Kilgore, Paul Evan, Kim, Daniel, Kim, Jun Y, Kim, Young-Eun, Kim, Yun Jin, Kimokoti, Ruth W, Kinfu, Yohanne, Kinra, Sanjay, Kisa, Adnan, Kivimäki, Mika, Kochhar, Sonali, Kokubo, Yoshihiro, Kolola, Tufa, Kopec, Jacek A, Kosek, Margaret N, Kosen, Soewarta, Koul, Parvaiz A, Koyanagi, Ai, Krishan, Kewal, Krishnaswami, Sanjay, Krohn, Kristopher J, Defo, Barthelemy Kuate, Bicer, Burcu Kucuk, Kumar, G Anil, Kumar, Manasi, Kumar, Pushpendra, Kumsa, Fekede Asefa, Kutz, Michael J, Lad, Sheetal D, Lafranconi, Alessandra, Lal, Dharmesh Kumar, Lalloo, Ratilal, Lam, Hilton, Lami, Faris Hasan, Lang, Justin J, Lanksy, Sonia, Lansingh, Van C, Laryea, Dennis Odai, Lassi, Zohra S, Latifi, Arman, Laxmaiah, Avula, Lazarus, Jeffrey V, Lee, James B, Lee, Paul H, Leigh, Jame, Leshargie, Cheru Tesema, Leta, Samson, Levi, Miriam, Li, Shanshan, Li, Xiaohong, Li, Yichong, Liang, Juan, Liang, Xiaofeng, Liben, Misgan Legesse, Lim, Lee-Ling, Limenih, Miteku Andualem, Linn, Shai, Liu, Shiwei, Lorkowski, Stefan, Lotufo, Paulo A, Lozano, Rafael, Lunevicius, Raimunda, Mabika, Crispin Mabika, Macarayan, Erlyn Rachelle King, Mackay, Mark T, Madotto, Fabiana, Mahmood, Tarek Abd Elaziz, Mahotra, Narayan Bahadur, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malik, Manzoor Ahmad, Mamun, Abdullah A, Manamo, Wondimu Ayele, Manda, Ana-Laura, Mangalam, Srikanth, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Mapoma, Chabila Christopher, Marami, Dadi, Maravilla, Joemer C, Marcenes, Wagner, Marina, Shakhnazarova, Martins-Melo, Francisco Rogerlândio, März, Winfried, Marzan, Melvin B, Mashamba-Thompson, Tivani Phosa, Masiye, Felix, Mason-Jones, Amanda J, Massenburg, Benjamin Ballard, Mathur, Manu Raj, Maulik, Pallab K, Mazidi, Mohsen, McGrath, John J, Mehata, Suresh, Mehendale, Sanjay Madhav, Mehndiratta, Man Mohan, Mehrotra, Ravi, Mehrzadi, Saeed, Mehta, Kala M, Mehta, Varshil, Mekonnen, Tefera C, Meles, Hagazi Gebre, Meles, Kidanu Gebremariam, Melese, Addisu, Melku, Mulugeta, Memiah, Peter T N, Memish, Ziad A, Mendoza, Walter, Mengesha, Melkamu Merid, Mengistu, Desalegn Tadese, Mengistu, Getnet, Mensah, George A, Mereta, Seid Tiku, Meretoja, Atte, Meretoja, Tuomo J, Mestrovic, Tomislav, Mezgebe, Haftay Berhane, Miangotar, Yode, Miazgowski, Bartosz, Miazgowski, Tomasz, Miller, Ted R, Miller-Petrie, Molly Katherine, Mini, G.K., Mirabi, Parvaneh, Mirica, Andreea, Mirrakhimov, Erkin M, Misganaw, Awoke Temesgen, Moazen, Babak, Mohammad, Karzan Abdulmuhsin, Mohammadi, Moslem, Mohammadifard, Noushin, Mohammadi-Khanaposhtani, Maryam, Mohammed, Mohammed A, Mohammed, Shafiu, Mokdad, Ali H, Mola, Glen Dl, Molokhia, Mariam, Monasta, Lorenzo, Montañez, Julio Cesar, Moradi, Ghobad, Moradi, Mahmoudreza, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morgado-Da-Costa, Joana, Mori, Rintaro, Morrison, Shane Dougla, Mosapour, Abba, Moschos, Marilita M, Mousavi, Seyyed Meysam, Muche, Achenef Asmamaw, Muchie, Kindie Fentahun, Mueller, Ulrich Otto, Mukhopadhyay, Satinath, Muller, Kate, Murphy, Tasha B, Murthy, G.V.S., Musa, Jonah, Musa, Kamarul Imran, Mustafa, Ghulam, Muthupandian, Saravanan, Nachega, Jean B, Nagel, Gabriele, Naghavi, Mohsen, Naheed, Aliya, Nahvijou, Azin, Naik, Gurudatta, Naik, Paulami, Najafi, Farid, Naldi, Luigi, Nangia, Vinay, Nansseu, Jobert Richie, Nascimento, Bruno Ramo, Nawaz, Haseeb, Ncama, Busisiwe P, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Neupane, Suba, Newton, Charles Richard Jame, Ngalesoni, Frida N, Ngunjiri, Josephine W, Nguyen, Grant, Nguyen, Long Hoang, Nguyen, Trang Huyen, Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nisar, Muhammad Imran, Nixon, Molly R, Nomura, Shuhei, Noroozi, Mehdi, Noubiap, Jean Jacque, Nouri, Hamid Reza, Shiadeh, Malihe Nourollahpour, Nowroozi, Mohammad Reza, Nyandwi, Alypio, Nyasulu, Peter S, Odell, Christopher M, Ofori-Asenso, Richard, Ogah, Okechukwu Samuel, Ogbo, Felix Akpojene, Oh, In-Hwan, Okoro, Anselm, Oladimeji, Olanrewaju, Olagunju, Andrew T, Olagunju, Tinuke O, Olivares, Pedro R, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Sok King, Ortiz, Alberto, Osgood-Zimmerman, Aaron, Ota, Erika, Otieno, Brenda Achieng, Otstavnov, Stanislav S, Owolabi, Mayowa Ojo, Oyekale, Abayomi Samuel, P A, Mahesh, Pakhale, Smita, Pakhare, Abhijit P, Pana, Adrian, Panda, Basant Kumar, Panda-Jonas, Songhomitra, Pandey, Achyut Raj, Park, Eun-Kee, Parsian, Hadi, Patel, Shanti, Patil, Snehal T, Patle, Ajay, Patton, George C, Paturi, Vishnupriya Rao, Paudel, Deepak, Pedroso, Marcel Morae, Peprah, Emmanuel K, Pereira, David M, Perico, Norberto, Pesudovs, Konrad, Petri, William A, Petzold, Max, Pierce, Maxwell, Pigott, David M, Pillay, Julian David, Pirsaheb, Meghdad, Polanczyk, Guilherme V, Postma, Maarten J, Pourmalek, Farshad, Pourshams, Akram, Poustchi, Hossein, Prakash, Swayam, Prasad, Narayan, Purcell, Caroline A, Purwar, Manorama B, Qorbani, Mostafa, Quansah, Reginald, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Md Shafiur, Rahman, Mohammad Hifz Ur, Rahman, Muhammad Aziz, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Rajati, Fatemeh, Rajsic, Sasa, Ram, Usha, Ranabhat, Chhabi Lal, Ranjan, Prabhat, Rawaf, David Laith, Rawaf, Salman, Ray, Sarah E, Razo-García, Christian, Reiner, Robert C, Reis, Cesar, Remuzzi, Giuseppe, Renzaho, Andre M N, Resnikoff, Serge, Rezaei, Satar, Rezaeian, Shahab, Rezai, Mohammad Sadegh, Riahi, Seyed Mohammad, Rios-Blancas, Maria Jesu, Roba, Kedir Teji, Roberts, Nicholas L S, Roever, Leonardo, Ronfani, Luca, Roshandel, Gholamreza, Rostami, Ali, Rubagotti, Enrico, Ruhago, George Mugambage, Sabde, Yogesh Damodar, Sachdev, Perminder S, Saddik, Basema, Saeedi Moghaddam, Sahar, Safari, Hosein, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Safi, Sare, Safiri, Saeid, Sagar, Rajesh, Sahebkar, Amirhossein, Sahraian, Mohammad Ali, Sajadi, Haniye Sadat, Salahshoor, Mohamadreza, Salam, Nasir, Salama, Joseph S, Salamati, Payman, Saldanha, Raphael De Freita, Saleem, Zikria, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A, Salvi, Sundeep Santosh, Salz, Inbal, Sambala, Evanson Zondani, Samy, Abdallah M, Sanabria, Juan, Sanchez-Niño, Maria Dolore, Santos, Itamar S, Santric Milicevic, Milena M, Sao Jose, Bruno Piassi, Sardana, Mayank, Sarker, Abdur Razzaque, Sarmiento-Suárez, Rodrigo, Saroshe, Satish, Sarrafzadegan, Nizal, Sartorius, Benn, Sarvi, Shahabeddin, Sathian, Brijesh, Satpathy, Maheswar, Sawant, Arundhati R, Sawhney, Monika, Saxena, Sonia, Schaeffner, Elke, Schelonka, Kathryn, Schneider, Ione J C, Schwebel, David C, Schwendicke, Falk, Seedat, Soraya, Sekerija, Mario, Sepanlou, Sadaf G, Serván-Mori, Edson, Shabaninejad, Hosein, Shackelford, Katya Anne, Shafieesabet, Azadeh, Shaheen, Amira A, Shaikh, Masood Ali, Shakir, Raad A, Shams-Beyranvand, Mehran, Shamsi, Mohammadbagher, Shamsizadeh, Morteza, Sharafi, Heidar, Sharafi, Kiomar, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharma, Jayendra, Sharma, Rajesh, She, Jun, Sheikh, Aziz, Shi, Peilin, Shibuya, Kenji, Shigematsu, Mika, Shiri, Rahman, Shirkoohi, Reza, Shiue, Ivy, Shokraneh, Farhad, Shukla, Sharvari Rahul, Si, Si, Siabani, Soraya, Sibai, Abla Mehio, Siddiqi, Tariq J, Sigfusdottir, Inga Dora, Sigurvinsdottir, Rannveig, Silpakit, Nari, Silva, Diego Augusto Santo, Silva, João Pedro, Silveira, Dayane Gabriele Alve, Singam, Narayana Sarma Venkata, Singh, Jasvinder A, Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Sliwa, Karen, Soares Filho, Adauto Martin, Sobaih, Badr Hasan, Sobhani, Soheila, Soofi, Moslem, Soriano, Joan B, Soyiri, Ireneous N, Sreeramareddy, Chandrashekhar T, Starodubov, Vladimir I, Steiner, Caitlyn, Stewart, Leo G, Stokes, Mark A, Strong, Mark, Subart, Michelle L, Sufiyan, Mu'awiyyah Babale, Sulo, Gerhard, Sunguya, Bruno F, Sur, Patrick John, Sutradhar, Ipsita, Sykes, Bryan L, Sylaja, P.N., Sylte, Dillon O, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Tabb, Karen M, Tadakamadla, Santosh Kumar, Tandon, Nikhil, Tassew, Aberash Abay, Tassew, Segen Gebremeskel, Taveira, Nuno, Tawye, Nega Yimer, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekle, Merhawi Gebremedhin, Temsah, Mohamad-Hani, Terkawi, Abdullah Sulieman, Teshale, Manaye Yihune, Tessema, Belay, Teweldemedhin, Mebrahtu, Thakur, Jarnail Singh, Thankappan, Kavumpurathu Raman, Thirunavukkarasu, Sathish, Thomas, Nihal, Thomson, Alan J, Tilahun, Binyam, To, Quyen G, Tonelli, Marcello, Topor-Madry, Roman, Torre, Anna E, Tortajada-Girbés, Miguel, Tovani-Palone, Marcos Roberto, Toyoshima, Hideaki, Tran, Bach Xuan, Tran, Khanh Bao, Tripathy, Srikanth Prasad, Truelsen, Thomas Clement, Truong, Nu Thi, Tsadik, Afewerki Gebremeskel, Tsegay, Amanuel, Tsilimparis, Nikolao, Tudor Car, Lorainne, Ukwaja, Kingsley