Author: "Vahabi SM" - Searchworks@Jio Institute Digital Library Search Results

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1. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Naghavi, M, Ong, KL, Aali, A, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbasi-Kangevari, M, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdelwahab, A, Abdollahi, M, Abdollahifar, M-A, Abdoun, M, Abdulah, DM, Abdullahi, A, Abebe, M, Abebe, SS, Abedi, A, Abegaz, KH, Abhilash, ES, Abidi, H, Abiodun, O, Aboagye, RG, Abolhassani, H, Abolmaali, M, Abouzid, M, Aboye, GB, Abreu, LG, Abrha, WA, Abtahi, D, Abu Rumeileh, S, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Abu-Rmeileh, NME, Aburuz, S, Abu-Zaid, A, Accrombessi, MMK, Adal, TG, Adamu, AA, Addo, IY, Addolorato, G, Adebiyi, AO, Adekanmbi, V, Adepoju, AV, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adeyinka, DA, Adeyomoye, OI, Admass, BAA, Adnani, QES, Adra, S, Afolabi, AA, Afzal, MS, Afzal, S, Agampodi, SB, Agasthi, P, Aggarwal, M, Aghamiri, S, Agide, FD, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmad, T, Ahmadi, K, Ahmadzade, AM, Ahmed, A, Ahmed, H, Ahmed, LA, Ahmed, MS, Ahmed, MB, Ahmed, SA, Ajami, M, Aji, B, Akara, EM, Akbarialiabad, H, Akinosoglou, K, Akinyemiju, T, Akkaif, MA, Akyirem, S, Al Hamad, H, Al Hasan, SM, Alahdab, F, Alalalmeh, SO, Alalwan, TA, Al-Aly, Z, Alam, K, Alam, M, Alam, N, Al-amer, RM, Alanezi, FM, Alanzi, TM, Al-Azzam, S, Albakri, A, Albashtawy, M, AlBataineh, MT, Alcalde-Rabanal, JE, Aldawsari, KA, Aldhaleei, WA, Aldridge, RW, Alema, HB, Alemayohu, MA, Alemi, S, Alemu, YM, Al-Gheethi, AAS, Alhabib, KF, Alhalaiqa, FAN, Al-Hanawi, MK, Ali, A, Ali, L, Ali, MU, Ali, R, Ali, S, Ali, SSS, Alicandro, G, Alif, SM, Alikhani, R, Alimohamadi, Y, Aliyi, AA, Aljasir, MAM, Aljunid, SM, Alla, F, Allebeck, P, Al-Marwani, S, Al-Maweri, SAA, Almazan, JU, Al-Mekhlafi, HM, Almidani, L, Almidani, O, Alomari, MA, Al-Omari, B, Alonso, J, Alqahtani, JS, Alqalyoobi, S, Alqutaibi, AY, Al-Sabah, SK, Altaany, Z, Altaf, A, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Alwafi, H, Al-Worafi, YM, Aly, H, Aly, S, Alzoubi, KH, Amani, R, Amare, AT, Amegbor, PM, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, MH, Amu, H, Amugsi, DA, Amusa, GA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Andrei, T, Angus, C, Anil, A, Anil, S, Anoushiravani, A, Ansari, H, Ansariadi, A, Ansari-Moghaddam, A, Antony, CM, Antriyandarti, E, Anvari, D, Anvari, S, Anwar, S, Anwar, SL, Anwer, R, Anyasodor, AE, Aqeel, M, Arab, JP, Arabloo, J, Arafat, M, Aravkin, AY, Areda, D, Aremu, A, Aremu, O, Ariffin, H, Arkew, M, Armocida, B, Arndt, MB, Ärnlöv, J, Arooj, M, Artamonov, AA, Arulappan, J, Aruleba, RT, Arumugam, A, Asaad, M, Asadi-Lari, M, Asgedom, AA, Asghariahmadabad, M, Asghari-Jafarabadi, M, Ashraf, M, Aslani, A, Astell-Burt, T, Athar, M, Athari, SS, Atinafu, BTT, Atlaw, HW, Atorkey, P, Atout, MMW, Atreya, A, Aujayeb, A, Ausloos, M, Avan, A, Awedew, AF, Aweke, AM, Ayala Quintanilla, BP, Ayatollahi, H, Ayuso-Mateos, JL, Ayyoubzadeh, SM, Azadnajafabad, S, Azevedo, RMS, Azzam, AY, B, DB, Babu, AS, Badar, M, Badiye, AD, Baghdadi, S, Bagheri, N, Bagherieh, S, Bah, S, Bahadorikhalili, S, Bahmanziari, N, Bai, R, Baig, AA, Baker, JL, Bako, AT, Bakshi, RK, Balakrishnan, S, Balasubramanian, M, Baltatu, OC, Bam, K, Banach, M, Bandyopadhyay, S, Banik, PC, Bansal, H, Bansal, K, Barbic, F, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Bärnighausen, TW, Barone-Adesi, F, Barqawi, HJ, Barrero, LH, Barrow, A, Barteit, S, Barua, L, Basharat, Z, Bashiri, A, Basiru, A, Baskaran, P, Basnyat, B, Bassat, Q, Basso, JD, Basting, AVL, Basu, S, Batra, K, Baune, BT, Bayati, M, Bayileyegn, NS, Beaney, T, Bedi, N, Beghi, M, Behboudi, E, Behera, P, Behnoush, AH, Behzadifar, M, Beiranvand, M, Bejarano Ramirez, DF, Béjot, Y, Belay, SA, Belete, CM, Bell, ML, Bello, MB, Bello, OO, Belo, L, Beloukas, A, Bender, RG, Bensenor, IM, Beran, A, Berezvai, Z, Berhie, AY, Berice, BN, Bernstein, RS, Bertolacci, GJ, Bettencourt, PJG, Beyene, KA, Bhagat, DS, Bhagavathula, AS, Bhala, N, Bhalla, A, Bhandari, D, Bhangdia, K, Bhardwaj, N, Bhardwaj, P, Bhardwaj, PV, Bhargava, A, Bhaskar, S, Bhat, V, Bhatti, GK, Bhatti, JS, Bhatti, MS, Bhatti, R, Bhutta, ZA, Bikbov, B, Bishai, JD, Bisignano, C, Bisulli, F, Biswas, A, Biswas, B, Bitaraf, S, Bitew, BD, Bitra, VR, Bjørge, T, Boachie, MK, Boampong, MS, Bobirca, AV, Bodolica, V, Bodunrin, AO, Bogale, EK, Bogale, KA, Bohlouli, S, Bolarinwa, OA, Boloor, A, Bonakdar Hashemi, M, Bonny, A, Bora, K, Bora Basara, B, Borhany, H, Borzutzky, A, Bouaoud, S, Boustany, A, Boxe, C, Boyko, EJ, Brady, OJ, Braithwaite, D, Brant, LC, Brauer, M, Brazinova, A, Brazo-Sayavera, J, Breitborde, NJK, Breitner, S, Brenner, H, Briko, AN, Briko, NI, Britton, G, Brown, J, Brugha, T, Bulamu, NB, Bulto, LN, Buonsenso, D, Burns, RA, Busse, R, Bustanji, Y, Butt, NS, Butt, ZA, Caetano dos Santos, FL, Calina, D, Cámera, LA, Campos, LA, Campos-Nonato, IR, Cao, C, Cao, Y, Capodici, A, Cárdenas, R, Carr, S, Carreras, G, Carrero, JJ, Carugno, A, Carvalheiro, CG, Carvalho, F, Carvalho, M, Castaldelli-Maia, JM, Castañeda-Orjuela, CA, Castelpietra, G, Catalá-López, F, Catapano, AL, Cattaruzza, MS, Cederroth, CR, Cegolon, L, Cembranel, F, Cenderadewi, M, Cercy, KM, Cerin, E, Cevik, M, Chadwick, J, Chahine, Y, Chakraborty, C, Chakraborty, PA, Chan, JSK, Chan, RNC, Chandika, RM, Chandrasekar, EK, Chang, C-K, Chang, J-C, Chanie, GS, Charalampous, P, Chattu, VK, Chaturvedi, P, Chatzimavridou-Grigoriadou, V, Chaurasia, A, Chen, AW, Chen, A-T, Chen, CS, Chen, H, Chen, MX, Chen, S, Cheng, C-Y, Cheng, ETW, Cherbuin, N, Cheru, WA, Chien, J-H, Chimed-Ochir, O, Chimoriya, R, Ching, PR, Chirinos-Caceres, JL, Chitheer, A, Cho, WCS, Chong, B, Chopra, H, Choudhari, SG, Chowdhury, R, Christopher, DJ, Chukwu, IS, Chung, E, Chung, S-C, Chutiyami, M, Cindi, Z, Cioffi, I, Claassens, MM, Claro, RM, Coberly, K, Cogen, RM, Columbus, A, Comfort, H, Conde, J, Cortese, S, Cortesi, PA, Costa, VM, Costanzo, S, Cousin, E, Couto, RAS, Cowden, RG, Cramer, KM, Criqui, MH, Cruz-Martins, N, Cuadra-Hernández, SM, Culbreth, GT, Cullen, P, Cunningham, M, Curado, MP, Dadana, S, Dadras, O, Dai, S, Dai, X, Dai, Z, Dalli, LL, Damiani, G, Darega Gela, J, Das, JK, Das, S, Dascalu, AM, Dash, NR, Dashti, M, Dastiridou, A, Davey, G, Dávila-Cervantes, CA, Davis Weaver, N, Davletov, K, De Leo, D, de Luca, K, Debele, AT, Debopadhaya, S, Degenhardt, L, Dehghan, A, Deitesfeld, L, Del Bo', C, Delgado-Enciso, I, Demessa, BH, Demetriades, AK, Deng, K, Deng, X, Denova-Gutiérrez, E, Deravi, N, Dereje, N, Dervenis, N, Dervišević, E, Des Jarlais, DC, Desai, HD, Desai, R, Devanbu, VGC, Dewan, SMR, Dhali, A, Dhama, K, Dhimal, M, Dhingra, S, Dhulipala, VR, Dias da Silva, D, Diaz, D, Diaz, MJ, Dima, A, Ding, DD, Ding, H, Dinis-Oliveira, RJ, Dirac, MA, Djalalinia, S, Do, THP, do Prado, CB, Doaei, S, Dodangeh, M, Dohare, S, Dokova, KG, Dolecek, C, Dominguez, R-MV, Dong, W, Dongarwar, D, D'Oria, M, Dorostkar, F, Dorsey, ER, dos Santos, WM, Doshi, R, Doshmangir, L, Dowou, RK, Driscoll, TR, Dsouza, HL, Dsouza, V, Du, M, Dube, J, Duncan, BB, Duraes, AR, Duraisamy, S, Durojaiye, OC, Dwyer-Lindgren, L, Dzianach, PA, Dziedzic, AM, E'mar, AR, Eboreime, E, Ebrahimi, A, Echieh, CP, Edinur, HA, Edvardsson, D, Edvardsson, K, Efendi, D, Efendi, F, Effendi, DE, Eikemo, TA, Eini, E, Ekholuenetale, M, Ekundayo, TC, El Sayed, I, Elbarazi, I, Elema, TB, Elemam, NM, Elgar, FJ, Elgendy, IY, ElGohary, GMT, Elhabashy, HR, Elhadi, M, El-Huneidi, W, Elilo, LT, Elmeligy, OAA, Elmonem, MA, Elshaer, M, Elsohaby, I, Emeto, TI, Engelbert