Endothelium Infection and Dysregulation by SARS-CoV-2: Evidence and Caveats in COVID-19
Bibliographic record
Abstract
The ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by the acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) poses a persistent threat to global public health. Although primarily a respiratory illness, extrapulmonary manifestations of COVID-19 include gastrointestinal, cardiovascular, renal and neurological diseases. Recent studies suggest that dysfunction of the endothelium during COVID-19 may exacerbate these deleterious events by inciting inflammatory and microvascular thrombotic processes. Although controversial, there is evidence that SARS-CoV-2 may infect endothelial cells by binding to the angiotensin-converting enzyme 2 (ACE2) cellular receptor using the viral Spike protein. In this review, we explore current insights into the relationship between SARS-CoV-2 infection, endothelial dysfunction due to ACE2 downregulation, and deleterious pulmonary and extra-pulmonary immunothrombotic complications in severe COVID-19. We also discuss preclinical and clinical development of therapeutic agents targeting SARS-CoV-2-mediated endothelial dysfunction. Finally, we present evidence of SARS-CoV-2 replication in primary human lung and cardiac microvascular endothelial cells. Accordingly, in striving to understand the parameters that lead to severe disease in COVID-19 patients, it is important to consider how direct infection of endothelial cells by SARS-CoV-2 may contribute to this process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".