N, Ullah, Irfan, Usman, Muhammad Shariq, Uthman, Olalekan A, Uzun, Selen Begüm, Vaduganathan, Muthiah, Vaezi, Afsane, Vaidya, Gaurang, Valdez, Pascual R, Varavikova, Elena, Varughese, Santosh, Vasankari, Tommi Juhani, Vasconcelos, Ana Maria Nogale, Venketasubramanian, Narayanaswamy, Villafaina, Santo, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vujcic, Isidora S, Wagnew, Fasil Shiferaw, Waheed, Yasir, Walson, Judd L, Wang, Yanping, Wang, Yuan-Pang, Weiderpass, Elisabete, Weintraub, Robert G, Weldegwergs, Kidu Gidey, Werdecker, Andrea, Westerman, Ronny, Whiteford, Harvey, Widecka, Justyna, Widecka, Katarzyna, Wijeratne, Tissa, Winkler, Andrea Sylvia, Wiysonge, Charles Shey, Wolfe, Charles D A, Wu, Shouling, Wyper, Grant M A, Xu, Gelin, Yamada, Tomohide, Yano, Yuichiro, Yaseri, Mehdi, Yasin, Yasin Jemal, Ye, Pengpeng, Yentür, Gökalp Kadri, Yeshaneh, Alex, Yimer, Ebrahim M, Yip, Paul, Yisma, Engida, Yonemoto, Naohiro, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zadnik, Vesna, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zare, Zohreh, Zeleke, Mulugeta Molla, Zenebe, Zerihun Menlkalew, Zerfu, Taddese Alemu, Zhang, Xueying, Zhao, Xiu-Ju, Zhou, Maigeng, Zhu, Jun, Zimsen, Stephanie R M, Zodpey, Sanjay, Zoeckler, Leo, Lopez, Alan D, Lim, Stephen S, Murray, C, Callender, C, Kulikoff, X, Abate, K, Abay, S, Abebe, H, Abebo, T, Abejie, A, Abraha, H, Abreu, D, Abrham, A, Abu-Raddad, L, Abu-Rmeileh, N, Accrombessi, M, Adamu, A, Adebayo, O, Adedeji, I, Adetokunboh, O, Adhena, B, Adhikari, T, Adib, M, Adou, A, Adsuar, J, Agesa, K, Aghayan, S, Ahmed, M, Aichour, A, Aichour, M, Akbari, M, Akinyemi, R, Aleman, A, Alene, K, Aljunid, S, Al-Raddadi, R, Amare, A, Anber, N, Andrei, C, Animut, M, Ansha, M, Antonio, C, Appiah, S, Areri, H, Aryal, K, Asfaw, E, Asgedom, S, Atey, T, Avokpaho, E, Ayala Quintanilla, B, Ayuk, T, Babalola, T, Bali, A, Barker-Collo, S, Bärnighausen, T, Barrero, L, Baune, B, Baynes, H, Bekele, B, Belachew, A, Belay, A, Belay, S, Belay, Y, Bell, M, Bello, A, Bennett, D, Bensenor, I, Berman, A, Bertolacci, G, Bhutta, Z, Birlik, S, Basara, B, Brady, O, Bragazzi, N, Breitborde, N, Brewer, J, Butt, Z, Campos-Nonato, I, Campuzano Rincon, J, Carrero, J, Castañeda-Orjuela, C, Chang, H, Chang, J, Chiang, P, Chin, K, Chisumpa, V, Choi, J, Christopher, D, Cicuttini, F, Ciobanu, L, Claro, R, Constantin, M, Cooper, L, Cortesi, P, Daba, A, Dachew, B, Dargan, P, Dasa, T, Dash, A, Weaver, N, Davitoiu, D, De Neve, J, Degefa, M, Degfie, T, Demoz, G, Des Jarlais, D, Dessie, G, Dharmaratne, S, Ding, E, Dinsa, G, Do, H, Doku, D, Dolan, K, Doyle, K, Driscoll, T, Duken, E, Duraes, A, Elyazar, I, Endries, A, Ermakov, S, Farinha, C, Farzaei, M, Fay, K, Feigin, V, Feigl, A, Fenny, A, Fereshtehnejad, S, Feyissa, G, Foigt, N, Foreman, K, Fuller, J, Gankpe, F, Garcia, G, Garcia-Gordillo, M, Gebre, A, Gebregergs, G, Gebrehiwot, T, Gebremedhin, A, Gelano, T, Gelaw, Y, Geleijnse, J, Gezae, K, Ghadami, M, Ghoshal, A, Gill, T, Gnedovskaya, E, Gona, P, Gopalani, S, Goulart, A, Goulart, B, Gugnani, H, Gupta, P, Haagsma, J, Hagos, T, Hailegiyorgis, T, Hailu, G, Hamadeh, R, Handal, A, Hankey, G, Harb, H, Haro, J, Hassen, H, Hay, S, Hegazy, M, Henry, N, Hibstu, D, Hole, M, Hosgood, H, Hosseini, S, Hosseini Chavoshi, M, Huang, J, Iburg, K, Igumbor, E, Ikeda, C, Irenso, A, Irvani, S, Isehunwa, O, Islam, S, Jain, S, Jalu, M, James, S, Jassal, S, Jayatilleke, A, Jha, R, Jonas, J, Jozwiak, J, Jungari, S, Matin, B, Karimi, S, Kassa, D, Kassa, G, Kassa, T, Kassa, Z, Kassebaum, N, Katikireddi, S, Karyani, A, Keiyoro, P, Kemp, G, Kengne, A, Khafaie, M, Khalil, I, Khan, E, Khang, Y, Khater, M, Khoja, A, Khosravi, M, Kibret, G, Kidanemariam, Z, Kiirithio, D, Kilgore, P, Kim, J, Kim, Y, Kimokoti, R, Kopec, J, Kosek, M, Koul, P, Krohn, K, Defo, B, Bicer, B, Kumar, G, Kumsa, F, Kutz, M, Lad, S, Lal, D, Lami, F, Lang, J, Lansingh, V, Laryea, D, Lazarus, J, Lee, J, Lee, P, Leshargie, C, Liben, M, Lim, L, Limenih, M, Lotufo, P, Mabika, C, Macarayan, E, Mackay, M, Mahmood, T, Mahotra, N, Malik, M, Mamun, A, Manamo, W, Manda, A, Mansournia, M, Mantovani, L, Mapoma, C, Maravilla, J, Martins-Melo, F, Marzan, M, Mashamba-Thompson, T, Mason-Jones, A, Massenburg, B, Mathur, M, Maulik, P, Mcgrath, J, Mehendale, S, Mehndiratta, M, Mehta, K, Mekonnen, T, Meles, H, Meles, K, Memiah, P, Memish, Z, Mengesha, M, Mengistu, D, Mensah, G, Mereta, S, Meretoja, T, Mezgebe, H, Miller, T, Miller-Petrie, M, Mini, G, Mirrakhimov, E, Misganaw, A, Mohammad, K, Mohammed, M, Mokdad, A, Mola, G, Montañez, J, Morrison, S, Moschos, M, Mousavi, S, Muche, A, Muchie, K, Mueller, U, Murphy, T, Murthy, G, Musa, K, Nachega, J, Nansseu, J, Nascimento, B, Ncama, B, Negoi, R, Newton, C, Ngalesoni, F, Ngunjiri, J, Nguyen, L, Nguyen, T, Ningrum, D, Nirayo, Y, Nisar, M, Nixon, M, Noubiap, J, Nouri, H, Shiadeh, M, Nowroozi, M, Odell, C, Ogbo, F, Oh, I, Olagunju, A, Olagunju, T, Olivares, P, Olusanya, B, Olusanya, J, Ong, S, Otieno, B, Owolabi, M, Pakhare, A, Panda, B, Pandey, A, Park, E, Patil, S, Patton, G, Paturi, V, Pedroso, M, Peprah, E, Pereira, D, Petri, W, Pigott, D, Pillay, J, Polanczyk, G, Postma, M, Purcell, C, Purwar, M, Rahman, S, Rai, R, Ranabhat, C, Rawaf, D, Ray, S, Reiner, R, Renzaho, A, Riahi, S, Rios-Blancas, M, Roba, K, Roberts, N, Ruhago, G, Sabde, Y, Sahraian, M, Saldanha, R, Salomon, J, Sambala, E, Samy, A, Sanchez-Niño, M, Santric Milicevic, M, Sao Jose, B, Sarker, A, Sawant, A, Schneider, I, Schwebel, D, Sepanlou, S, Shackelford, K, Shaheen, A, Shaikh, M, Shakir, R, Shukla, S, Sibai, A, Siddiqi, T, Sigfusdottir, I, Silva, D, Silva, J, Silveira, D, Singam, N, Singh, J, Singh, N, Sinha, D, Soares Filho, A, Sobaih, B, Soriano, J, Soyiri, I, Sreeramareddy, C, Starodubov, V, Stewart, L, Stokes, M, Subart, M, Sufiyan, M, Sunguya, B, Sur, P, Sykes, B, Sylaja, P, Sylte, D, Szoeke, C, Tabb, K, Tadakamadla, S, Tassew, A, Tassew, S, Tawye, N, Tekalign, T, Tekle, M, Temsah, M, Teshale, M, Thankappan, K, Thomson, A, To, Q, Torre, A, Tovani-Palone, M, Tran, B, Tran, K, Tripathy, S, Truelsen, T, Truong, N, Tsadik, A, Ukwaja, K, Uthman, O, Uzun, S, Valdez, P, Vasankari, T, Vasconcelos, A, Vladimirov, S, Vollset, S, Walson, J, Weintraub, R, Weldegwergs, K, Wolfe, C, Wyper, G, Yasin, Y, Yentür, G, Yimer, E, Yoon, S, Younis, M, Zaman, S, Zeleke, M, Zenebe, Z, Zerfu, T, Zhao, X, Zimsen, S, Lopez, A, and Lim, S
Subjects: Adult, Male, Population Density, GBD, population, fertility, Adolescent, Medicine (all), Infant, Newborn, Infant, 11 Medical And Health Sciences, Middle Aged, Global Health, Global Burden of Disease, Young Adult, Child, Preschool, General & Internal Medicine, Female, Human medicine, Mortality, Child, Population Growth, Birth Rate, Aged, Human, Maternal Age
Abstract: Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10-54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10-14 years and 50-54 years was estimated from data on fertility in women aged 15-19 years and 45-49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-cotnponent method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings From 1950 to 2017, TFRs decreased by 49.4% (95% uncertainty interval [UI] 46.4-52.0). The TFR decreased from 4.7 livebirths (4.5-4.9) to 2.4 livebirths (2.2-2.5), and the ASFR of mothers aged 10-19 years decreased from 37 livebirths (34-40) to 22 livebirths (19-24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83.8 million people per year since 1985. The global population increased by 197-2% (193.3-200.8) since 1950, from 2.6 billion (2.5-2.6) to 7.6 billion (7.4-7.9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2.0%; this rate then remained nearly constant until 1970 and then decreased to 1.1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2.5% in 1963 to O7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2.7%. The global average age increased from 26.6 years in 1950 to 32.1 years in 2017, and the proportion of the population that is of working age (age 15-64 years) increased from 59.9% to 65.3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1.0 livebirths (95% UI 0. 9-1.2) in Cyprus to a high of 7.1 livebirths (6.8-7.4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0.08 livebirths (0.07-0.09) in South Korea to 2.4 livebirths (2.2-2.6) in Niger, and the TFR over age 30 years (I F030; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0.3 livebirths (0.3-0-4) in Puerto Rico to a high of 3.1 livebirths (3.0-3.2) in Niger. TF030 was higher than TFU25 in 145 countries and territories in 2017.33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2.0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.
Published: 2018

