COVID-19 Clinical Research Studies

The paper analyzes immune gene expression across nasal, buccal, and blood tissues to identify severity-related immune signatures in COVID-19 patients.

This study analyzes cell-free DNA in COVID-19 patients to show its potential for disease monitoring and tissue injury assessment.

This study identifies differentially expressed genes and pathways in SARS-CoV-2-infected cardiomyocytes, revealing immune-inflammatory responses and inhibited cellular functions contributing to myocardial injury.

The paper investigates how Th1, Th2, and Th17 inflammatory pathways interact with cardiometabolic proteins in COVID-19 patients using OLINK proteomics data.

The paper develops a gene expression risk score to predict severe COVID-19 illness using peripheral blood samples and validates it in independent cohorts.

This study examines how a major genetic risk factor for COVID-19 affects mortality and severity, finding stronger effects in younger adults.

The paper reports a genome-wide association study of COVID-19 in a Japanese population, identifying a novel risk variant associated with severe disease in younger patients.

The paper investigates the role of the SARS-CoV-2 infected pancreas secretome in multi-organ dysfunctions using scRNA-seq and identifies upregulated genes and secretory proteins linked to various organ systems and diseases.

This study identifies transcriptional changes in SARS-CoV-2 infected patients, showing upregulation of innate immunity and downregulation of sensory and neuro-related genes, linked to clinical symptoms.

The paper uses co-expression analysis to identify gene modules and hub genes associated with COVID19 in platelets, revealing pathways linked to platelet activation and thrombosis.

The paper presents an open-source platform hosting predictive models for COVID-19 diagnosis and prognosis, enabling clinicians to use validated models in clinical practice.

The paper identifies a genetic variant in the SELP gene associated with increased thrombosis risk in male COVID-19 patients, suggesting potential for anti-P-selectin antibody therapy.

The paper identifies potential imaging and therapeutic targets for severe COVID-19 inflammation by analyzing single-cell RNA-seq data from patient samples.

This paper uses Mendelian randomization to identify causal links between coagulation factors and COVID-19 severity, finding VWF and ADAMTS13 as significant factors.

The paper identifies a conserved 166-gene host immune signature across viral pandemics, with a 20-gene subset predicting disease severity and guiding therapeutic strategies.

The paper identifies high neutrophil count as a causal trait in severe COVID-19 and suggests CDK6 inhibitors as potential treatment.

The paper compares immune responses in influenza and SARS-CoV-2 infected patients and COVID-19 survivors using transcriptomic data, identifying distinct immune signatures and prognostic indicators.

The paper uses plasma proteomics to identify tissue-specific cell death and cell-cell interaction mediators in severe COVID-19 patients, linking them to survival outcomes and viral infection effects.

The paper extends a cancer biomarker data model to create a COVID-19 biomarker knowledgebase, integrating 138 biomarkers and 265 type combinations with ontological annotations.

The paper uses scRNA-seq to identify TROP2+ liver progenitor cells co-expressing ACE2 and TMPRSS2, suggesting they may be targets for SARS-CoV-2 infection and contribute to COVID-19-associated liver dysfunction.

The paper identifies a common inflammatory macrophage phenotype across severe COVID-19 and other inflammatory diseases, driven by IFN-γ and TNF-α, and suggests potential therapeutic targets.

The paper identifies shorter androgen receptor polyQ alleles as protective against severe COVID-19 in males using machine learning on whole-exome sequencing data from the GEN-COVID cohort.

The paper follows a hospital cohort of suspected COVID-19 cases in Brazil to identify the first cases and analyze symptom prevalence and risk factors.

This study uses cell-free DNA methylation profiling in plasma to identify organ-specific injury and predict COVID-19 severity in hospitalized patients.

The paper describes a biobanking initiative collecting genetic and clinical data from over 1,000 SARS-CoV-2-positive individuals to study multi-organ involvement and genetic factors in COVID-19 severity.

This study uses single-cell RNA sequencing to analyze Tocilizumab-treated PBMCs, showing reduced inflammatory gene expression in monocytes, supporting its potential to mitigate cytokine storms in COVID-19.

This study identifies 91 differentially expressed plasma proteins between severe and non-severe COVID-19 patients, highlighting potential biomarkers and therapeutic targets.

The paper identifies prevalent respiratory, nosocomial, and bacterial pathogens in Brazilian COVID-19 ICU patients and correlates their virulence factors with disease severity.

The paper identifies molecular signatures and dysregulated pathways in COVID-19 patients with varying disease severity using transcriptome sequencing of nasopharyngeal swabs.

The paper identifies upregulated procoagulant genes in SARS-CoV-2 infected pulmonary epithelial cells through RNA-seq analysis and pathway enrichment, suggesting fibrinolysis inhibition as a potential therapeutic target.