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10.1615/CritRevImmunol.2020034784 http://scihub22266oqcxt.onion/10.1615/CritRevImmunol.2020034784 33389884!ä!33389884 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Crit+Rev+Immunol 2020 ; 40 (3): 195-203 Nephropedia Template TP {{tp|p=33389884|t=2020. Immune Computation and COVID-19 Mortality: A Rationale for IVIg |pdf=|usr=}}{{33389884}} gab.com Text Immune Computation and COVID-19 Mortality: A Rationale for IVIg JO: Crit Rev Immunol http://www.kidney.de/pget.php?&tw=1&pmid=33389884 #FOAvip COVID-19 infection tends to be more lethal in older persons than in the young; death results from an overactive inflammatory response, leading to cytokine storm and organ failure. Here we describe immune regulation of the inflammatory response phenotype as emerging from a process that is analogous to machine-learning algorithms used in computers. We briefly describe some strategic similarities between immune learning and computer machine learning. We reason that a balanced response to COVID-19 infection might be induced by treating the elderly patient with a wellness repertoire of antibodies obtained from healthy young people. We propose that a beneficial training set of such antibodies might be administered in the form of intravenous immunoglobulin (IVIg). Twit Text FOAVip Immune Computation and COVID-19 Mortality: A Rationale for IVIg ????Crit Rev Immunol http://www.kidney.de/pget.php?&tw=1&pmid=33389884 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33389884 #FOAvip English Wikipedia <ref>{{Cite pmid|33389884}}</ref> Immune Computation and COVID-19 Mortality: A Rationale for IVIg #MMPMID33389884 Cohen IR; Efroni S; Atlan H Crit Rev Immunol 2020[]; 40 (3): 195-203 PMID33389884show ga COVID-19 infection tends to be more lethal in older persons than in the young; death results from an overactive inflammatory response, leading to cytokine storm and organ failure. Here we describe immune regulation of the inflammatory response phenotype as emerging from a process that is analogous to machine-learning algorithms used in computers. We briefly describe some strategic similarities between immune learning and computer machine learning. We reason that a balanced response to COVID-19 infection might be induced by treating the elderly patient with a wellness repertoire of antibodies obtained from healthy young people. We propose that a beneficial training set of such antibodies might be administered in the form of intravenous immunoglobulin (IVIg). |Aged[MESH] |Aged, 80 and over[MESH] |Algorithms[MESH] |COVID-19 Serotherapy[MESH] |COVID-19/mortality/pathology/*therapy[MESH] |Cytokine Release Syndrome/mortality/therapy[MESH] |Humans[MESH] |Immunization, Passive/methods[MESH] |Immunoglobulins, Intravenous/*therapeutic use[MESH] |Inflammation/pathology/therapy[MESH] |Machine Learning[MESH]Immune Computation and COVID-19 Mortality: A Rationale for IVIg (epmc).pdf DeepDyve Pubget Overpricing