Bain, L, Erkhembayar, R, Esezobor, CI, Eshrati, B, Eskandarieh, S, Espinosa-Montero, J, Esubalew, H, Etaee, F, Fabin, N, Fadaka, AO, Fagbamigbe, AF, Fahim, A, Fahimi, S, Fakhri-Demeshghieh, A, Falzone, L, Fareed, M, Farinha, CSES, Faris, MEM, Faris, PS, Faro, A, Fasanmi, AO, Fatehizadeh, A, Fattahi, H, Fauk, NK, Fazeli, P, Feigin, VL, Feizkhah, A, Fekadu, G, Feng, X, Fereshtehnejad, S-M, Feroze, AH, Ferrante, D, Ferrari, AJ, Ferreira, N, Fetensa, G, Feyisa, BR, Filip, I, Fischer, F, Flavel, J, Flood, D, Florin, BT, Foigt, NA, Folayan, MO, Fomenkov, AA, Foroutan, B, Foroutan, M, Forthun, I, Fortuna, D, Foschi, M, Fowobaje, KR, Francis, KL, Franklin, RC, Freitas, A, Friedman, J, Friedman, SD, Fukumoto, T, Fuller, JE, Fux, B, Gaal, PA, Gadanya, MA, Gaidhane, AM, Gaihre, S, Gakidou, E, Galali, Y, Galles, NC, Gallus, S, Ganbat, M, Gandhi, AP, Ganesan, B, Ganiyani, MA, Garcia-Gordillo, MA, Gardner, WM, Garg, J, Garg, N, Gautam, RK, Gbadamosi, SO, Gebi, TG, Gebregergis, MW, Gebrehiwot, M, Gebremeskel, TG, Georgescu, SR, Getachew, T, Gething, PW, Getie, M, Ghadiri, K, Ghahramani, S, Ghailan, KY, Ghasemi, M-R, Ghasempour Dabaghi, G, Ghasemzadeh, A, Ghashghaee, A, Ghassemi, F, Ghazy, RM, Ghimire, A, Ghoba, S, Gholamalizadeh, M, Gholamian, A, Gholamrezanezhad, A, Gholizadeh, N, Ghorbani, M, Ghorbani Vajargah, P, Ghoshal, AG, Gill, PS, Gill, TK, Gillum, RF, Ginindza, TG, Girmay, A, Glasbey, JC, Gnedovskaya, EV, Göbölös, L, Godinho, MA, Goel, A, Golchin, A, Goldust, M, Golechha, M, Goleij, P, Gomes, NGM, Gona, PN, Gopalani, SV, Gorini, G, Goudarzi, H, Goulart, AC, Goulart, BNG, Goyal, A, Grada, A, Graham, SM, Grivna, M, Grosso, G, Guan, S-Y, Guarducci, G, Gubari, MIM, Gudeta, MD, Guha, A, Guicciardi, S, Guimarães, RA, Gulati, S, Gunawardane, DA, Gunturu, S, Guo, C, Gupta, AK, Gupta, B, Gupta, MK, Gupta, M, Gupta, RD, Gupta, R, Gupta, S, Gupta, VB, Gupta, VK, Gurmessa, L, Gutiérrez, RA, Habibzadeh, F, Habibzadeh, P, Haddadi, R, Hadei, M, Hadi, NR, Haep, N, Hafezi-Nejad, N, Hailu, A, Haj-Mirzaian, A, Halboub, ES, Hall, BJ, Haller, S, Halwani, R, Hamadeh, RR, Hameed, S, Hamidi, S, Hamilton, EB, Han, C, Han, Q, Hanif, A, Hanifi, N, Hankey, GJ, Hanna, F, Hannan, MA, Haque, MN, Harapan, H, Hargono, A, Haro, JM, Hasaballah, AI, Hasan, I, Hasan, MT, Hasani, H, Hasanian, M, Hashi, A, Hasnain, MS, Hassan, I, Hassanipour, S, Hassankhani, H, Haubold, J, Havmoeller, RJ, Hay, SI, He, J, Hebert, JJ, Hegazi, OE, Heidari, G, Heidari, M, Heidari-Foroozan, M, Helfer, B, Hendrie, D, Herrera-Serna, BY, Herteliu, C, Hesami, H, Hezam, K, Hill, CL, Hiraike, Y, Holla, R, Horita, N, Hossain, MM, Hossain, S, Hosseini, M-S, Hosseinzadeh, H, Hosseinzadeh, M, Hosseinzadeh Adli, A, Hostiuc, M, Hostiuc, S, Hsairi, M, Hsieh, VC-R, Hsu, RL, Hu, C, Huang, J, Hultström, M, Humayun, A, Hundie, TG, Hussain, J, Hussain, MA, Hussein, NR, Hussien, FM, Huynh, H-H, Hwang, B-F, Ibitoye, SE, Ibrahim, KS, Iftikhar, PM, Ijo, D, Ikiroma, AI, Ikuta, KS, Ikwegbue, PC, Ilesanmi, OS, Ilic, IM, Ilic, MD, Imam, MT, Immurana, M, Inamdar, S, Indriasih, E, Iqhrammullah, M, Iradukunda, A, Iregbu, KC, Islam, MR, Islam, SMS, Islami, F, Ismail, F, Ismail, NE, Iso, H, Isola, G, Iwagami, M, Iwu, CCD, Iyamu, IO, Iyer, M, J, LM, Jaafari, J, Jacob, L, Jacobsen, KH, Jadidi-Niaragh, F, Jafarinia, M, Jafarzadeh, A, Jaggi, K, Jahankhani, K, Jahanmehr, N, Jahrami, H, Jain, N, Jairoun, AA, Jaiswal, A, Jamshidi, E, Janko, MM, Jatau, AI, Javadov, S, Javaheri, T, Jayapal, SK, Jayaram, S, Jebai, R, Jee, SH, Jeganathan, J, Jha, AK, Jha, RP, Jiang, H, Jin, Y, Johnson, O, Jokar, M, Jonas, JB, Joo, T, Joseph, A, Joseph, N, Joshua, CE, Joshy, G, Jozwiak, JJ, Jürisson, M, K, V, Kaambwa, B, Kabir, A, Kabir, Z, Kadashetti, V, Kadir, DH, Kalani, R, Kalankesh, LR, Kaliyadan, F, Kalra, S, Kamal, VK, Kamarajah, SK, Kamath, R, Kamiab, Z, Kamyari, N, Kanagasabai, T, Kanchan, T, Kandel, H, Kanmanthareddy, AR, Kanmiki, EW, Kanmodi, KK, Kannan S, S, Kansal, SK, Kantar, RS, Kapoor, N, Karajizadeh, M, Karanth, SD, Karasneh, RA, Karaye, IM, Karch, A, Karim, A, Karimi, SE, Karimi Behnagh, A, Kashoo, FZ, Kasnazani, QHA, Kasraei, H, Kassebaum, NJ, Kassel, MB, Kauppila, JH, Kaur, N, Kawakami, N, Kayode, GA, Kazemi, F, Kazemian, S, Kazmi, TH, Kebebew, GM, Kebede, AD, Kebede, F, Keflie, TS, Keiyoro, PN, Keller, C, Kelly, JT, Kempen, JH, Kerr, JA, Kesse-Guyot, E, Khajuria, H, Khalaji, A, Khalid, N, Khalil, AA, Khalilian, A, Khamesipour, F, Khan, A, Khan, G, Khan, I, Khan, IA, Khan, MN, Khan, M, Khan, MJ, Khan, MAB, Khan, ZA, Khan suheb, MZ, Khanmohammadi, S, Khatab, K, Khatami, F, Khatatbeh, H, Khatatbeh, MM, Khavandegar, A, Khayat Kashani, HR, Khidri, FF, Khodadoust, E, Khorgamphar, M, Khormali, M, Khorrami, Z, Khosravi, A, Khosravi, MA, Kifle, ZD, Kim, G, Kim, J, Kim, K, Kim, MS, Kim, YJ, Kimokoti, RW, Kinzel, KE, Kisa, A, Kisa, S, Klu, D, Knudsen, AKS, Kocarnik, JM, Kochhar, S, Kocsis, T, Koh, DSQ, Kolahi, A-A, Kolves, K, Kompani, F, Koren, G, Kosen, S, Kostev, K, Koul, PA, Koulmane Laxminarayana, SL, Krishan, K, Krishna, H, Krishna, V, Krishnamoorthy, V, Krishnamoorthy, Y, Krohn, KJ, Kuate Defo, B, Kucuk Bicer, B, Kuddus, MA, Kuddus, M, Kuitunen, I, Kulimbet, M, Kulkarni, V, Kumar, A, Kumar, H, Kumar, M, Kumar, R, Kumari, M, Kumie, FT, Kundu, S, Kurmi, OP, Kusnali, A, Kusuma, D, Kwarteng, A, Kyriopoulos, I, Kyu, HH, La Vecchia, C, Lacey, B, Ladan, MA, Laflamme, L, Lagat, AK, Lager, ACJ, Lahmar, A, Lai, DTC, Lal, DK, Lalloo, R, Lallukka, T, Lam, H, Lám, J, Landrum, KR, Lanfranchi, F, Lang, JJ, Langguth, B, Lansingh, VC, Laplante-Lévesque, A, Larijani, B, Larsson, AO, Lasrado, S, Lassi, ZS, Latief, K, Latifinaibin, K, Lauriola, P, Le, NHH, Le, TTT, Le, TDT, Ledda, C, Ledesma, JR, Lee, M, Lee, PH, Lee, SW, Lee, SWH, Lee, W-C, Lee, YH, LeGrand, KE, Leigh, J, Leong, E, Lerango, TL, Li, M-C, Li, W, Li, X, Li, Y, Li, Z, Ligade, VS, Likaka, ATM, Lim, L-L, Lim, SS, Lindstrom, M, Linehan, C, Liu, C, Liu, G, Liu, J, Liu, R, Liu, S, Liu, X, Llanaj, E, Loftus, MJ, López-Bueno, R, Lopukhov, PD, Loreche, AM, Lorkowski, S, Lotufo, PA, Lozano, R, Lubinda, J, Lucchetti, G, Lugo, A, Lunevicius, R, Ma, ZF, Maass, KL, Machairas, N, Machoy, M, Madadizadeh, F, Madsen, C, Madureira-Carvalho, ÁM, Maghazachi, AA, Maharaj, SB, Mahjoub, S, Mahmoud, MA, Mahmoudi, A, Mahmoudi, E, Mahmoudi, R, Majeed, A, Makhdoom, IF, Malakan Rad, E, Maled, V, Malekzadeh, R, Malhotra, AK, Malhotra, K, Malik, AA, Malik, I, Malta, DC, Mamun, AA, Mansouri, P, Mansournia, MA, Mantovani, LG, Maqsood, S, Marasini, BP, Marateb, HR, Maravilla, JC, Marconi, AM, Mardi, P, Marino, M, Marjani, A, Martinez, G, Martinez-Guerra, BA, Martinez-Piedra, R, Martini, D, Martini, S, Martins-Melo, FR, Martorell, M, Marx, W, Maryam, S, Marzo, RR, Masaka, A, Masrie, A, Mathieson, S, Mathioudakis, AG, Mathur, MR, Mattumpuram, J, Matzopoulos, R, Maude, RJ, Maugeri, A, Maulik, PK, Mayeli, M, Mazaheri, M, Mazidi, M, McGrath, JJ, McKee, M, McKowen, ALW, McLaughlin, SA, McPhail, SM, Mechili, EA, Medina, JRC, Mediratta, RP, Meena, JK, Mehra, R, Mehrabani-Zeinabad, K, Mehrabi Nasab, E, Mekene Meto, T, Meles, GG, Mendez-Lopez, MAM, Mendoza, W, Menezes, RG, Mengist, B, Mentis, A-FA, Meo, SA, Meresa, HA, Meretoja, A, Meretoja, TJ, Mersha, AM, Mesfin, BA, Mestrovic, T, Mettananda, KCD, Mettananda, S, Meylakhs, P, Mhlanga, A, Mhlanga, L, Mi, T, Miazgowski, T, Micha, G, Michalek, IM, Miller, TR, Mills, EJ, Minh, LHN, Mini, GK, Mir Mohammad Sadeghi, P, Mirica, A, Mirijello, A, Mirrakhimov, EM, Mirutse, MK, Mirzaei, M, Misganaw, A, Mishra, A, Misra, S, Mitchell, PB, Mithra, P, Mittal, C, Mobayen, M, Moberg, ME, Mohamadkhani, A, Mohamed, J, Mohamed, MFH, Mohamed, NS, Mohammad-Alizadeh-Charandabi, S, Mohammadi, S, Mohammadian-Hafshejani, A, Mohammadifard, N, Mohammed, H, Mohammed, M, Mohammed, S, Mohan, V, Mojiri-Forushani, H, Mokari, A, Mokdad, AH, Molinaro, S, Molokhia, M, Momtazmanesh, S, Monasta, L, Mondello, S, Moni, MA, Moodi Ghalibaf, A, Moradi, M, Moradi, Y, Moradi-Lakeh, M, Moradzadeh, M, Moraga, P, Morawska, L, Moreira, RS, Morovatdar, N, Morrison, SD, Morze, J, Mosser, JF, Motappa, R, Mougin, V, Mouodi, S, Mousavi, P, Mousavi, SE, Mousavi Khaneghah, A, Mpolya, EA, Mrejen, M, Mubarik, S, Muccioli, L, Mueller, UO, Mughal, F, Mukherjee, S, Mulita, F, Munjal, K, Murillo-Zamora, E, Musaigwa, F, Musallam, KM, Mustafa, A, Mustafa, G, Muthupandian, S, Muthusamy, R, Muzaffar, M, Myung, W, Nagarajan, AJ, Nagel, G, Naghavi, P, Naheed, A, Naik, 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Zhu, L, Ziafati, M, Zielińska, M, Zitoun, OA, Zoladl, M, Zou, Z, Zuhlke, LJ, Zumla, A, Zweck, E, Zyoud, SH, Wool, EE, and Murray, CJL
Published: 2024

2. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Ferrari, AJ, Santomauro, DF, Aali, A, Abate, YH, Abbafati, C, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdollahi, A, Abdullahi, A, Abegaz, KH, Abeldaño Zuñiga, RA, Aboagye, RG, Abolhassani, H, Abreu, LG, Abualruz, H, Abu-Gharbieh, E, Abu-Rmeileh, NME, Ackerman, IN, Addo, IY, Addolorato, G, Adebiyi, AO, Adepoju, AV, Adewuyi, HO, Afyouni, S, Afzal, S, Agodi, A, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, S, Ahmed, A, Ahmed, LA, Ahmed, MB, Ajami, M, Akinosoglou, K, Akkaif, MA, Al Hasan, SM, Alalalmeh, SO, Al-Aly, Z, Albashtawy, M, Aldridge, RW, Alemu, MD, Alemu, YM, Alene, KA, Al-Gheethi, AAS, Alharrasi, M, Alhassan, RK, Ali, MU, Ali, R, Ali, SSS, Alif, SM, Aljunid, SM, Al-Marwani, S, Almazan, JU, Alomari, MA, Al-Omari, B, Altaany, Z, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Alwafi, H, Al-Wardat, MS, Al-Worafi, YM, Aly, S, Alzoubi, KH, Amare, AT, Amegbor, PM, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amugsi, DA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Ansari, H, Antony, CM, Anwar, S, Anwar, SL, Anwer, R, Anyanwu, PE, Arab, JP, Arabloo, J, Arafat, M, Araki, DT, Aravkin, AY, Arkew, M, Armocida, B, Arndt, MB, Arooj, M, Artamonov, AA, Aruleba, RT, Arumugam, A, Ashbaugh, C, Ashemo, MY, Ashraf, M, Asika, MO, Askari, E, Astell-Burt, T, Athari, SS, Atorkey, P, Atout, MMW, Atreya, A, Aujayeb, A, Ausloos, M, Avan, A, Awotidebe, AW, Awuviry-Newton, K, Ayala Quintanilla, BP, Ayuso-Mateos, JL, Azadnajafabad, S, Azevedo, RMS, Babu, AS, Badar, M, Badiye, AD, Baghdadi, S, Bagheri, N, Bah, S, Bai, R, Baker, JL, Bakkannavar, SM, Bako, AT, Balakrishnan, S, Bam, K, Banik, PC, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Barqawi, HJ, Barrow, A, Barteit, S, Barua, L, Bashiri Aliabadi, S, Basiru, A, Basu, S, Bathini, PP, Batra, K, Baune, BT, Bayileyegn, NS, Behnam, B, Behnoush, AH, Beiranvand, M, Bejarano Ramirez, DF, Bell, ML, Bello, OO, Beloukas, A, Bensenor, IM, Berezvai, Z, Bernabe, E, Bernstein, RS, Bettencourt, PJG, Bhagavathula, AS, Bhala, N, Bhandari, D, Bhargava, A, Bhaskar, S, Bhat, V, Bhatti, GK, Bhatti, JS, Bhatti, MS, Bhatti, R, Bhutta, ZA, Bikbov, B, Bishai, JD, Bisignano, C, Bitra, VR, Bjørge, T, Bodolica, V, Bodunrin, AO, Bogale, EK, Bonakdar Hashemi, M, Bonny, A, Bora Basara, B, Borhany, H, Boxe, C, Brady, OJ, Bragazzi, NL, Braithwaite, D, Brant, LC, Brauer, M, Breitner, S, Brenner, H, Brown, J, Brugha, T, Bulamu, NB, Buonsenso, D, Burkart, K, Burns, RA, Busse, R, Bustanji, Y, Butt, ZA, Byun, J, Caetano dos Santos, FL, Calina, D, Cámera, LA, Campos-Nonato, IR, Cao, C, Capodici, A, Carr, S, Carreras, G, Carugno, A, Carvalho, M, Castaldelli-Maia, JM, Castañeda-Orjuela, CA, Castelpietra, G, Catapano, AL, Cattaruzza, MS, Caye, A, Cegolon, L, Cembranel, F, Cenderadewi, M, Cerin, E, Chakraborty, PA, Chan, JSK, Chan, RNC, Chandika, RM, Chandrasekar, EK, Charalampous, P, Chattu, VK, Chatzimavridou-Grigoriadou, V, Chen, AW, Chen, A-T, Chen, CS, Chen, H, Chen, NM, Cheng, ETW, Chimed-Ochir, O, Chimoriya, R, Ching, PR, Cho, WCS, Choi, S, Chong, B, Chong, YY, Choudhari, SG, Chowdhury, R, Christensen, SWM, Chu, D-T, Chukwu, IS, Chung, E, Chutiyami, M, Claassens, MM, Cogen, RM, Columbus, A, Conde, J, Cortesi, PA, Cousin, E, Criqui, MH, Cruz-Martins, N, Dadras, O, Dai, S, Dai, X, Dai, Z, Dalaba, MA, Damiani, G, Das, JK, Das, S, Dashti, M, Dávila-Cervantes, CA, Davletov, K, De Leo, D, Debele, AT, Debopadhaya, S, DeCleene, NK, Deeba, F, Degenhardt, L, Del Bo', C, Delgado-Enciso, I, Demetriades, AK, Denova-Gutiérrez, E, Dervenis, N, Desai, HD, Desai, R, Deuba, K, Dhama, K, Dharmaratne, SD, Dhingra, S, Dias da Silva, D, Diaz, D, Diaz, LA, Diaz, MJ, Dima, A, Ding, DD, Dirac, MA, Do, THP, do Prado, CB, Dohare, S, Dominguez, R-MV, Dong, W, Dongarwar, D, D'Oria, M, Dorsey, ER, Doshmangir, L, Dowou, RK, Driscoll, TR, Dsouza, HL, Dsouza, V, Dube, J, Dumith, SC, Duncan, BB, Duraes, AR, Duraisamy, S, Durojaiye, OC, Dzianach, PA, Dziedzic, AM, Eboreime, E, Ebrahimi, A, Edinur, HA, 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S, Hsu, JM, Huang, J, Hugo, FN, Hushmandi, K, Hussain, J, Hussein, NR, Huynh, CK, Huynh, H-H, Hwang, B-F, Iannucci, VC, Ihler, AL, Ikiroma, AI, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Imam, MT, Immurana, M, Irham, LM, Islam, MR, Islam, SMS, Islami, F, Ismail, F, Ismail, NE, Isola, G, Iwagami, M, Iwu, CCD, Iyer, M, Jaafari, J, Jacobsen, KH, Jadidi-Niaragh, F, Jafarinia, M, Jaggi, K, Jahankhani, K, Jahanmehr, N, Jahrami, H, Jain, A, Jain, N, Jairoun, AA, Jaiswal, A, Jakovljevic, M, Jatau, AI, Javadov, S, Javaheri, T, Jayapal, SK, Jayaram, S, Jee, SH, Jeganathan, J, Jeyakumar, A, Jha, AK, Jiang, H, Jin, Y, Jonas, JB, Joo, T, Joseph, A, Joseph, N, Joshua, CE, Jozwiak, JJ, Jürisson, M, K, V, Kaambwa, B, Kabir, A, Kabir, Z, Kadashetti, V, Kalani, R, Kalankesh, LR, Kaliyadan, F, Kalra, S, Kamenov, K, Kamyari, N, Kanagasabai, T, Kandel, H, Kanmanthareddy, AR, Kanmodi, KK, Kantar, RS, Karaye, IM, Karim, A, Karimi, SE, Karimi, Y, Kasraei, H, Kassel, MB, Kauppila, JH, Kawakami, N, Kayode, 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Lubinda, J, Lucchetti, G, Lunevicius, R, Lusk, JB, lv, H, Ma, ZF, Machairas, N, Madureira-Carvalho, ÁM, Magaña Gómez, JA, Maghazachi, AA, Maharjan, P, Mahasha, PW, Maheri, M, Mahjoub, S, Mahmoud, MA, Mahmoudi, E, Majeed, A, Makris, KC, Malakan Rad, E, Malhotra, K, Malik, AA, Malik, I, Malta, DC, Manla, Y, Mansour, A, Mansouri, P, Mansournia, MA, Mantilla Herrera, AM, Mantovani, LG, Manu, E, Marateb, HR, Mardi, P, Martinez, G, Martinez-Piedra, R, Martini, D, Martins-Melo, FR, Martorell, M, Marx, W, Maryam, S, Marzo, RR, Mathangasinghe, Y, Mathieson, S, Mathioudakis, AG, Mattumpuram, J, Maugeri, A, Mayeli, M, Mazidi, M, Mazzotti, A, McGrath, JJ, McKee, M, McKowen, ALW, McPhail, MA, Mehrabani-Zeinabad, K, Mehrabi Nasab, E, Mekene Meto, T, Mendoza, W, Menezes, RG, Mensah, GA, Mentis, A-FA, Meo, SA, Meresa, HA, Meretoja, A, Meretoja, TJ, Mersha, AM, Mestrovic, T, Mettananda, KCD, Mettananda, S, Michalek, IM, Miller, PA, Miller, TR, Mills, EJ, Minh, LHN, Mirijello, A, Mirrakhimov, EM, Mirutse, MK, Mirza-Aghazadeh-Attari, M, Mirzaei, M, Mirzaei, R, Misganaw, A, Mishra, AK, Mitchell, PB, Mittal, C, Moazen, B, Moberg, ME, Mohamed, J, Mohamed, MFH, Mohamed, NS, Mohammadi, E, Mohammadi, S, Mohammed, H, Mohammed, S, Mohr, RM, Mokdad, AH, Molinaro, S, Momtazmanesh, S, Monasta, L, Mondello, S, Moodi