4. Anticipating influential factors on suicide outcomes through machine learning techniques: Insights from a suicide registration program in western Iran.

Author: Matinnia N, Alafchi B, Haddadi A, Ghaleiha A, Davari H, Karami M, Taslimi Z, Afkhami MR, and Yazdi-Ravandi S
Subjects: Humans, Iran epidemiology, Adult, Female, Male, Middle Aged, Young Adult, Adolescent, Suicide, Attempted statistics & numerical data, Aged, Risk Factors, Motivation, Age Factors, Machine Learning, Registries, Suicide statistics & numerical data
Abstract: Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Hamadan, Iran, where suicide rates have been on the rise. The research aims to predict factors influencing suicide outcomes by leveraging machine learning techniques on the Hamadan Suicide Registry Program data collected from 2016 to 2017. The study employs Naïve Bayes and Random Forest algorithms, comparing their performance to logistic regression. Results highlight the superiority of the Random Forest model. Based on the variable importance and multiple logistic regression analyses, the most important determinants of suicide outcomes were identified as suicide method, age, and timing of attempts, income, and motivation. The findings emphasize the cultural context's impact on suicide methods and underscore the importance of tailoring prevention programs to address specific risk factors, especially for older individuals. This study contributes valuable insights for suicide prevention efforts in the region, advocating for context-specific interventions and further research to refine predictive models and develop targeted prevention strategies., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)
Published: 2024
Full Text: View/download PDF

5. Cytomodulatory characteristics of Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF) against cypermethrin on skin fibroblast cells (HFF-1).

Author: Aghajanshakeri S, Ataee R, Karami M, Aghajanshakeri S, and Shokrzadeh M
Subjects: Granulocytes, Fibroblasts, Granulocyte-Macrophage Colony-Stimulating Factor pharmacology, Hematopoietic Stem Cells physiology
Abstract: The hematopoietic factor granulocyte macrophage-colony stimulating factor (GM-CSF) has been identified via its capacity to promote bone marrow progenitors' development and differentiation into granulocytes and macrophages. Extensive pre-clinical research has established its promise as a critical therapeutic target in an assortment of inflammatory and autoimmune disorders. Despite the broad literature on GM-CSF as hematopoietic of stem cells, the cyto/geno protective aspects remain unknown. This study aimed to assess the cyto/geno protective possessions of GM-CSF on cypermethrin-induced cellular toxicity on HFF-1 cells as an in vitro model. In pre-treatment culture, cells were exposed to various GM-CSF concentrations (5, 10, 20, and 40 ng/mL) with cypermethrin at IC 50 (5.13 ng/mL). Cytotoxicity, apoptotic rates, and genotoxicity were measured using the MTT, Annexin V-FITC/PI staining via flow-cytometry, and the comet assay. Cypermethrin at 5.13 ng/mL revealed cytotoxicity, apoptosis, oxidative stress, and genotoxicity while highlighting GM-CSF's protective properties on HFF-1. GM-CSF markedly attenuated cypermethrin-induced apoptotic cell death (early and late apoptotic rates). GM-CSF considerably regulated oxidative stress and genotoxicity by reducing the ROS and LPO levels, maintaining the status of GSH and activity of SOD, and suppressing genotoxicity in the comet assay parameters. Therefore, GM-CSF could be promising as an antioxidant, anti-apoptotic, genoprotective and cytomodulating agent., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)
Published: 2023
Full Text: View/download PDF

6. Gender and age differences in suicide attempt: A large population study in the West of Iran.

Author: Yazdi-Ravandi S, Khazaei S, Davari H, Matinnia N, Karami M, Taslimi Z, Afkhami MR, and Ghaleiha A
Subjects: Male, Female, Humans, Aged, Iran epidemiology, Sex Factors, Research Design, Risk Factors, Suicide, Attempted, Self-Injurious Behavior
Abstract: Present study was to evaluate the relationship between suicide attempt, gender and age. We used all of suicide attempt entered in Hamadan Suicide Registry Program (2016-2017). Finding revealed that suicide attempt was lower among elderly patients. Using poison and self-immolation was more common in elder patients. Suicide attempt in females against males was higher in married. In males the higher rate of suicide attempt was in autumn, while in females was in summer. Using of drug was more frequent in females, while self-harm was more common in males. Gender and age are important risk factors of suicide attempts., Competing Interests: Conflicts of interest The authors declare no conflicts of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)
Published: 2023
Full Text: View/download PDF

7. Effectiveness of COVID-19 vaccines on hospitalization and death in Guilan, Iran: a test-negative case-control study.

Author: Heidarzadeh A, Amini Moridani M, Khoshmanesh S, Kazemi S, Hajiaghabozorgi M, and Karami M
Subjects: Humans, Case-Control Studies, ChAdOx1 nCoV-19, Iran epidemiology, RNA-Directed DNA Polymerase, SARS-CoV-2, COVID-19 mortality, COVID-19 prevention & control, COVID-19 Vaccines therapeutic use, Hospitalization statistics & numerical data
Abstract: Objectives: The present study was conducted to estimate the effectiveness of (BBIBP)-CorV (Sinopharm), ChAdOx1-S/nCoV-19 (AZD1222, Oxford-AstraZeneca), rAd26-rAd5 (Gam-COVID-Vac, Sputnik V), and BIV1-CovIran (COVIran Barekat) and BBV152 COVAXIN (Bharat Biotech) vaccines against hospitalization and death of COVID-19 in Guilan Province of Iran from May 22 to December 21, 2021., Methods: This test-negative case-control study was conducted on the population aged 5 years and above by extracting information from local databases (The Medical Care Monitoring Center and The Integrated Health System). A logistic regression analysis was performed to estimate the effectiveness of the vaccines against COVID-19 hospitalization and death., Results: The total study population was 42,084, including 19,500 cases (with a positive Reverse Transcriptase-Polymerase Chain Reaction test admitted to hospitals in Guilan Province) and 22,586 controls (with a negative Reverse Transcriptase-Polymerase Chain Reaction test). Among the admitted patients, 1887 deaths occurred. The maximum effectiveness of BBIBP-CorV (Sinopharm) in preventing temporary hospitalization and regular hospitalization was observed 151 days after receiving the second dose, 95% (95% CI: 67-99.4%) and 85% (95% CI: 77-91%) respectively. The maximum effectiveness of the BBIBP-CorV (Sinopharm) vaccine 91-120 days after receiving the second dose against death was showed 56% (95% CI: 33-71%). The maximum effectiveness of ChAdOx1-S/nCoV-19 (AZD1222, Oxford-AstraZeneca) and BIV1-CovIran (COVIran Barekat) in preventing regular hospitalization and death was observed 121-150 and 61-90 days (respectively) after receiving the second dose, reaching 98% (95% CI: 94-99%) and 92% (95% CI: 48-99%), respectively for ChAdOx1-S/nCoV-19 and 95% (95% CI: 91-97%) and 89% (95% CI: 55-98%) respectively, for BIV1-CovIran., Conclusion: For almost all vaccines, the study observed an increase in effectiveness against hospitalization and death over time., (Copyright © 2022. Published by Elsevier Ltd.)
Published: 2023
Full Text: View/download PDF

