Verma, Jitender S.Libertin, Claudia R.Gupta, YashKhanna, GeetikaKumar, RohitArora, Balvinder S.Krishna, LoveneeshFasina, Folorunso OludayoHittner, James B.Antoniades, Athosvan Regenmortel, Marc H. V.Durvasula, RaviKempaiah, PrakashaRivas, Ariel L.2022-07-262022-07-262022-02-24Verma, J.S., Libertin, C.R., Gupta, Y., Khanna, G., Kumar, R., Arora, B.S., Krishna, L., Fasina, F.O., Hittner, J.B., Antoniades, A., Van Regenmortel, M.H.V., Durvasula, R., Kempaiah, P. & Rivas, A.L. (2022) Multi-Cellular Immunological Interactions Associated With COVID-19 Infections. Frontiers in immunology 13:794006. doi: 10.3389/fimmu.2022.794006.1664-3224 (online)10.3389/fimmu.2022.794006https://repository.up.ac.za/handle/2263/86465To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a “reductionist” one, which analyzes each cell type separately, and (ii) a “non-reductionist” method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern’s immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data pointwide line of observations, which informed at a personalized level and identified 97.8% of all nonsurvivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the nonreductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.en© 2022 Verma, Libertin, Gupta, Khanna, Kumar, Arora, Krishna, Fasina, Hittner, Antoniades, van Regenmortel, Durvasula, Kempaiah and Rivas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).Pattern recognitionCutoff-freeError preventionBiological complexityPersonalized methodsMulti-cellularityPersonalized medicineCOVID-19 pandemicCoronavirus disease 2019 (COVID-19)Complete blood cell counts (CBC)Article