Ghalibaf, A, Moradi, M, Moradi, Y, Moradi-Lakeh, M, Moraga, P, Morawska, L, Moreira, RS, Morovatdar, N, Morrison, SD, Morze, J, Mosapour, A, Mosser, JF, Mossialos, E, Motappa, R, Mougin, V, Mouodi, S, Mrejen, M, Msherghi, A, Mubarik, S, Mueller, UO, Mulita, F, Munjal, K, Murillo-Zamora, E, Murlimanju, BV, Mustafa, G, Muthu, S, Muzaffar, M, Myung, W, Nagarajan, AJ, Naghavi, P, Naik, GR, Nainu, F, Nair, S, Najmuldeen, HHR, Nangia, V, Naqvi, AA, Narayana, AI, Nargus, S, Nascimento, GG, Nashwan, AJ, Nasrollahizadeh, A, Natto, ZS, Nayak, BP, Nayak, VC, Nduaguba, SO, Negash, H, Negoi, I, Negoi, RI, Nejadghaderi, SA, Nesbit, OD, Netsere, HB, Ng, M, Nguefack-Tsague, G, Ngunjiri, JW, Nguyen, DH, Nguyen, HQ, Niazi, RK, Nikolouzakis, TK, Nikoobar, A, Nikoomanesh, F, Nikpoor, AR, Nnaji, CA, Nnyanzi, LA, Noman, EA, Nomura, S, Norrving, B, Nri-Ezedi, CA, Ntaios, G, Ntsekhe, M, Nurrika, D, Nzoputam, CI, Nzoputam, OJ, Oancea, B, Odetokun, IA, O'Donnell, MJ, Oguntade, AS, Oguta, JO, Okati-Aliabad, H, Okeke, SR, Okekunle, AP, Okonji, OC, Olagunju, AT, Olasupo, OO, Olatubi, MI, Oliveira, GMM, Olufadewa, II, Olusanya, BO, Olusanya, JO, Omar, HA, Omer, GL, Omonisi, AEE, Onie, S, Onwujekwe, OE, Ordak, M, Orish, VN, Ortega-Altamirano, DV, Ortiz, A, Ortiz-Brizuela, E, Osman, WMS, Ostroff, SM, Osuagwu, UL, Otoiu, A, Otstavnov, N, Otstavnov, SS, Ouyahia, A, Ouyang, G, Owolabi, MO, P A, MP, Padron-Monedero, A, Padubidri, JR, Palicz, T, Palladino, C, Pan, F, Pandi-Perumal, SR, Pangaribuan, HU, Panos, GD, Panos, LD, Pantea Stoian, AM, Pardhan, S, Parikh, RR, Pashaei, A, Pasovic, M, Passera, R, Patel, J, Patel, SK, Patil, S, Patoulias, D, Patthipati, VS, Pawar, S, Pazoki Toroudi, H, Pease, SA, Peden, AE, Pedersini, P, Peng, M, Pensato, U, Pepito, VCF, Peprah, EK, Peprah, P, Perdigão, J, Pereira, MO, Perianayagam, A, Perico, N, Pesudovs, K, Petermann-Rocha, FE, Petri, WA, Pham, HT, Philip, AK, Phillips, MR, Pigeolet, M, Pigott, DM, Pillay, JD, Piracha, ZZ, Pirouzpanah, S, Plass, D, Plotnikov, E, Poddighe, D, Polinder, S, Postma, MJ, Pourtaheri, N, Prada, SI, Pradhan, PMS, Prakash, V, Prasad, M, Prates, EJS, Priscilla, T, Pritchett, N, Puri, P, Puvvula, J, Qasim, NH, Qattea, I, Qazi, AS, Qian, G, Rabiee Rad, M, Radhakrishnan, RA, Radhakrishnan, V, Raeisi Shahraki, H, Rafferty, Q, Raggi, A, Raghav, PR, Rahim, MJ, Rahman, MM, Rahman, MHU, Rahman, M, Rahman, MA, Rahmani, S, Rahmanian, M, Rahmawaty, S, Rajaa, S, Ramadan, MM, Ramasamy, SK, Ramasubramani, P, Ramazanu, S, Rana, K, Ranabhat, CL, Rancic, N, Rane, A, Rao, CR, Rao, K, Rao, M, Rao, SJ, Rashidi, M-M, Rathnaiah Babu, G, Rauniyar, SK, Rawaf, DL, Rawaf, S, Razo, C, Reddy, MMRK, Redwan, EMM, Reifels, L, Reiner Jr, RC, Remuzzi, G, Renzaho, AMN, Reshmi, B, Reyes, LF, Rezaei, N, Rezaei Hachesu, P, Rezaeian, M, Rickard, J, Rodrigues, CF, Rodriguez, JAB, Roever, L, Ronfani, L, Roshandel, G, Rotimi, K, Rout, HS, Roy, B, Roy, N, Roy, P, Rubagotti, E, S N, C, Saad, AMA, Saber-Ayad, MM, Sabour, S, Sacco, S, Sachdev, PS, Saddik, B, Saddler, A, Sadee, BA, Sadeghi, E, Sadeghi, M, Saeb, MR, Saeed, U, Safi, SZ, Sagar, R, Sagoe, D, Saif, Z, Sajid, MR, Sakshaug, JW, Salam, N, Salami, AA, Salaroli, LB, Saleh, MA, Salem, MR, Salem, MZY, Sallam, M, Samadzadeh, S, Samargandy, S, Samodra, YL, Samy, AM, Sanabria, J, Sanna, F, Santos, IS, Santric-Milicevic, MM, Sarasmita, MA, Sarikhani, Y, Sarmiento-Suárez, R, Sarode, GS, Sarode, SC, Sarveazad, A, Sathian, B, Sathyanarayan, A, Satpathy, M, Sawhney, M, Scarmeas, N, Schaarschmidt, BM, Schmidt, MI, Schneider, IJC, Schumacher, AE, Schwebel, DC, Schwendicke, F, Sedighi, M, Senapati, S, Senthilkumaran, S, Sepanlou, SG, Sethi, Y, Setoguchi, S, Seylani, A, Shadid, J, Shafie, M, Shah, H, Shah, NS, Shah, PA, Shahbandi, A, Shahid, S, Shahid, W, Shahwan, MJ, Shaikh, MA, Shakeri, A, Shalash, AS, Sham, S, Shamim, MA, Shamshirgaran, MA, Shamsi, MA, Shanawaz, M, Shankar, A, Shannawaz, M, Sharath, M, Sharifan, A, Sharifi-Rad, J, Sharma, M, Sharma, R, Sharma, S, Sharma, U, Sharma, V, Shastry, RP, Shavandi, A, Shayan, AM, Shayan, M, Shehabeldine, AME, Shetty, PH, Shibuya, K, Shifa, JE, Shiferaw, D, Shiferaw, WS, Shigematsu, M, Shiri, R, Shitaye, NA, Shittu, A, Shivakumar, KM, Shivarov, V, Shokati Eshkiki, Z, Shool, S, Shrestha, S, Shuval, K, Sibhat, MM, Siddig, EE, Sigfusdottir, ID, Silva, DAS, Silva, JP, Silva, LMLR, Silva, S, Simpson, CR, Singal, A, Singh, A, Singh, BB, Singh, H, Singh, JA, Singh, M, Singh, P, Skou, ST, Sleet, DA, Slepak, ELN, Solanki, R, Soliman, SSM, Song, S, Song, Y, Sorensen, RJD, Soriano, JB, Soyiri, IN, Spartalis, M, Sreeramareddy, CT, Stark, BA, Starodubova, AV, Stein, C, Stein, DJ, Steiner, C, Steiner, TJ, Steinmetz, JD, Steiropoulos, P, Stockfelt, L, Stokes, MA, Subedi, NS, Subramaniyan, V, Suemoto, CK, Suleman, M, Suliankatchi Abdulkader, R, Sultana, A, Sundström, J, Swain, CK, Szarpak, L, Tabaee Damavandi, P, Tabarés-Seisdedos, R, Tabatabaei Malazy, O, Tabatabaeizadeh, S-A, Tabatabai, S, Tabche, C, Tabish, M, Tadakamadla, SK, Taheri Abkenar, Y, Taheri Soodejani, M, Taherkhani, A, Taiba, J, Talaat, IM, Talukder, A, Tampa, M, Tamuzi, JL, Tan, K-K, Tandukar, S, Tang, H, Tavakoli Oliaee, R, Tavangar, SM, Teimoori, M, Temsah, M-H, Teramoto, M, Thangaraju, P, Thankappan, KR, Thapar, R, Thayakaran, R, Thirunavukkarasu, S, Thomas, N, Thomas, NK, Thum, CCC, Tichopad, A, Ticoalu, JHV, Tillawi, T, Tiruye, TY, Tobe-Gai, R, Tonelli, M, Topor-Madry, R, Torre, AE, Touvier, M, Tovani-Palone, MR, Tran, JT, Tran, MTN, Tran, NM, Tran, N-H, Trico, D, Tromans, SJ, Truyen, TTTT, Tsatsakis, A, Tsegay, GM, Tsermpini, EE, Tumurkhuu, M, Tyrovolas, S, Udoh, A, Umair, M, Umakanthan, S, Umar, TP, Undurraga, EA, Unim, B, Unnikrishnan, B, Unsworth, CA, Upadhyay, E, Urso, D, Usman, JS, Vahabi, SM, Vaithinathan, AG, Van den Eynde, J, Varga, O, Varma, RP, Vart, P, Vasankari, TJ, Vasic, M, Vaziri, S, Vellingiri, B, Venketasubramanian, N, Veroux, M, Verras, G-I, Vervoort, D, Villafañe, JH, Violante, FS, Vlassov, V, Vollset, SE, Volovat, SR, Vongpradith, A, Waheed, Y, Wang, C, Wang, F, Wang, N, Wang, S, Wang, Y, Wang, Y-P, Ward, P, Wassie, EG, Weaver, MR, Weerakoon, KG, Weintraub, RG, Weiss, DJ, Weldemariam, AH, Wells, KM, Wen, YF, Whisnant, JL, Whiteford, HA, Wiangkham, T, Wickramasinghe, DP, Wickramasinghe, ND, Wilandika, A, Wilkerson, C, Willeit, P, Wimo, A, Woldegebreal, DH, Wolf, AW, Wong, YJ, Woolf, AD, Wu, C, Wu, F, Wu, X, Wu, Z, Wulf Hanson, S, Xia, Y, Xiao, H, Xu, X, Xu, YY, Yadav, L, Yadollahpour, A, Yaghoubi, S, Yamagishi, K, Yang, L, Yano, Y, Yao, Y, Yaribeygi, H, Yazdanpanah, MH, Ye, P, Yehualashet, SS, Yesuf, SA, Yezli, S, Yiğit, A, Yiğit, V, Yigzaw, ZA, Yismaw, Y, Yon, DK, Yonemoto, N, Younis, MZ, Yu, C, Yu, Y, Yusuf, H, Zahid, MH, Zakham, F, Zaki, L, Zaki, N, Zaman, BA, Zamora, N, Zand, R, Zandieh, GGZ, Zar, HJ, Zarrintan, A, Zastrozhin, MS, Zhang, H, Zhang, N, Zhang, Y, Zhao, H, Zhong, C, Zhong, P, Zhou, J, Zhu, Z, Ziafati, M, Zielińska, M, Zimsen, SRM, Zoladl, M, Zumla, A, Zyoud, SH, Vos, T, and Murray, CJL
Published: 2024