8. An overview on role of nutrition on COVID-19 immunity: Accumulative review from available studies.

Author: Mohammadi AH, Behjati M, Karami M, Abari AH, Sobhani-Nasab A, Rourani HA, Hazrati E, Mirghazanfari SM, Hadi V, Hadi S, and Milajerdi A
Abstract: The novel coronavirus infection (COVID-19) conveys a serious global threat to health and economy. A common predisposing factor for development to serious progressive disease is presence of a low-grade inflammation, e.g., as seen in diabetes, metabolic syndrome, and heart failure. Micronutrient deficiencies may also contribute to the development of this state. Therefore, the aim of the present study is to explore the role of the nutrition to relieve progression of COVID-19. According PRISMA protocol, we conducted an online databases search including Scopus, PubMed, Google Scholar and web of science for published literatures in the era of COVID-19 Outbreak regarding to the status of nutrition and COVID-19 until December 2021. There were available studies (80 studies) providing direct evidence regarding the associations between the status of nutrition and COVID-19 infection. Adequate nutritional supply is essential for resistance against other viral infections and also for improvement of immune function and reduction of inflammation. Hence, it is suggested that nutritional intervention which secures an adequate status might protect against the novel coronavirus SARS-CoV-2 (Severe Acute Respiratory Syndrome - coronavirus-2) and mitigate its course. We also recommend initiation of adequate nutritional supplementation in high-risk areas and/or soon after the time of suspected infection with SARS-CoV-2. Subjects in high-risk groups should have high priority for applying this nutritive adjuvant therapy that should be started prior to administration of specific and supportive medical measures., (© 2022 Published by Elsevier Ltd on behalf of European Society for Clinical Nutrition and Metabolism.)
Published: 2023
Full Text: View/download PDF

9. Dispersive magnetic solid phase microextraction on microfluidic systems for extraction and determination of parabens.

Author: Farahmandi M, Yamini Y, Baharfar M, and Karami M
Subjects: Chromatography, High Pressure Liquid, Magnetic Phenomena, Microfluidics, Solid Phase Extraction, Parabens analysis, Solid Phase Microextraction
Abstract: In this study, a customized microfluidic system was utilized for magnetic solid phase extraction of parabens. For this sake, magnetite nanoparticles were synthesized and coated with polyaniline to enable efficient extraction and magnetic separation of sorbents particles. The synthesized particles were extensively characterized in terms of morphology, composition, and magnetic properties. The utilized microfluidic platform consisted of a relatively long spiral microchannel fabricated through laser-cutting and multi-layered assembly. To obtain an efficient dispersion, simultaneous flows of sample solution and magnetic beads dispersion were introduced to the chip with the aid of two syringe pumps. In order to increase the stability of the dispersed nanoparticles in the aqueous solution, various chemical and instrumental parameters were investigated and optimized. In this context, exploitation of hydrophobic surfactants and surface charge manipulation of the particles was shown to be a highly promising approach for effective dispersion and maintenance of magnetic beads in long microfluidic channels. Under the optimized conditions, the calibration curves were linear in the range of 5.0-1000.0 μg L -1 for propyl paraben and 8.0-1000.0 μg L -1 for methyl- and ethyl paraben with coefficients of determination greater than 0.992. Relative standard deviations were assessed as intra- and inter-day values which were less than 7.2% and the preconcentration factors in water were 10-15 for 100 μg L -1 of parabens in water. Finally, the method was applied for the extraction of parabens from fruit juice, sunscreen, and urine samples which showed favorable accuracy and precision., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)
Published: 2021
Full Text: View/download PDF

10. The relationship between chronic exposure to arsenic through drinking water and hearing function in exposed population aged 10-49 years: A cross-sectional study.

Author: Shokoohi R, Khazaei M, Karami M, Seidmohammadi A, Berijani N, Khotanlou H, and Torkshavand Z
Subjects: Adolescent, Adult, Arsenic toxicity, Auditory Threshold, Child, Cohort Studies, Cross-Sectional Studies, Drinking Water analysis, Female, Hearing Loss etiology, Humans, Logistic Models, Male, Middle Aged, Multivariate Analysis, Risk Factors, Smoking, Young Adult, Arsenic analysis, Drinking Water chemistry, Environmental Exposure statistics & numerical data, Hearing drug effects, Hearing Loss epidemiology, Water Pollution, Chemical statistics & numerical data
Abstract: It has been documented that arsenic has a potential risk to human health and identified as a risk factor for hearing impairment. However, there are few studies that confirm the ototoxic effect of arsenic, especially on the human auditory system. Therefore, the current study was conducted to investigate the correlation between auditory thresholds at different frequencies (0.25, 0.5, 1, 2, 4 and 8 kHz) and arsenic levels in drinking water samples. A total of 240 people, divided into two equal groups: exposed and reference, were selected for the auditory tests. It should be noted that, at frequencies from 0.25 to 1 kHz, no hearing loss was observed in the both groups. Based on the results, no significant correlations (p > 0.05) were found between hearing thresholds and confounding variables including gender and BMI. However, smoking and age are known to be the main variables for hearing loss in univariate regression analysis. In the case of age, the hearing loss risk in the older participants was increased compared with the younger participants (4 kHz (OR =1.09; 95% CI: 1.04, 1.13) and 8 kHz (OR =1.12; 95% CI: 1.06, 1.18)). Smoking habits had significant associations with hearing loss risk at 4 kHz (OR = 3.48; 95% CI: 1.47, 8.22) and 8 kHz (OR = 3.01; 95% CI: 1.14, 7.95). The multivariate regression analysis showed that age, smoking status, and exposure to arsenic were significantly associated with increased risk of hearing loss. Moreover, no statistically significant correlation (p˃0.05) was observed between arsenic exposure and hearing loss in the logistic regression model compared to the reference group. These outcomes suggest that further investigation and cohort studies with a larger number of participants should be conducted to find an association between arsenic exposure and hearing loss in general population., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2021
Full Text: View/download PDF

11. Phase distribution and risk assessment of PAHs in ambient air of Hamadan, Iran.

Author: Nadali A, Leili M, Bahrami A, Karami M, and Afkhami A
Subjects: Adult, Benzo(a)pyrene analysis, Carcinogens, Child, Cities, Coal, Environmental Monitoring, Gases, Humans, Iran, Mutagens, Particulate Matter analysis, Risk Assessment, Seasons, Wind, Air Pollutants analysis, Environmental Exposure statistics & numerical data, Polycyclic Aromatic Hydrocarbons analysis
Abstract: In the present study, both gaseous and particulate (PM with dae <2.5 µm) phases of polycyclic aromatic hydrocarbons (PAHs) were measured in the ambient air of Hamadan city, Iran. For this reason, two low-volume samplers equipped with glass fiber filters were used for sampling of particulate phase (N = 30) and XAD-2 sorbent tubes were applied for sampling gaseous phase of PAHs (N = 30). The sampling was conducted during warm and cold seasons in 2019. The average of cold/warm season ratios for Σ 16 PAH and PM concentrations were 1.14 and 0.62, respectively. Summed PAHs concentration were determined to be in the range 0.008-59.46 (mean: 11.61) ng/m 3 and 0.05-40.83 (mean: 10.22) ng/m 3 for the cold and warm seasons, respectively. A negative Pearson correlation coefficient was obtained for wind speed and relative humidity. The average Benzo (a) Pyrene equivalent carcinogenic (BaP eq ) levels in the cold season were lower than the maximum permissible risk level of 1 ng/m 3 for BaP. The BaP toxicity equivalency (ΣBaP TEQ ) and BaP mutagenicity equivalency (ΣBaP MEQ ) appeared to be significantly higher in the cold season (averaging 0.35 and 1.65 ng/m 3 , respectively) than those in warm season. Health risk assessment was performed for children and adults based on BaP eq , inhalation cancer risk. The diagnostic ratios of individual PAHs concentration showed that the significant sources of PAH emissions may be related to light duty vehicles (LDVs) in Hamadan. Although, some other sources such as pyrogenic source and petrol combustion were also suggested., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2021
Full Text: View/download PDF

12. GIS-based spatial modeling of snow avalanches using four novel ensemble models.

Author: Yariyan P, Avand M, Abbaspour RA, Karami M, and Tiefenbacher JP
Abstract: Snow avalanches can destroy lives and infrastructure and are very important phenomena in some regions of the world. This study maps snow avalanche susceptibility in Sirvan Watershed, Iran, using a new approach. Two statistical models - belief function (Bel) and probability density (PD) - are combined with two learning models - multi-layer perceptron (MLP) and logistic regression (LR) - to predict avalanche susceptibility using remote sensing data in a geographic information system (GIS). A snow avalanche inventory map was generated from Google Earth imagery, regional documentation, and field surveys. Of 101 avalanche locations, 71 (70%) were used to train the models and 30 (30%) were used to validate the resulting models. Fourteen snow avalanche conditioning factors were used as independent variables in the predictive modeling process. First, the weight of Bel and PD techniques were applied to each class of factors. Then, they were combined with two MLP and LR learning models for snow avalanche susceptibility mapping (SASM). The results were validated using positive predictive values, negative predictive values, sensitivity, specificity, accuracy, root-mean-square error, and area-under-the-curve (AUC) values. Thus, the AUCs for the PD-LR, Bel-LR, Bel-MLP, and PD-MLP hybrid models are 0.941, 0.936, 0.931 and 0.924, respectively. Based on the validation results, the PD-LR hybrid model achieved the best accuracy among the models. This hybrid modeling approach can provide accurate and reliable evaluations of snow avalanche-prone areas for management and decision making., Competing Interests: Declaration of competing interest The authors whose names are listed immediately below certify that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Peyman Yariyan(1)*, Mohammadtaghi Avand(2), Rahim Ali Abbaspour(3), Mohammadreza Karami(4), John P. Tiefenbacher(5)*, (Copyright © 2020 Elsevier B.V. All rights reserved.)
Published: 2020
Full Text: View/download PDF

13. Epidemiologic profile of nosocomial infections among paediatric patients in a referral hospital in Hamadan, west of Iran.

