MitraClip Insertion to Hasten Recovery from Severe COVID-19.
Ontology highlight
ABSTRACT:
SUBMITTER: Nagaraja V
PROVIDER: S-EPMC7566784 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-BSST416 | biostudies-other
Project description:Early in the COVID-19 pandemic, type 2 diabetes (T2D) was marked as a risk-factor for severe disease. Inflammation is central to the aetiology of both conditions where immune responses influence disease course. Identifying at-risk groups through immuno-inflammatory signatures can direct personalised care and help develop potential targets for precision therapy. This observational study characterised immunophenotypic variation associated with COVID-19 severity in T2D. Broad-spectrum immunophenotyping quantified 15 leukocyte populations in circulation from a cohort of 45 hospitalised COVID-19 patients with and without T2D. Lymphocytopenia, of CD8+ lymphocytes, was associated with severe COVID-19 and intensive care admission in non-diabetic and T2D patients. A morphological anomaly of increased monocyte size and monocytopenia of classical monocytes were specifically associated with severe COVID-19 in patients with T2D requiring intensive care. Over-expression of inflammatory markers reminiscent of the type-1 interferon pathway underlaid the immunophenotype associated with T2D. These changes may contribute to severity of COVID-19 in T2D. These findings show characteristics of severe COVID-19 in T2D as well as provide evidence that type-1 interferons may be actionable targets for future studies.
| S-SCDT-EMM-2020-13038 | biostudies-other
Project description:The study aims at investigating the specifical transcriptional signatures of severe COVID-19
2021-01-15 | GSE164805 | GEO
Project description:Analysis of COVID-19 hospitalized patients, with different kind of symptoms, by human rectal swabs collection and 16S sequencing approach.
2022-09-22 | E-MTAB-12236 | biostudies-arrayexpress
Project description:BackgroundLung function impairment persists in some patients for months after acute coronavirus disease 2019 (COVID-19). Long-term lung function, radiological features, and their association remain to be clarified.ObjectivesWe aimed to prospectively investigate lung function and radiological abnormalities over 12 months after severe and non-severe COVID-19.Methods584 patients were included in the Swiss COVID-19 lung study. We assessed lung function at 3, 6, and 12 months after acute COVID-19 and compared chest computed tomography (CT) imaging to lung functional abnormalities.ResultsAt 12 months, diffusion capacity for carbon monoxide (DLCOcorr) was lower after severe COVID-19 compared to non-severe COVID-19 (74.9% vs. 85.2% predicted, p < 0.001). Similarly, minimal oxygen saturation on 6-min walk test and total lung capacity were lower after severe COVID-19 (89.6% vs. 92.2%, p = 0.004, respectively, 88.2% vs. 95.1% predicted, p = 0.011). The difference for forced vital capacity (91.6% vs. 96.3% predicted, p = 0.082) was not statistically significant. Between 3 and 12 months, lung function improved in both groups and differences in DLCO between non-severe and severe COVID-19 patients decreased. In patients with chest CT scans at 12 months, we observed a correlation between radiological abnormalities and reduced lung function. While the overall extent of radiological abnormalities diminished over time, the frequency of mosaic attenuation and curvilinear patterns increased.ConclusionsIn this prospective cohort study, patients who had severe COVID-19 had diminished lung function over the first year compared to those after non-severe COVID-19, albeit with a greater extent of recovery in the severe disease group.
| S-EPMC9932828 | biostudies-literature
Project description:Lung transplantation can potentially be a life-saving treatment for patients with non-resolving COVID-19-associated respiratory failure. Concerns limiting transplant include recurrence of SARS-CoV-2 infection in the allograft, technical challenges imposed by viral-mediated injury to the native lung, and potential risk for allograft infection by pathogens associated with ventilator-associated pneumonia in the native lung. Most importantly, the native lung might recover, resulting in long-term outcomes preferable to transplant. Here, we report results of the first successful lung transplantation procedures in patients with non-resolving COVID-19-associated respiratory failure in the United States. We performed sm-FISH to detect both positive and negative strands of SARS-CoV-2 RNA in the explanted lung tissue, extracellular matrix imaging using SHIELD tissue clearance, and single cell RNA-Seq on explant and warm post-mortem lung biopsies from patients who died from severe COVID-19 pneumonia. Lungs from patients with prolonged COVID-19 were free of virus but pathology showed extensive evidence of injury and fibrosis which resembled end-stage pulmonary fibrosis. We used a machine learning approach to project single cell RNA-Seq data from patients with late stage COVID-19 onto a single cell atlas of pulmonary fibrosis, revealing similarities across cell lineages. There was no recurrence of SARS-CoV-2 or pathogens associated with pre-transplant ventilator associated pneumonias following transplantation. Our findings suggest that some patients with severe COVID-19 develop fibrotic lung disease for which lung transplantation is the only option for survival.
2020-10-28 | GSE158127 | GEO
Project description: Not available
| S-BSST1135 | biostudies-other
Project description:Single-cell RNA-seq analysis of PBMCs during COVID-19
2022-01-27 | GSE192391 | GEO
Project description:iCLIP data to identify OAS1 binding sites in uninfected and SARS-CoV-2 infected cells
2021-10-07 | GSE182394 | GEO
Project description:The etiology of severe forms of COVID19, especially in young patients, remains a salient unanswered question. Here we build on a 3-tier cohort where all individuals/patients were strictly below 50 years of age and where a number of comorbidities were excluded at study onset. Besides healthy controls (N=22), these include patients in the intensive care unit with Acute Respiratory Distress Syndrome (ARDS) (“critical group”; N=47), and those in a non-critical care ward under supplemental oxygen (“non-critical group”, N=25). This highly curated cohort allowed us to perform a deep multi-omics approach which included whole genome sequencing, whole blood RNA-sequencing, plasma and peripheral-blood mononuclear cells proteomics, multiplex cytokine profiling, mass-cytometry-based immune cell profiling in conjunction with viral parameters i.e. anti-SARS-Cov-2 neutralizing antibodies and multi-target antiviral serology. Critical patients were characterized by an exacerbated inflammatory state, perturbed lymphoid and myeloid cell compartments, signatures of dysregulated blood coagulation and active regulation of viral entry into the cells. A unique gene signature that differentiates critical from non-critical patients was identified by an ensemble machine learning, deep learning and quantum computing approach. Within this gene network, Structural Causal Modeling identified several ARDS driver genes, among which the up-regulated metalloprotease ADAM9 seems to be a key driver. Inhibition of ADAM9 ex vivo interfered with SARS-Cov-2 uptake and replication in human epithelial cells. Hence we apply a machine learning approach to identify driver genes for severe forms of COVID-19 in a small, uncluttered cohort of patients.
2021-11-25 | PXD025265 | Pride