3. Topical immunotherapy with diphenylcyclopropenone in paediatric patients with alopecia areata-A retrospective study of 97 patients.

Author: Esmaeili F, Vahabi SM, Abdoli M, Fazeli P, Ghandi N, Seddigh L, Aryanian Z, and Etesami I
Abstract: Background: Alopecia areata (AA) is an autoimmune disease causing chronic non-scarring hair loss. Different therapeutic regimens have been suggested for AA, which depend on patients' age, scalp involvement extent and duration. Topical immunotherapy with diphenylcyclopropenone (DPCP) is one of the treatment options for these patients., Objectives: We aimed to investigate the response to DPCP in paediatric AA patients., Methods: This retrospective study included 97 paediatric AA patients followed in the DPCP clinic from March 2016 to March 2021 at a referral dermatology hospital., Results: In a cohort of 97 paediatric patients with AA under treatment with DPCP, with a mean age of 11.10 ± 0.9, 53.6% of the patients were male. Patchy alopecia was the most prevalent type (45.4%). After 6 months of DPCP treatment, 51.5% showed no response, while 3.1% achieved complete response. At the 12-month evaluation, among the 68 patients who continued treatment, complete response was observed in 8.8%. A significant positive correlation was found between alopecia type, specifically patchy, and treatment response ( p = 0.031). Additionally, treatment duration emerged as a significant predictor of positive response at both six (OR 1.450, p = 0.026) and 12 months (OR 1.310, p = 0.043). A higher initial Severity of Alopecia Tool score was inversely correlated with treatment response (Spearman's rho -0.14, p = 0.002), indicating that initial disease severity may predict treatment efficacy., Conclusions: One year after the onset of DPCP in paediatric AA patients, the complete response and any hair regrowth rates were 8.8% and 61.8%, respectively. The milder initial disease severity and longer duration of treatment resulted in a better response., Competing Interests: None to declare., (© 2024 The Author(s). Skin Health and Disease published by John Wiley & Sons Ltd on behalf of British Association of Dermatologists.)
Published: 2024
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4. Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