Author: Khazaei S, Adabi M, Bashirian S, Shojaeian M, Bathaei SJ, and Karami M
Abstract: Healthcare-associated infections (HC-AI) are major health problem with high financial impact. HC-AIs are one of the main causes of morbidity and mortality in paediatric hospitals. This study was performed to determine the epidemiology of HC-AIs in children admitted to medical wards of Besat Hospital in Hamadan, west of Iran. Data on cases of HC-AIs in paediatrics were collected from March 2017 to February 2018 in Besat Hospital. The medical records of eligible cases were extracted from Iranian Nosocomial Infections Surveillance Software. During the study period, a total of 355 HC-AIs in children were detected, 213 (60%) in boys and 214 (60.3%) in the 0-4-year age group. Of these, bloodstream infection was the most frequent infection in both age groups (37.38% in 0-4 years and 34.75% in 5-14 years). Escherichia coli was the common detected microorganism in girls (25.84% in those aged 0-4 years and 24.53% in 5-14 years), whereas Staphylococcus was more prevalent in boys (33.6% in those aged 0-4 years and 29.55% in 5-14 years). HC-AIs were more prevalent in burn, haematology and intensive care unit wards. In Besat Hospital, bloodstream infection and urinary tract infection were the most frequent infections among paediatric patients, and E. coli and Staphylococcus were the commonest detected microorganism in girls and boys respectively. Preventive activities should be targeted to reduce the rate of HC-AIs in wards associated with more contamination., (© 2020 The Author(s).)
Published: 2020
Full Text: View/download PDF

14. KRD-PACE Mobilization for Multiple Myeloma Patients With Significant Residual Disease Before Autologous Stem-Cell Transplantation.

Author: Cowan AJ, Green DJ, Karami M, Becker PS, Tuazon S, Coffey DG, Hyun TS, Libby EN, Gopal AK, and Holmberg LA
Subjects: Adult, Aged, Female, Humans, Male, Middle Aged, Multiple Myeloma complications, Retrospective Studies, Hematopoietic Stem Cell Transplantation methods, Multiple Myeloma therapy, Neoplasm, Residual therapy, Transplantation Conditioning methods, Transplantation, Autologous methods
Abstract: Background: Bortezomib has been incorporated into thalidomide and dexamethasone provided with cisplatin, doxorubicin, cyclophosphamide, and etoposide (PACE) as an intensive regimen before autologous stem-cell transplantation for multiple myeloma (MM). We examined MM patients at our center who received chemomobilization with a regimen that substituted carfilzomib and lenalidomide for bortezomib and thalidomide (KRD-PACE)., Patients and Methods: This was a retrospective study of 27 MM patients who received KRD-PACE for chemomobilization. Our analysis included patients who had circulating plasma cells (CPCs) by flow cytometry, ≥ 10% bone marrow plasma cells (BMPC), a monoclonal protein ≥ 1 g/dL, or an involved serum free light chain ≥ 10 mg/dL., Results: The most common indication for KRD-PACE was BMPC ≥ 10% in 16 patients (60%), followed by CPCs in 11 (41%). The median (range) age was 61 (35-69) years, and the median (range) BMPC before treatment was 10% (5%-47%). The overall response rate was 43%, and a median (range) of 20.24 (8.08-69.88) × 10 6 CD34 + cells/kg were collected. CPC clearance rate was 50%, and the median reduction in BMPC was 75%. Two patients had sinus bradycardia and 5 (19%) had neutropenic fever., Conclusion: KRD-PACE is an effective therapy to mobilize peripheral blood stem cells in MM patients with residual disease burden. This regimen was successful at clearing CPCs and reducing BMPC burden, with an overall response rate of 43%. Despite theoretical concern regarding the combination of 3 cardiotoxic agents, we observed a low frequency of cardiac issues., (Published by Elsevier Inc.)
Published: 2020
Full Text: View/download PDF

15. Saffron carotenoids change the superoxide dismutase activity in breast cancer: In vitro, in vivo and in silico studies.

Author: Hashemi SA, Karami M, and Bathaie SZ
Abstract: Superoxide dismutase (SOD) is an important member of the antioxidant defense system and is proposed as a therapeutic agent against the ROS-mediated diseases, and a therapeutic target for cancer treatment. Saffron carotenoids, crocin (Cro) and crocetin (Crt), are antioxidants with anticancer activity. In the present study, we investigated the effects of Cro/Crt on the SOD activity in both in vivo and in vitro models of breast cancer. Both Cro and Crt showed strong radical scavenging activity and SOD inhibition in the MCF-7 breast cancer cell line. The UVVis, circular dichroism and fluorometry studies proposed the binding of both Cro and Crt with SOD; the ΔG° of binding at 310 °K was -8.6 and -4.4 kcal/mol, respectively. The docking analysis predicted the Cro/Crt binding near the active site channel, but in different sites. According to the obtained data, Cro inhibits SOD activity by scavenging superoxide radical (O 2 ), while Crt inhibits SOD by affecting the copper-binding site. In contrast to the in vitro data, both Cro and Crt effectively increased SOD activity in breast tumors of BALB/c mice, after one month of treatment. The mechanism that is important to compensate for the SOD decreased activity in cancer., Competing Interests: Declaration of competing interest The authors claim that there is no conflict of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)
Published: 2020
Full Text: View/download PDF

16. On-disc electromembrane extraction-dispersive liquid-liquid microextraction: A fast and effective method for extraction and determination of ionic target analytes from complex biofluids by GC/MS.

Author: Karami M and Yamini Y
Subjects: Chromatography, Gas, Healthy Volunteers, Humans, Ions blood, Ions urine, Mass Spectrometry, Molecular Structure, Particle Size, Surface Properties, Antidepressive Agents, Tricyclic blood, Antidepressive Agents, Tricyclic urine, Body Fluids chemistry, Liquid Phase Microextraction
Abstract: In this study, an electromembrane extraction-dispersive liquid-liquid microextraction (EME-DLLME) was performed using a lab-on-a-disc device. It was used for sample microextraction, preconcentration, and quantitative determination of tricyclic antidepressants as model analytes in biofluids. The disc consisted of six extraction units for six parallel extractions. First, 100 μL of a biofluid was used to extract the analytes by the drop-to-drop EME to clean-up the sample. The extraction then was followed by applying the DLLME method to preconcentrate the analytes and make them ready for being analyzed by gas chromatography (GC). Implementing the EME-DLLME method on a chip device brought some significant advantages over the conventional methods, including saving space, cost, and materials as well as low sample and energy consumption. In the designed device, centrifugal force was used to move the fluids in the disc. Both sample preparation methods were performed on the same disc without manual transference of the donor phases for doing the two methods. Scalable centrifugal force made it possible to adjust the injection speed of the organic solvent into the aqueous solution in the DLLME step by changing the spin speed. Spin speed of 100 rpm was used in dispersion step and spin speed of 3500 rpm was used to sediment organic phase in DLLME step. The proposed device provides effective and reproducible extraction using a low volume of the sample solution. After optimization of the effective parameters, an EME-DLLME followed by GC-MS was performed for determination of amitriptyline and imipramine in saliva, urine, and blood plasma samples. The method provides extraction recoveries and preconcentration factors in the range of 43%-70.8% and 21.5-35.5 respectively. The detection limits less than 0.5 μg L -1 with the relative standard deviations of the analysis which were found in the range of 1.9%-3.5% (n = 5). The method is suitable for drug monitoring and analyzing biofluids containing low levels of the model analytes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)
Published: 2020
Full Text: View/download PDF

17. On-chip ion pair-based dispersive liquid-liquid extraction for quantitative determination of histamine H 2 receptor antagonist drugs in human urine.

Author: Karami M, Yamini Y, and Asl YA
Subjects: Chromatography, High Pressure Liquid, Humans, Liquid Phase Microextraction instrumentation, Polymethyl Methacrylate chemistry, Cimetidine urine, Histamine H2 Antagonists urine, Lab-On-A-Chip Devices, Liquid Phase Microextraction methods, Ranitidine urine
Abstract: In the present work, an ion-pair based dispersive liquid-liquid microextraction was performed on a centrifugal chip for the first time. The entire DLLME procedure, including flow direction, desperation, and sedimentation of the extracting phase, can be fulfilled automatically on a solitary chip. The chip was made of Poly(methyl methacrylate) (PMMA) and was of two units for two parallel extractions, each consisting of three chambers (for the sample solution, extracting solvents, and sedimentation). As the chip rotated, fluids flowed within the chip, and the dispersion, mixing, extraction, and sedimentation of the final phase were performed on the chip by simply adjusting the spin speed. Determination of two histamine H 2 receptor antagonist drugs, cimetidine and ranitidine, as the model analytes from the urine samples was done using the developed on-chip ion-pair based DLLME method followed by an HPLC-UV. The effective parameters on the extraction efficiency of the model analytes were investigated and optimized using the one variable at a time method. Under optimized conditions, the calibration curve was linear in the range of 15-2000 μg L -1 with a coefficient of determination (R 2 ) more than 0.9987. The relative standard deviations (RSD %) for extraction and determination of the analytes were less than 3.7% based on five replicated measurements. LODs less than 10.0 μg L -1 and preconcentration factors higher than 39-fold were obtained for both of the model analytes. The proposed chip enjoys the advantages of both the DLLME method and miniaturization on a centrifugal chip., (Copyright © 2019 Elsevier B.V. All rights reserved.)
Published: 2020
Full Text: View/download PDF

18. Assessing the role of Ca 2+ in skeletal muscle fatigue using a multi-scale continuum model.

Author: Karami M, Calvo B, Zohoor H, Firoozbakhsh K, and Grasa J
Subjects: Animals, Biomechanical Phenomena physiology, Isometric Contraction, Muscle Contraction, Rabbits, Calcium pharmacology, Models, Biological, Muscle Fatigue drug effects, Muscle, Skeletal physiology
Abstract: The Calcium ion Ca 2+ plays a critical role as an initiator and preserving agent of the cross-bridge cycle in the force generation of skeletal muscle. A new multi-scale chemo-mechanical model is presented in order to analyze the role of Ca 2+ in muscle fatigue and to predict fatigue behavior. To this end, a cross-bridge kinematic model was incorporated in a continuum based mechanical model, considering a thermodynamic compatible framework. The contractile velocity and the generated active force were directly related to the force-bearing states that were considered for the cross-bridge cycle. In order to determine the values of the model parameters, the output results of an isometric simulation were initially fitted with experimental data obtained for rabbit Extensor Digitorum Longus muscle. Furthermore, a simulated force-velocity curve under concentric contractions was compared with reported experimental results. Finally, by varying the Ca 2+ concentration level and its kinetics in the tissue, the model was able to predict the evolution of the active force of an experimental fatigue protocol. The good agreement observed between the simulated results and the experimental outcomes proves the ability of the model to reproduce the fatigue behavior and its applicability for more detailed multidisciplinary investigations related to chemical conditions in muscle performance., (Copyright © 2018 Elsevier Ltd. All rights reserved.)
Published: 2019
Full Text: View/download PDF

