The Etiology of COVID-19 in Silico by SARS-Cov-2 Infection with the Quantum Microrna Language-AI
Abstract
Objective: Coronavirus disease 2019 (COVID-19) is complexed infectious disease caused by severe respiratory syndrome (SARS) human coronavirus 2 (CoV-2). We have previously shown that the microRNA (miRNA) entangling sorter (METS) analysis with quantum miRNA/miRNA language is available for the etiology investigation in silico of human virus-associated diseases. To investigate COVID-19 etiology, SARS-CoV-2 infection was simulated by METS algorithm with artificial intelligence (AI) machine learning (MIRAI). Materials and Methods: The information of coronavirus was extracted from database. Putative CoV-2 miRNAs were predicted by functionally analogy analysis. Statistical data was calculated by Prediction One. Results: The quantum miRNA immunity was observed in SARS-CoV-2 infection. Acute inflammation and viral infection mechanisms in COVID-19 were independently shown in host and viral miRNA networks according to the output of MIRAI. SARS-CoV-2 infection induced IL-6 upregulation by downregulation of miR-98-5p hub, and hypoxia was induced protein HIF1A suppression by viral miRNAs. C1q complement inhibition was tuned by viral miRNAs. Conclusion: We found in silico that COVID-19 might show IL-6 production by host miRNAs, and hypoxic vascular hypertension and hypocomplementemia-like symptom by viral miRNAs.
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