Author: Moulaei K, Afrash MR, Parvin M, Shadnia S, Rahimi M, Mostafazadeh B, Evini PET, Sabet B, Vahabi SM, Soheili A, Fathy M, Kazemi A, Khani S, Mortazavi SM, and Hosseini SM
Subjects: Humans, Male, Female, Deep Learning, Intubation, Intratracheal methods, Iran, Adult, Middle Aged, ROC Curve, Machine Learning, Methanol poisoning, Artificial Intelligence
Abstract: The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately predicting intubation needs for methanol-poisoned patients. So, our study aims to assess Explainable Artificial Intelligence (XAI) for predicting intubation necessity in methanol-poisoned patients, comparing deep learning and machine learning models. This study analyzed a dataset of 897 patient records from Loghman Hakim Hospital in Tehran, Iran, encompassing cases of methanol poisoning, including those requiring intubation (202 cases) and those not requiring it (695 cases). Eight established ML (SVM, XGB, DT, RF) and DL (DNN, FNN, LSTM, CNN) models were used. Techniques such as tenfold cross-validation and hyperparameter tuning were applied to prevent overfitting. The study also focused on interpretability through SHAP and LIME methods. Model performance was evaluated based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior performance in accuracy (94.0%), sensitivity (99.0%), specificity (94.0%), and F1-score (97.0%). CNN led in ROC with 78.0%. For ML models, RF excelled in accuracy (97.0%) and specificity (100%), followed by XGB with sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%). Overall, RF and XGB outperformed other models, with accuracy (97.0%) and specificity (100%) for RF, and sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%) for XGB. ML models surpassed DL models across all metrics, with accuracies from 93.0% to 97.0% for DL and 93.0% to 99.0% for ML. Sensitivities ranged from 98.0% to 99.37% for DL and 93.0% to 99.0% for ML. DL models achieved specificities from 78.0% to 94.0%, while ML models ranged from 93.0% to 100%. F1-scores for DL were between 93.0% and 97.0%, and for ML between 96.0% and 98.27%. DL models scored ROC between 68.0% and 78.0%, while ML models ranged from 84.0% to 96.08%. Key features for predicting intubation necessity include GCS at admission, ICU admission, age, longer folic acid therapy duration, elevated BUN and AST levels, VBG_HCO3 at initial record, and hemodialysis presence. This study as the showcases XAI's effectiveness in predicting intubation necessity in methanol-poisoned patients. ML models, particularly RF and XGB, outperform DL counterparts, underscoring their potential for clinical decision-making., (© 2024. The Author(s).)
Published: 2024
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5. Prediction of acute methanol poisoning prognosis using machine learning techniques.