19. Safety and efficacy of allogenic placental mesenchymal stem cells for treating knee osteoarthritis: a pilot study.

Author: Khalifeh Soltani S, Forogh B, Ahmadbeigi N, Hadizadeh Kharazi H, Fallahzadeh K, Kashani L, Karami M, Kheyrollah Y, and Vasei M
Subjects: Adult, Aged, Arthrography, Double-Blind Method, Female, Follow-Up Studies, Humans, Injections, Intra-Articular, Male, Mesenchymal Stem Cell Transplantation adverse effects, Middle Aged, Pilot Projects, Pregnancy, Quality of Life, Range of Motion, Articular, Surveys and Questionnaires, Transplantation, Homologous, Treatment Outcome, Visual Analog Scale, Mesenchymal Stem Cell Transplantation methods, Osteoarthritis, Knee therapy, Placenta cytology
Abstract: Objective: Knee osteoarthritis (OA) is a common skeletal impairment that can cause many limitations in normal life activities. Stem cell therapy has been studied for decades for its regenerative potency in various diseases. We investigated the safety and efficacy of intra-articular injection of placental mesenchymal stem cells (MSCs) in knee OA healing., Methods: In this double-blind, placebo-controlled clinical trial, 20 patients with symptomatic knee OA were randomly divided into two groups to receive intra-articular injection of either 0.5-0.6 × 10 8 allogenic placenta-derived MSCs or normal saline. The visual analogue scale, Knee OA Outcome Score (KOOS) questionnaire, knee flexion range of motion (ROM) and magnetic resonance arthrography were evaluated for 24 weeks post-treatment. Blood laboratory tests were performed before and 2 weeks after treatment., Results: Four patients in the MSC group showed mild effusion and increased local pain, which resolved safely within 48-72 h. In 2 weeks post-injection there was no serious adverse effect and all of the laboratory test results were unchanged. Early after treatment, there was a significant knee ROM improvement and pain reduction (effect size, 1.4). Significant improvements were seen in quality of life, activity of daily living, sport/recreational activity and decreased OA symptoms in the MSC-injected group until 8 weeks (P < 0.05). These clinical improvements were also noted in 24 weeks post-treatment but were not statistically significant. Chondral thickness was improved in about 10% of the total knee joint area in the intervention group in 24 weeks (effect size, 0.3). There was no significant healing in the medial/lateral meniscus or anterior cruciate ligament. There was no internal organ impairment at 24 weeks follow-up., Conclusion: Single intra-articular allogenic placental MSC injection in knee OA is safe and can result in clinical improvements in 24 weeks follow-up., Trial Registration Number: IRCT2015101823298N., (Copyright © 2018. Published by Elsevier Inc.)
Published: 2019
Full Text: View/download PDF

20. Pollution and health risk assessment of heavy metals in agricultural soil, atmospheric dust and major food crops in Kermanshah province, Iran.

Author: Doabi SA, Karami M, Afyuni M, and Yeganeh M
Subjects: Adult, Agriculture, Child, Crops, Agricultural, Environmental Monitoring, Humans, Iran, Risk Assessment, Dust analysis, Food Contamination analysis, Metals, Heavy analysis, Soil Pollutants analysis, Triticum, Zea mays
Abstract: A total of 167 samples of agricultural soil, atmospheric dust and food crops (wheat and maize) were collected, and four heavy metals, including Zn, Cu, Ni, and Cr, were analyzed for their concentrations, pollution levels and human health risks. The mean heavy metal contents in the agricultural soil and atmospheric dust were exceeds background values and lower than their IEQS (Iranian Environmental Quality Standard) with an exception of Ni. A pollution assessment by Geo-accumulation Index (I geo ) showed that the pollution levels were in the order of Ni> Cu> Cr> Zn for agricultural soils and Ni> Cu> Zn> Cr for atmospheric dust. The Ni levels can be considered "moderately to heavily contaminated" status. The human health risk assessment indicated that non-carcinogenic values were below the threshold values (1), and main exposure pathway of heavy metals to both children and adults are ingestion. The carcinogenic risks values for Ni and Cr were higher than the safe value (1 × 10 -6 ), suggesting that all receptors (especially wheat) in Kermanshah province might have significant and acceptable potential health risk because of exposure to Ni and Cr. The carcinogenic risk for children and adults has a descending order of Ni> Cr, except for wheat. These results provide basic information on heavy metal contamination control and human health risk assessment management in the Kermanshah province., (Copyright © 2018 Elsevier Inc. All rights reserved.)
Published: 2018
Full Text: View/download PDF

21. Comment on "Accelerant-related burns and drug abuse: Challenging combination".

Author: Karami M and Khazaei S
Subjects: Child Abuse, Humans, Substance-Related Disorders, Burn Units, Burns
Published: 2018
Full Text: View/download PDF

22. Timely detection of influenza outbreaks in Iran: Evaluating the performance of the exponentially weighted moving average.

Author: Solgi M, Karami M, and Poorolajal J
Subjects: Area Under Curve, Epidemiological Monitoring, Humans, Influenza, Human virology, Iran epidemiology, Public Health, Sensitivity and Specificity, Algorithms, Disease Outbreaks prevention & control, Influenza, Human diagnosis, Influenza, Human epidemiology, Quality Indicators, Health Care statistics & numerical data
Abstract: Background: Real time detection of influenza outbreaks is necessary by public health authorities. The aim of this study was to determine the performance of the Exponentially Weighted Moving Average (EWMA) in detection of influenza outbreaks in Iran from January 2010 to December 2015., Methods: The EWMA algorithms were applied to weekly counts of suspected cases of influenza (influenza-like illnesses) to detect real outbreaks which have occurred in Iran from January 2010 to December 2015. The performance of EWMA algorithms was measured using sensitivity, specificity, false alarm rate, likelihood ratios and area under the receiver operating characteristics (ROC) curve., Results: Sensitivity of the EWMA for all of occurred outbreaks from 2010 to 2015 was 40% (95% CI: 29%, 50%). The corresponding value of detection of occurred outbreaks in 2010, 2011, 2013, 2014 and 2015 were 50%, 100%, 76%, 64% and 100% respectively. Among different algorithms, EWMA with λ=0.5 had the best performance (area under the Curve=100%) for the detection of occurred outbreaks in 2011., Conclusions: Our findings revealed that the performance of the EWMA in the real time detection influenza outbreak in Iran is appropriate. However, public health surveillance systems need to use different outbreak detection methods for detecting aberrations in influenza-like illnesses activity., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Published: 2018
Full Text: View/download PDF

23. Comment on "Alcohol and incident atrial fibrillation-A systematic review and meta-analysis".

Author: Khazaei S, Karami M, and Veisani Y
Subjects: Humans, Atrial Fibrillation, Ethanol
Published: 2018
Full Text: View/download PDF

24. Comment on "The influence of training characteristics on the effect of exercise training in patients with coronary artery disease: Systematic review and meta-regression analysis".

Author: Karami M and Khazaei S
Subjects: Exercise, Humans, Regression Analysis, Coronary Artery Disease, Exercise Therapy
Published: 2017
Full Text: View/download PDF

25. On-chip pulsed electromembrane extraction as a new concept for analysis of biological fluids in a small device.

Author: Karami M, Yamini Y, Abdossalami Asl Y, and Rezazadeh M
Subjects: Blood Chemical Analysis instrumentation, Codeine analysis, Electrodes, Humans, Limit of Detection, Membranes, Artificial, Naloxone analysis, Naltrexone analysis, Solvents chemistry, Urinalysis instrumentation, Blood Chemical Analysis methods, Electrochemistry, Liquid-Liquid Extraction, Urinalysis methods
Abstract: In the present work, an on-chip pulsed electromembrane extraction technique followed by HPLC-UV was developed for the analysis of codeine, naloxone and naltrexone as model analytes in biological fluids. The chip consisted of two channels for the introduction of the donor and acceptor phases. The channels were carved in two poly (methyl methacrylate) plates and a porous polypropylene membrane, which is impregnated by an organic solvent separating the two channels. Two platinum electrodes were mounted on the bottom of these channels and a pulsed electrical voltage was applied as an electrical driving force for the migration of ionized analytes from the sample solution through the porous sheet membrane into the acceptor phase. Using the pulsed voltage provided effective and reproducible extractions and could successfully overcome the disadvantages of applying constant voltages. Effective parameters of on-chip pulsed electromembrane extraction such as chemical composition of the organic solvent, applied voltage, pH of the donor and acceptor phases, flow rate and pulse duration were optimized using one-variable-at-a-time method. Under the optimized conditions, the model analytes were effectively extracted from different matrices and good linearity in the range of 10.0-500.0μgL -1 was achieved for calibration curves with coefficients of determinations (R 2 ) higher than 0.997. Extraction recoveries and%RSDs were obtained in the ranges of 28.6-32.9% and 2.15-3.8, respectively. Also, limits of detection were obtained in the ranges of 5-10μgL -1 and 2-5μgL -1 in plasma and urine samples, respectively., (Copyright © 2017 Elsevier B.V. All rights reserved.)
Published: 2017
Full Text: View/download PDF

26. Quantitative analysis of clonidine and ephedrine by a microfluidic system: On-chip electromembrane extraction followed by high performance liquid chromatography.

