Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications
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Abstract
Coronavirus disease 2019 (COVID-19) has affected millions of people globally, yet how the human immune system responds to and influences COVID-19 severity remains unclear. Mathew et al. present a comprehensive atlas of immune modulation associated with COVID-19. They performed high-dimensional flow cytometry of hospitalized COVID-19 patients and found three prominent and distinct immunotypes that are related to disease severity and clinical parameters. Arunachalam et al. report a systems biology approach to assess the immune system of COVID-19 patients with mild-to-severe disease. These studies provide a compendium of immune cell information and roadmaps for potential therapeutic interventions.
Science , this issue p. eabc8511 , p. 1210
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SciScore for 10.1101/2020.05.20.106401: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: Patients, subjects, and clinical data collection: Patients admitted to the Hospital of the University of Pennsylvania with a SARS-CoV2 positive result were screened and approached for informed consent within 3 days of hospitalization. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Other statistical analysis was performed using Prism software (GraphPad). Prismsuggested: (PRISM, RRID:SCR_005375)GraphPadsuggested: (GraphPad Prism, RRID:SCR_002798)High dimensional data analysis of flow cytometry data: viSNE and FlowSOM analysis were … SciScore for 10.1101/2020.05.20.106401: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: Patients, subjects, and clinical data collection: Patients admitted to the Hospital of the University of Pennsylvania with a SARS-CoV2 positive result were screened and approached for informed consent within 3 days of hospitalization. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Other statistical analysis was performed using Prism software (GraphPad). Prismsuggested: (PRISM, RRID:SCR_005375)GraphPadsuggested: (GraphPad Prism, RRID:SCR_002798)High dimensional data analysis of flow cytometry data: viSNE and FlowSOM analysis were performed on Cytobank (https://cytobank.org). FlowSOMsuggested: (FlowSOM, RRID:SCR_016899)Cytobanksuggested: (Cytobank, RRID:SCR_014043)Resulting scores were hierarchically clustered using the hclust package in R. hclustsuggested: (HCLUST, RRID:SCR_009154)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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