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: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:iCLIP data to identify OAS1 binding sites in uninfected and SARS-CoV-2 infected cells
2021-10-07 | GSE182394 | GEO
Project description:Single-cell RNA-seq analysis of PBMCs during COVID-19
2022-01-27 | GSE192391 | GEO
Project description:Severe disease of SARS-CoV-2 is characterized by vigorous inflammatory responses in the lung, often with a sudden onset after 5-7 days of stable disease. Efforts to modulate this hyperinflammation and the associated acute respiratory distress syndrome rely on the unraveling of the immune cell interactions and cytokines that drive such responses. Given that every patient is captured at different stages of infection, longitudinal monitoring of the immune response is critical and systems-level analyses are required to capture cellular interactions. Here, we report on a systems-level blood immunomonitoring study of 37 adult patients diagnosed with COVID-19 and followed with up to 14 blood samples from acute to recovery phases of the disease. We describe an IFNγ-eosinophil axis activated before lung hyperinflammation and changes in cell-cell co-regulation during different stages of the disease. We also map an immune trajectory during recovery that is shared among patients with severe COVID-19.
| S-EPMC7405891 | biostudies-literature
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