Author: Baharfar M, Yamini Y, Seidi S, and Karami M
Subjects: Electrochemical Techniques, Humans, Limit of Detection, Linear Models, Reproducibility of Results, Chromatography, High Pressure Liquid methods, Clonidine blood, Clonidine urine, Ephedrine blood, Ephedrine urine, Microfluidic Analytical Techniques methods
Abstract: In this work, a microfluidic device was developed for on-chip electromembrane extraction of trace amounts of ephedrine (EPH) and clonidine (CLO) in human urine and plasma samples followed by HPLC-UV analysis. Two polymethylmethacrylate plates were used as substrates and a microchannel was carved in each plate. The microchannel channel on the underneath plate provided the flow pass of the sample solution and the one on the upper plate dedicated to a compartment for the stagnant acceptor phase. A piece of polypropylene sheet was impregnated by an organic solvent and mounted between the two parts of the chip device. An electrical field, across the porous sheet, was created by two embedded platinum electrodes placed in the bottom of the channels which were connected to a power supply. The analytes were converted to their ionized form, passed through the supported liquid membrane, and then extracted into the acceptor phase by the applied voltage. All the effective parameters including the type of the SLM, the SLM composition, pH of donor and acceptor phases, and the quantity of the applied voltage were evaluated and optimized. Several organic solvents were evaluated as the SLM to assess the effect of SLM composition. Other parameters were optimized by a central composite design. Under the optimal conditions of voltage of 74V, flow rate of 28μLmin -1 , 100 and 20mM HCl as acceptor and donor phase composition, respectively, the calibration curves were plotted for both analytes. The limits of detection were less than 7.0 and 11μgL -1 in urine and plasma, respectively. The linear dynamic ranges were within the range of 10-450 and 25-500μgL -1 (r 2 ˃0.9969) for CLO, and within the range of 20-450 and 30-500μgL -1 (r 2 ˃0.9907) for EPH in urine and plasma, respectively. To examine the capability of the method, real biological samples were analyzed. The results represented a high accuracy in the quantitative analysis of the analytes with relative recoveries within the range of 94.6-105.2% and acceptable repeatability with relative standard deviations lower than 5.1%., (Copyright © 2017 Elsevier B.V. All rights reserved.)
Published: 2017
Full Text: View/download PDF

27. Klotho gene expression decreases in peripheral blood mononuclear cells (PBMCs) of patients with relapsing-remitting multiple sclerosis.

Author: Karami M, Mehrabi F, Allameh A, Pahlevan Kakhki M, Amiri M, and Emami Aleagha MS
Subjects: Adult, Case-Control Studies, Female, Gene Expression, Humans, Klotho Proteins, Male, RNA, Messenger metabolism, Real-Time Polymerase Chain Reaction, Retrospective Studies, Glucuronidase metabolism, Leukocytes, Mononuclear metabolism, Multiple Sclerosis, Relapsing-Remitting metabolism
Abstract: Background: we recently showed that a hypothesized anti-aging and anti-inflammatory protein, namely Klotho, may contribute to the etiology and/or pathogenesis of multiple sclerosis (MS). In addition, Klotho function and its gene expression are dependent on inflammatory pathways. Accordingly, the aim of this study was to investigate the Klotho gene expression within peripheral blood mononuclear cells (PBMCs) of patients with MS., Methods: Altogether, 30 patients with relapsing-remitting MS (RRMS) along with 30 age and sex-matched healthy individuals were enrolled in this study. Blood samples were obtained from all participants and then PBMCs were isolated. The quantitative Real-Time PCR was carried out for Klotho mRNA derived from PBMCs., Results: The results showed that klotho gene expression in the PBMCs of patients with RRMS is nearly 2.5-fold less than healthy individuals (P=0.0006)., Conclusion: This is the first study demonstrating a possible role of Klotho in the PBMCs of MS patients., (Copyright © 2017 Elsevier B.V. All rights reserved.)
Published: 2017
Full Text: View/download PDF

28. Epidemiological characteristics of human brucellosis in Hamadan Province during 2009-2015: results from the National Notifiable Diseases Surveillance System.

Author: Nematollahi S, Ayubi E, Karami M, Khazaei S, Shojaeian M, Zamani R, Mansori K, and Gholamaliee B
Subjects: Adolescent, Adult, Age Factors, Animals, Child, Child, Preschool, Female, Humans, Incidence, Infant, Infant, Newborn, Iran epidemiology, Logistic Models, Male, Middle Aged, Public Health, Recurrence, Risk Factors, Seasons, Young Adult, Brucellosis epidemiology
Abstract: Background: Human brucellosis and recurrent brucellosis is an ever-increasing public health concern, especially in endemic areas like Iran. Nevertheless, little is known regarding the epidemiology and determinants of recurrent brucellosis. Therefore, the objective of this study was to investigate epidemiological patterns and potential determinants of recurrent brucellosis in Hamadan Province during the years 2009-2015., Methods: Data on reported cases of new and recurrent brucellosis from 2009 to 2015 were obtained from the provincial Notifiable Diseases Surveillance System at Hamadan University of Medical Sciences. Incidence rates per 100000 were estimated at the county level. Binary logistic regression was used to estimate the effects of background characteristics and recurrent brucellosis. The power of discrimination of the model for recurrent brucellosis was assessed using the area under the curve (AUC)., Results: Among 7318 brucellosis cases, the total frequency (%) of recurrent cases was 472 (6.45%). The rate of recurrent brucellosis was higher in females, people aged 50 years and over, people with a history of consuming unpasteurized dairy products with no history of contact with animals, and in the winter season. Multivariable logistic regression analysis showed that female sex (adjusted odds ratio (AOR) 1.36, 95% confidence interval (CI) 1.13-1.65), age ≥55 years (AOR 4.15, 95% CI 2.32-7.42), consumption of unpasteurized dairy products (AOR 1.16, 95% CI 0.96-1.40), and winter season (AOR 1.32, 95% CI 1.03-1.71) are potential risk factors for recurrent brucellosis. The final model that involved all the determinants showed moderate discrimination (AUC 0.61)., Conclusions: Female sex, older age, and winter months were found to be significant determinants of recurrent human brucellosis. Enhanced surveillance systems with an emphasis on these population characteristics will allow effective preventive and protective measures to be implemented and might alleviate the recurrence of brucellosis in the country., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Published: 2017
Full Text: View/download PDF

29. Validating the IBIS and BOADICEA Models for Predicting Breast Cancer Risk in the Iranian Population.

Author: Ghoncheh M, Ziaee F, Karami M, and Poorolajal J
Subjects: Adult, Aged, Algorithms, Breast Neoplasms epidemiology, Case-Control Studies, Female, Follow-Up Studies, Humans, Incidence, Iran epidemiology, Middle Aged, Prognosis, Young Adult, Breast Neoplasms diagnosis, Breast Neoplasms etiology, Genetic Predisposition to Disease, Models, Statistical, Risk Assessment
Abstract: Background: Several approaches have been suggested for incorporating risk factors to predict the future risk of breast cancer. The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and International Breast Cancer Intervention Study (IBIS) are among these approaches. We compared the performance of these models in discriminating between cases and noncases in the Iranian population., Patients and Methods: We performed a case-control study in Tehran, from November 2015 to April 2016, and enrolled 1633 women aged 24 to 75 years, including 506 cases of breast cancer, 916 population-based controls, and 211 clinic-based controls. We calculated and compared the risk of breast cancer predicted by the IBIS and BOADICEA models and the logistic regression model. For model discrimination, we computed the area under the receiver operating characteristic (ROC) curve., Results: The risk of breast cancer predicted by the IBIS model was higher than the BOADICEA model, but lower than the logistic model. The area under the ROC plots indicated that the logistic regression model showed better discrimination between cases and noncases (71.53%) compared with the IBIS model (49.36%) and BOADICEA model (35.84%). Based on the Pierson correlation coefficient, the correlation between IBIS and BOADICEA models was much stronger than the correlation between IBIS and logistic models (0.3884 and 0.1639, respectively)., Conclusion: The IBIS model discriminated cases and noncases better than the BOADICEA model in the Iranian population. However, the discrimination of the logistic regression model, which included a larger array of familial, genetic, and personal risk factors, was better than the 2 models., (Copyright © 2017 Elsevier Inc. All rights reserved.)
Published: 2017
Full Text: View/download PDF

30. Combined virtual screening, MMPBSA, molecular docking and dynamics studies against deadly anthrax: An in silico effort to inhibit Bacillus anthracis nucleoside hydrolase.

Author: Karami M, Jalali C, and Mirzaie S
Subjects: Anthrax drug therapy, Bacillus anthracis drug effects, Bacterial Proteins antagonists & inhibitors, Catalytic Domain, Drug Design, Humans, Molecular Docking Simulation, Molecular Dynamics Simulation, Anthrax prevention & control, Bacillus anthracis enzymology, Computer Simulation, Models, Molecular, N-Glycosyl Hydrolases antagonists & inhibitors
Abstract: Anthrax is a deadly disease caused by Bacillus anthracis, a dangerous biological warfare agent employed for both military and terrorist purposes. A critical selective target for chemotherapy against this disease is nucleoside hydrolase (NH), an enzyme still not found in mammals. In the current study, we have performed molecular docking and dynamics studies, aiming to propose the new potent inhibitors of B. anthracis NH among National Cancer Institute (NCI) Diversity Set. We also analyzed the principal interactions of proposed compounds with the active site residues of NH and the relevant factors to biological activity. Additionally, the physic-chemical properties of free and inhibitor bound NH were evaluated and discussed. Our data showed that compound NSC79887 is a good candidate to inhibit NH and also for biological tests and further development. Also, ADMET prediction revealed that all physic-chemical parameters are within the acceptable range defined for human use., (Copyright © 2017 Elsevier Ltd. All rights reserved.)
Published: 2017
Full Text: View/download PDF

31. 3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction.