Author: Rahimi M, Hosseini SM, Mohtarami SA, Mostafazadeh B, Evini PET, Fathy M, Kazemi A, Khani S, Mortazavi SM, Soheili A, Vahabi SM, and Shadnia S
Subjects: Humans, Male, Female, Adult, Retrospective Studies, Prognosis, Middle Aged, Iran epidemiology, Young Adult, Poisoning diagnosis, Poisoning therapy, Machine Learning, Methanol poisoning
Abstract: Methanol poisoning is a global public health concern, especially prevalent in developing nations. This study focuses on predicting the severity of methanol intoxication using machine learning techniques, aiming to improve early identification and prognosis assessment. The study, conducted at Loghman Hakim Hospital in Tehran, Iran. The data pertaining to individuals afflicted with methanol poisoning was retrieved retrospectively and divided into training and test groups at a ratio of 70:30. The selected features were then inputted into various machine learning methods. The models were implemented using the Scikit-learn library in the Python programming language. Ultimately, the efficacy of the developed models was assessed through ten-fold cross-validation techniques and specific evaluation criteria, with a confidence level of 95%. A total number of 897 patients were included and divided in three groups including without sequel (n = 573), with sequel (n = 234), and patients who died (n = 90). The two-step feature selection was yielded 43 features in first step and 23 features in second step. In best model (Gradient Boosting Classifier) test dataset metric by 32 features younger age, higher methanol ingestion, respiratory symptoms, lower GCS scores, type of visual symptom, duration of therapeutic intervention, ICU admission, and elevated CPK levels were among the most important features predicting the prognosis of methanol poisoning. The Gradient Boosting Classifier demonstrated the highest predictive capability, achieving AUC values of 0.947 and 0.943 in the test dataset with 43 and 23 features, respectively. This research introduces a machine learning-driven prognostic model for methanol poisoning, demonstrating superior predictive capabilities compared to traditional statistical methods. The identified features provide valuable insights for early intervention and personalized treatment strategies., 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 © 2024 Elsevier B.V. All rights reserved.)
Published: 2024
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6. JAK Inhibitors in Cutaneous T-Cell Lymphoma: Friend or Foe? A Systematic Review of the Published Literature.

Author: Vahabi SM, Bahramian S, Esmaeili F, Danaei B, Kalantari Y, Fazeli P, Sadeghi S, Hajizadeh N, Assaf C, and Etesami I
Abstract: Cutaneous T-cell lymphomas (CTCLs) are a group of lymphoid neoplasms with high relapse rates and no curative treatment other than allogeneic stem cell transplantation (allo-SCT). CTCL is significantly influenced by disruption of JAK/STAT signaling. Therefore, Janus kinase (JAK) inhibitors may be promising for CTCL treatment. This study is a systematic review aiming to investigate the role of JAK inhibitors in the treatment of CTCL, including their efficacy and safety. Out of 438 initially searched articles, we present 13 eligible ones. The overall response rate (ORR) in the treatment with JAK inhibitors in clinical trials was 11-35%, although different subtypes of CTCL showed different ORRs. Mycosis fungoides showed an ORR of 14-45%, while subcutaneous-panniculitis-like T-cell lymphoma (SPTCL) displayed an ORR ranging from 75% to 100%. Five cases were reported having a relapse/incident of CTCL after using JAK inhibitors; of these, three cases were de novo CTCLs in patients under treatment with a JAK inhibitor due to refractory arthritis, and two cases were relapsed disease after graft-versus-host disease treatment following allo-SCT. In conclusion, using JAK inhibitors for CTCL treatment seems promising with acceptable side effects, especially in patients with SPTCL. Some biomarkers, like pS6, showed an association with better responses. Caution should be taken when treating patients with an underlying autoimmune disease and prior immunosuppression.
Published: 2024
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7. Investigation of sexual dysfunction and depression prevalence in neuromyelitis optica spectrum disorder.

Author: Mamaghani HJ, Erfani Z, Torkamand F, Vahabi SM, Erfani N, Vahabizad F, and Moghadasi AN
Abstract: Objective: To investigate the prevalence of sexual dysfunction (SD) and depression in patients with neuromyelitis optica (NMO), a demyelinating disorder of the central nervous system., Methods: A total of 110 NMO patients and 112 healthy individuals were included as a control group, and their SD was assessed using the Female Sexual Function Inventory (FSFI) and the International Index of Erectile Function (IIEF) for women and men, respectively. The FSFI categorizes female sexual dysfunction into six subscores, including libido, arousal, lubrication, orgasm, sexual satisfaction, and pain, while the IIEF categorizes male sexual dysfunction into five subscores, including sexual desire, erection, orgasm, intercourse satisfaction, and overall satisfaction., Results: SD was prevalent among NMO patients, with 78% of female patients and 63.2% of male patients reporting SD in at least one subscore. The severity of the disease, as measured by the Expanded Disability Status Scale (EDSS), was found to be significantly correlated with SD in all subscores, while the duration of the disease was only correlated with the overall satisfaction subscore in men and the pain subscore in women. Furthermore, SD was found to be significantly correlated with depression in these patients., Conclusion: The study highlights the importance of addressing SD and depression in NMO patients, as they adversely affect the quality of life. The findings suggest that the physical aspects of SD are mostly affected by the severity of the disease, while psychological aspects are highly correlated with the chronicity of the disease., (© 2023. Fondazione Società Italiana di Neurologia.)
Published: 2023
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8. Clinicopathological Characteristics of Bowen Disease: A 7-Year Study at a Referral Dermatology Hospital.

Author: Vahabi SM, Dehghan Nasiri MS, Esmaeili Tarki F, Mahmoudi H, Daneshpazhooh M, and Etesami I
Published: 2023
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9. Co-occurrence of celiac disease and glycogen storage disease in a five-year-old patient with diabetes mellitus; a case report.

Author: Khani S, Soheili A, Vahabi SM, Dara N, Sayyari A, Nilipour Y, Parvizi M, and Hosseini A
Abstract: A patient presented with edema, ascites and jaundice. Histologic report was consistent with Celiac Disease. Liver biopsy commensurate with Glycogen storage disease III, which was confirmed by genetic testing. A gluten-free diet was initiated. After 2 months, ascites was relieved, hepatic function was improved, and hepatic size reduced., Competing Interests: None of the authors of this article have a conflict of interest in this field., (© 2023 The Authors. Clinical Case Reports published by John Wiley & Sons Ltd.)
Published: 2023
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10. Applications of MCNP simulation in treatment planning: a comparative study.

Author: Vahabi SM and Shamsaie Zafarghandi M
Subjects: Cobalt Radioisotopes, Computer Simulation, Photons, Radiotherapy Dosage, Monte Carlo Method, Radiotherapy Planning, Computer-Assisted
Abstract: Monte Carlo codes have been used for approximately 80 years to solve various problems in medical physics. In this paper, the importance of the MCNPX code in treatment planning is highlighted. As illustrative examples of the role of MCNPX in this field, some dosimetric parameters, isodose distribution curves, and figures of merit (FOMs) were considered for photon beams of various energies. To the best of the authors' knowledge, such a systematic study has not been done before. Tissue-air ratio values were obtained as a function of depth in tissue as well as field size. The results of the simulations were in agreement within 3.5% with experimental results reported in the literature. Backscatter factor values were calculated as a function of beam energy, and found to be in agreement with published experimental values within 5.9%. The isodose curves for different conditions and beam arrangements were also simulated. Besides, FOMs were calculated for different radiation energies. All the results were in agreement with related data in the literature. It is concluded that the MCNPX code and the models developed in the present study can be used in different conditions where these parameters are involved, improving individualized treatment planning for individual patients.
Published: 2020
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11. Study on the dose modification factor of strut adjusted volume implant (SAVI) with a 169 Yb source using MCNP4C.

Author: Vahabi SM, Bahreinipour M, and Shamsaie Zafarghandi M
Subjects: Dose-Response Relationship, Radiation, Female, Humans, Phantoms, Imaging, Brachytherapy methods, Monte Carlo Method, Radiotherapy Dosage, Ytterbium chemistry
Abstract: The SAVI has gained widespread use for accelerated partial breast irradiation (APBI) brachytherapy. Treatments with SAVI produce inherent heterogeneities including variable backscatter due to proximity to the tissue-air interface and variable cavity contents, causing inaccuracy in the dose calculation. In this study, a model of SAVI with sources of 169 Yb developed recently was defined and simulations with MCNP4C code of Monte Carlo were performed through different scenarios to assess the effects of these heterogeneities on the dose distribution. The results showed that the dose delivered to target volume may be lower than the planned dose by up to 9-16%. Therefore, the therapy with 169 Yb must be viewed with caution and a correction factor must be applied in treatment planning systems. It was also observed that the presence of air cavity changed the relative dose 1% for a symmetric plan compared to a water cavity and up to 5% for an asymmetric plan. It was indicated that the dose modification factor (DMF) did not differ in any significant way to that of the symmetric plan. The effect of the composition on the DMF was negligible because the natures of water and breast tissue were approximately similar. All the obtained results indicated that should 169 Yb sources be used with SAVI applications, some amendments of treatment planning systems would be employed.
Published: 2018
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1. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

3. Topical immunotherapy with diphenylcyclopropenone in paediatric patients with alopecia areata-A retrospective study of 97 patients.

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5. Prediction of acute methanol poisoning prognosis using machine learning techniques.

6. JAK Inhibitors in Cutaneous T-Cell Lymphoma: Friend or Foe? A Systematic Review of the Published Literature.

7. Investigation of sexual dysfunction and depression prevalence in neuromyelitis optica spectrum disorder.

8. Clinicopathological Characteristics of Bowen Disease: A 7-Year Study at a Referral Dermatology Hospital.

9. Co-occurrence of celiac disease and glycogen storage disease in a five-year-old patient with diabetes mellitus; a case report.

10. Applications of MCNP simulation in treatment planning: a comparative study.

11. Study on the dose modification factor of strut adjusted volume implant (SAVI) with a 169 Yb source using MCNP4C.

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