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0 0.5 1 1.5 2+ Case 21% Improvement Relative Risk Melatonin for COVID-19  Zhou et al.  Prophylaxis Does melatonin reduce COVID-19 infections? PSM retrospective 26,779 patients in the USA Fewer cases with melatonin (p=0.011) Zhou et al., PLOS Biology, November 2020 Favors melatonin Favors control

A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19

Zhou et al., PLOS Biology, doi:10.1371/journal.pbio.3000970
Nov 2020  
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Melatonin for COVID-19
10th treatment shown to reduce risk in December 2020
*, now known with p = 0.0000002 from 18 studies.
Lower risk for mortality, ventilation, and recovery.
No treatment is 100% effective. Protocols combine complementary and synergistic treatments. * >10% efficacy in meta analysis with ≥3 clinical studies.
4,000+ studies for 60+ treatments.
PSM observational study with a database of 26,779 patients in the USA, showing significantly lower risk of PCR+ with melatonin usage.
risk of case, 21.1% lower, RR 0.79, p = 0.01, treatment 222 of 1,055 (21.0%), control 8,052 of 25,724 (31.3%), NNT 9.7, odds ratio converted to relative risk, PSM.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Zhou et al., 6 Nov 2020, retrospective, propensity score matching, USA, peer-reviewed, 18 authors.
This PaperMelatoninAll
A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19
Yadi Zhou, Yuan Hou, Jiayu Shen, Reena Mehra, Asha Kallianpur, Daniel A Culver, Michaela U Gack, Samar Farha, Joe Zein, Suzy Comhair, Claudio Fiocchi, Thaddeus Stappenbeck, Timothy Chan, Charis Eng, Jae U Jung, Lara Jehi, Serpil Erzurum, Feixiong Cheng
PLOS Biology, doi:10.1371/journal.pbio.3000970
The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56-0.91) is
Ethics statement Procedures follow institutional guidelines for research on the COVID-19 registry database and were approved by the Cleveland Clinic Foundation Institutional Review Board. The expression of TMPRSS2 in the epithelial cells in (F). (K and L) The expression levels of ACE2 and TMPRSS2 in inflamed versus uninflamed ileal absorptive enterocytes in Crohn disease patients. The single-cell data were retrieved from Martin et al. [72] , which contains 67,050 inflamed and uninflamed cells from the ileal samples of 8 patients with Crohn disease. (PDF) S12 Fig. Patient -based validation of drug repurposing for COVID-19 using 3 different disease subgroups. The disease subgroups are (A) asthma, (B) diabetes, and (C) hypertension. Four models were evaluated. These models were matched and adjusted using different variables, as shown in the table. The variable that was used to extract each patient subgroup was not used for propensity score matching or odds ratio adjustment. ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker. The up-and down-expressed genes in the 2 asthma datasets (GSE63142 and GSE130499, severe versus control) were computed against the up-and down-expressed genes from the SARS2-DEG dataset. Overall, the results show more significant network proximities and smaller Z scores than when the direction is not considered, as in Supporting information S1
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