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10.1111/jcmm.17098 http://scihub22266oqcxt.onion/10.1111/jcmm.17098 35064759!8899198!35064759     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=35064759&cmd=llinks): Failed to open stream: HTTP request failed! 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Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks |pdf=|usr=}}{{35064759}} gab.com Text Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks JO: J Cell Mol Med http://www.kidney.de/pget.php?&tw=1&pmid=35064759 #FOAvip There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype. Twit Text FOAVip Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks ????J Cell Mol Med http://www.kidney.de/pget.php?&tw=1&pmid=35064759 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35064759 #FOAvip English Wikipedia <ref>{{Cite pmid|35064759}}</ref> Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks #MMPMID35064759 Asteris PG; Gavriilaki E; Touloumenidou T; Koravou EE; Koutra M; Papayanni PG; Pouleres A; Karali V; Lemonis ME; Mamou A; Skentou AD; Papalexandri A; Varelas C; Chatzopoulou F; Chatzidimitriou M; Chatzidimitriou D; Veleni A; Rapti E; Kioumis I; Kaimakamis E; Bitzani M; Boumpas D; Tsantes A; Sotiropoulos D; Papadopoulou A; Kalantzis IG; Vallianatou LA; Armaghani DJ; Cavaleri L; Gandomi AH; Hajihassani M; Hasanipanah M; Koopialipoor M; Lourenco PB; Samui P; Zhou J; Sakellari I; Valsami S; Politou M; Kokoris S; Anagnostopoulos A J Cell Mol Med 2022[Mar]; 26 (5): 1445-1455 PMID35064759show ga There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype. |*Neural Networks, Computer[MESH] |COVID-19/epidemiology/*genetics/*mortality[MESH] |Complement Activation/genetics[MESH] |Complement Factor H/genetics[MESH] |Complement System Proteins/genetics[MESH] |Female[MESH] |Greece/epidemiology[MESH] |Hospitalization/statistics & numerical data[MESH] |Humans[MESH] |Intensive Care Units/statistics & numerical data[MESH] |Male[MESH] |Middle Aged[MESH] |Models, Genetic[MESH] |Morbidity[MESH] |Polymorphism, Single Nucleotide[MESH]Genetic prediction of ICU hospitalization and mortality in COVID-19 pa(epmc).pdf DeepDyve Pubget Overpricing