Author: Khaji E, Karami M, and Garkani-Nejad Z
Subjects: Predictive Value of Tests, Protein Structure, Tertiary, Algorithms, Proteins chemistry, Proteins genetics, Sequence Analysis, Protein methods
Abstract: Predicting the native structure of proteins based on half-sphere exposure and contact numbers has been studied deeply within recent years. Online predictors of these vectors and secondary structures of amino acids sequences have made it possible to design a function for the folding process. By choosing variant structures and directs for each secondary structure, a random conformation can be generated, and a potential function can then be assigned. Minimizing the potential function utilizing meta-heuristic algorithms is the final step of finding the native structure of a given amino acid sequence. In this work, Imperialist Competitive algorithm was used in order to accelerate the process of minimization. Moreover, we applied an adaptive procedure to apply revolutionary changes. Finally, we considered a more accurate tool for prediction of secondary structure. The results of the computational experiments on standard benchmark show the superiority of the new algorithm over the previous methods with similar potential function., (Copyright © 2015 Elsevier Ltd. All rights reserved.)
Published: 2016
Full Text: View/download PDF

32. Analysis of ammonia separation from purge gases in microporous hollow fiber membrane contactors.

Author: Karami MR, Keshavarz P, Khorram M, and Mehdipour M
Subjects: Absorption, Air Pollutants chemistry, Carbon Dioxide chemistry, Chemical Industry, Diffusion, Gases, Microscopy, Electron, Scanning, Polymers chemistry, Polypropylenes chemistry, Porosity, Solubility, Water chemistry, Ammonia chemistry, Membranes, Artificial, Models, Theoretical
Abstract: In this study, a mathematical model was developed to analyze the separation of ammonia from the purge gas of ammonia plants using microporous hollow fiber membrane contactors. A numerical procedure was proposed to solve the simultaneous linear and non linear partial differential equations in the liquid, membrane and gas phases for non-wetted or partially wetted conditions. An equation of state was applied in the model instead of Henry's law because of high solubility of ammonia in water. The experimental data of CO₂-water system in the literature was used to validate the model due to the lack of data for ammonia-water system. The model showed that the membrane contactor can separate ammonia very effectively and with recoveries higher than 99%. SEM images demonstrated that ammonia caused some micro-cracks on the surfaces of polypropylene fibers, which could be an indication of partial wetting of membrane in long term applications. However, the model results revealed that the membrane wetting did not have significant effect on the absorption of ammonia because of very high solubility of ammonia in water. It was also found that the effect of gas velocity on the absorption flux was much more than the effect of liquid velocity., (Copyright © 2013 Elsevier B.V. All rights reserved.)
Published: 2013
Full Text: View/download PDF

33. The effect of palm oil or canola oil on feedlot performance, plasma and tissue fatty acid profile and meat quality in goats.

Author: Karami M, Ponnampalam EN, and Hopkins DL
Subjects: Animal Nutritional Physiological Phenomena, Animals, Body Composition drug effects, Diet, Dietary Supplements, Fatty Acids chemistry, Fatty Acids metabolism, Fatty Acids, Monounsaturated chemistry, Fatty Acids, Monounsaturated pharmacology, Goats growth & development, Goats physiology, Lipid Peroxidation, Male, Muscle, Skeletal chemistry, Muscle, Skeletal metabolism, Palm Oil, Plant Oils chemistry, Rapeseed Oil, Animal Feed analysis, Fatty Acids blood, Meat standards, Plant Oils pharmacology
Abstract: Twenty-four entire male Kacang kid goats were fed diets containing 3% canola (n=12) or palm oil (n=12) supplements for 16 weeks. The goats had an initial live weight of 14.2±1.46 kg and were fed a mixed ration ad libitum (10.4 MJ/ME and 14% crude protein). There was no difference in feedlot performance due to diet. Inclusion of canola oil reduced (P<0.05) kidney fat weight and increased (P<0.05) linolenic acid (18:3n-3) concentration in the blood plasma, m. longissimus lumborum (LL), liver, and kidney. The palm oil diet increased (P<0.05) myristic (14:0) and palmitic (16:0) acid content in the blood, but this did not alter these fatty acids in the LL muscle. Lipid oxidative substances in the liver and LL from palm oil fed kids were higher (P<0.05) than those from canola supplemented kids. The incorporation of canola oil into the goats' diet increased muscle omega-3 fatty acid content, but lipid oxidation was lowered in the blood and muscle LL., (Copyright © 2013 Elsevier Ltd. All rights reserved.)
Published: 2013
Full Text: View/download PDF

34. Concern about interpretation of the data.

Author: Karami M
Subjects: Female, Humans, Male, Airway Remodeling, Malocclusion therapy, Maxilla anatomy & histology, Nasopharynx anatomy & histology, Palatal Expansion Technique
Published: 2013
Full Text: View/download PDF

35. Effects of dietary antioxidants on the quality, fatty acid profile, and lipid oxidation of longissimus muscle in Kacang goat with aging time.

Author: Karami M, Alimon AR, Sazili AQ, Goh YM, and Ivan M
Subjects: Andrographis chemistry, Animal Feed, Animals, Curcuma chemistry, Diet veterinary, Dietary Supplements, Goats, Male, Muscle, Skeletal metabolism, Palm Oil, Pigmentation, Plant Oils administration & dosage, Thiobarbituric Acid Reactive Substances metabolism, Animal Nutritional Physiological Phenomena, Antioxidants administration & dosage, Fatty Acids metabolism, Lipid Metabolism, Meat analysis, Vitamin E administration & dosage
Abstract: Thirty-two male goats were randomly assigned to four dietary treatments namely, basal diet 70% concentrate and 30% oil palm fronds (control, CN), CN + 400 mg/kg vitamin E (VE), 0.5% turmeric (TU) or 0.5% Anderographis paniculata (AP). After 100 days of feeding, the goats were slaughtered and longissimus dorsi (LD) muscle was sampled. The muscle was vacuum-packaged and conditioned for 0, 7 and 14 days in a chiller (4 °C). The drip loss of the LD muscle increased (P < 0.05) with aging time. Meat tenderness was improved (p < 0.05) at 14 days aging. All antioxidant supplements improved (P < 0.05) colour of the meat. The TBARS value increased (P < 0.05) at 7 days of aging while the fatty acid composition was not affected by the dietary supplements. It is concluded that TU and AP are potential dietary antioxidant supplements, for the purpose of improving the quality of chevon., (Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.)
Published: 2011
Full Text: View/download PDF

36. H-point standard addition method applied to simultaneous kinetic determination of antimony(III) and antimony(V) by adsorptive linear sweep voltammetry.

Author: Zarei K, Atabati M, and Karami M
Subjects: Adsorption, Catalysis, Electrochemistry, Indicators and Reagents, Kinetics, Polarography, Pyrogallol chemistry, Solutions, Water Pollutants, Chemical analysis, Water Supply analysis, Antimony analysis, Antimony chemistry
Abstract: In this work, the applicability of H-point standard addition method (HPSAM) to the kinetic voltammetry data is verified. For this purpose, a procedure is described for the determination of Sb(III) and Sb(V) by adsorptive linear sweep voltammetry using pyrogallol as a complexing agent. The method is based on the differences between the rate of complexation of pyrogallol with Sb(V) and Sb(III) at pH 1.2. The results show that the H-point standard addition method is suitable for the speciation of antimony. Sb(III) and Sb(V) can be determined in the ranges of 0.003-0.120 and 0.010-0.240 microg mL(-1), respectively. Moreover, the solution is analyzed for any possible effects of foreign ions. The obtained results show that the HPSAM in combination to electroanalytical techniques is a powerful method with high sensitivity and selectivity. The procedure is successfully applied to the speciation of antimony in water samples., (2010 Elsevier B.V. All rights reserved.)
Published: 2010
Full Text: View/download PDF

37. Mean centering of ratio kinetic profiles for the simultaneous kinetic determination of binary mixtures in electroanalytical methods.

Author: Zarei K, Atabati M, and Karami M
Abstract: In this work, the applicability of mean centering (MC) of ratio kinetic profiles method to the kinetic voltammetry data is verified. For this purpose, a procedure is described for the determination of Sb(III) and Sb(V) by adsorptive linear sweep voltammetry using pyrogallol (py) as a complexing agent. The method is based on the differences between the rate of complexation of pyrogallol with Sb(V) and Sb(III) at pH 1.2. The results show that the mean centering of ratio kinetic profiles method is suitable for the speciation of antimony. Sb(III) and Sb(V) can be determined in the ranges of 3.0-120.0 and 10.0-240.0 ng mL(-1), respectively. Moreover, the solution is analyzed for any possible effects of foreign ions. The obtained results show that the method of MC in combination to electroanalytical techniques is a powerful method with high sensitivity and selectivity. The procedure is successfully applied to the speciation of antimony in pharmaceutical preparations.
Published: 2009
Full Text: View/download PDF

38. Role of NKG2D signaling in the cytotoxicity of activated and expanded CD8+ T cells.

Author: Verneris MR, Karimi M, Baker J, Jayaswal A, and Negrin RS
Subjects: Base Sequence, CD8-Positive T-Lymphocytes drug effects, Cell Line, Cytotoxicity, Immunologic, Humans, In Vitro Techniques, Interleukin-2 pharmacology, Lymphocyte Activation, Membrane Proteins metabolism, NK Cell Lectin-Like Receptor Subfamily K, RNA, Small Interfering genetics, Receptors, Immunologic genetics, Receptors, Natural Killer Cell, Signal Transduction, CD8-Positive T-Lymphocytes immunology, CD8-Positive T-Lymphocytes metabolism, Receptors, Immunologic metabolism
Abstract: Activating and expanding T cells using T-cell receptor (TCR) cross-linking antibodies and interleukin 2 (IL-2) results in potent cytotoxic effector cells capable of recognizing a broad range of malignant cell targets, including autologous leukemic cells. The mechanism of target cell recognition has previously been unknown. Recent studies show that ligation of NKG2D on natural killer (NK) cells directly induces cytotoxicity, whereas on T cells it costimulates TCR signaling. Here we demonstrate that NKG2D expression is up-regulated upon activation and expansion of human CD8+ T cells. Antibody blocking, redirected cytolysis, and small interfering RNA (siRNA) studies using purified CD8+ T cells demonstrate that cytotoxicity against malignant target cells occurs through NKG2D-mediated recognition and signaling and not through the TCR. Activated and expanded CD8+ T cells develop cytotoxicity after 10 to 14 days of culture, coincident with the expression of the adapter protein DAP10. T cells activated and expanded in low (30 U/mL) and high (300 U/mL) concentrations of IL-2 both up-regulated NKG2D expression equally, but only cells cultured in high-dose IL-2 expressed DAP10 and were cytotoxic. Collectively these results establish that NKG2D triggering accounts for the majority of major histocompatibility complex (MHC)-unrestricted cytotoxicity of activated and expanded CD8+ T cells, likely through DAP10-mediated signaling.
Published: 2004
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Region

Database

38 results on '"Karami M"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources