Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Care without a compass: Including patients with cancer in COVID-19 studies.

ABSTRACT:

SUBMITTER: Friese CR

PROVIDER: S-EPMC8049404 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Care without a compass: Including patients with cancer in COVID-19 studies.

Friese Christopher R CR Choueiri Toni K TK Duma Narjust N Farmakiotis Dimitrios D Grivas Petros P Rini Brian I BI Shah Dimpy P DP Thompson Michael A MA Pergam Steven A SA Mishra Sanjay S Warner Jeremy L JL

Cancer cell 20210415 7

PMID: 33894136

Similar Datasets

A proteomic survival predictor for COVID-19 patients in intensive care

Project description:Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Instead, using plasma proteomes quantifying 302 plasma protein groups at 387 timepoints in 57 critically ill patients on invasive mechanical ventilation, we found 14 proteins that showed trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81, n=49). We tested the established predictor on an independent validation cohort (AUROC of 1.0, n=24). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.

2022-03-03 | PXD029009 | Pride

A multifactorial score including autophagy for prognosis and care of COVID-19 patients.

Project description: Not available

| S-EPMC7751655 | biostudies-literature

Cardiometabolic MicroRNAs are Associated with severeerity and 28-Day Intensive Care Unit Mortality in COVID-19 Patients

Project description:Coronavirus disease 2019 (COVID-19) can lead to multiorgan damage and fatal outcomes. MicroRNAs (miRNAs) are detectable in blood, reflecting cell activation and tissue injury. We performed small RNA-Seq in healthy controls (N=11), non-severe (N=18) and severe (N=16) COVID-19 patients

2021-11-17 | GSE176498 | GEO

SARS-CoV-2 RNAemia and proteomic biomarker trajectory inform prognostication in COVID-19 patients admitted to intensive care

Project description:Prognostic characteristics inform risk stratification in intensive care unit (ICU) patients with coronavirus disease 2019 (COVID-19). We obtained blood samples (n = 474) from hospitalized COVID-19 patients (n = 123), non-COVID-19 ICU sepsis patients (n = 25) and healthy controls (n = 30). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in plasma or serum (RNAemia) of COVID-19 ICU patients when neutralizing antibody response was low. RNAemia was associated with higher 28-day ICU mortality (hazard ratio [HR], 1.84 [95% CI, 1.22–2.77] adjusted for age and sex). In longitudinal comparisons, COVID-19 ICU patients had a distinct proteomic trajectory associated with RNAemia and mortality. Among COVID-19-enriched proteins, galectin-3 binding protein (LGALS3BP) and proteins of the complement system were identified as interaction partners of SARS-CoV-2 spike glycoprotein. Finally, machine learning identified ‘Age, RNAemia’ and ‘Age, pentraxin-3 (PTX3)’ as the best binary signatures associated with 28-day ICU mortality.

2021-04-26 | PXD024089 | Pride

SARS-CoV-2 RNAemia and proteomic biomarker trajectory inform prognostication in COVID-19 patients admitted to intensive care

2021-04-26 | PXD024026 | Pride

Severity of COVID-19 in Cancer patients versus patients without Cancer: A Propensity Score Matching Analysis.

Project description: Not available

| S-EPMC8120166 | biostudies-literature

Delayed Colorectal Cancer Care During Coronavirus Disease (COVID-19) Pandemic (DECOR-19)

Project description:To understand and analyse the global impact of COVID-19 on outpatient services, inpatient care, elective surgery, and perioperative colorectal cancer care, a DElayed COloRectal cancer surgery (DECOR-19) survey was conducted in collaboration with numerous international colorectal societies with the objective of obtaining several learning points from the impact of the COVID-19 outbreak on our colorectal cancer patients which will assist us in the ongoing management of our colorectal cancer patients and to provide us safe oncological pathways for future outbreaks.

| 2346925 | ecrin-mdr-crc

Insights into standards of care – dexamethasone and antibodies against COVID-19 in hamster models

Project description:We describe the cellular response to SARS-CoV-2 infections combined with antibody and/or dexamethasone treatment in Syrian and Roborovski dwarf hamsters

2021-12-18 | GSE191080 | GEO

Convalescent COVID-19 patients without comorbidities display similar immunophenotypes over time despite divergent disease severities

Project description:COVID-19, the disease caused by SARS-CoV-2 infection, can assume a highly variable disease course, ranging from asymptomatic infection, which constitutes the majority of cases, to severe respiratory failure. This implies a diverse host immune response to SARS-CoV-2. However, the immunological underpinnings underlying these divergent disease courses remain elusive. We therefore set out to longitudinally characterize immune signatures of convalescent COVID-19 patients stratified according to their disease severity. Our unique convalescent COVID-19 cohort consists of 74 patients not confounded by comorbidities. This is the first study of which we are aware that excludes immune abrogations associated with non-SARS-CoV-2 related risk factors of disease severity. Patients were followed up and analyzed longitudinally (2, 4 and 6 weeks after infection) by high-dimensional flow cytometric profiling of peripheral blood mononuclear cells (PBMCs), in-depth serum analytics, and transcriptomics. Immune phenotypes were correlated to disease severity. Convalescence was overall associated with uniform immune signatures, but distinct immune signatures for mildly versus severely affected patients were detectable within a 2-week time window after infection.

2021-07-30 | GSE181032 | GEO

Effects of Dexamethasone in COVID-19 patients

Project description:Dexamethasone improves the survival of COVID-19 patients in need of supplemental oxygen therapy. Hospitalized COVID-19 patients eligible for dexamethasone therapy were recruited from the general care ward in several centers in Greece and the Netherlands and whole blood transcriptomic analysis was performed before and after starting dexamethasone treatment. Peripheral blood mononuclear cells (PBMCs) were isolated from healthy individuals and COVID-19 patients and stimulated with inactivated SARS-CoV-2 ex vivo in the presence or absence of dexamethasone and their transcriptome was assessed.

2023-07-26 | GSE227585 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data