Overcoming Barriers
Bibliographic record
Abstract
OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a global pandemic involving >5 500 000 cases worldwide as of May 26, 2020. The culprit is the severe acute respiratory syndrome coronavirus-2, which invades cells by binding to ACE2 (angiotensin-converting enzyme 2). While the majority of patients mount an appropriate antiviral response and recover at home, others progress to respiratory distress requiring hospital admission for supplemental oxygen. In severe cases, deterioration to acute respiratory distress syndrome necessitating mechanical ventilation, development of severe thrombotic events, or cardiac injury and dysfunction occurs. In this review, we highlight what is known to date about COVID-19 and cardiovascular risk, focusing in on the putative role of the endothelium in disease susceptibility and pathogenesis. Approach and Results: Cytokine-driven vascular leak in the lung alveolar-endothelial interface facilitates acute lung injury in the setting of viral infection. Given that the virus affects multiple organs, including the heart, it likely gains access into systemic circulation by infecting or passing from the respiratory epithelium to the endothelium for viral dissemination. Indeed, cardiovascular complications of COVID-19 are highly prevalent and include acute cardiac injury, myocarditis, and a hypercoagulable state, all of which may be influenced by altered endothelial function. Notably, the disease course is worse in individuals with preexisting comorbidities that involve endothelial dysfunction and may be linked to elevated ACE2 expression, such as diabetes mellitus, hypertension, and cardiovascular disease. CONCLUSIONS: Rapidly emerging data on COVID-19, together with results from studies on severe acute respiratory syndrome coronavirus-1, are providing insight into how endothelial dysfunction may contribute to the pandemic that is paralyzing the globe. This may, in turn, inform the design of biomarkers predictive of disease course, as well as therapeutics targeting pathogenic endothelial responses.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.474 | 0.333 |
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".