Abstract 111: Multiparametric Circulating Cardiovascular Biomarkers Elucidate A Molecular Signature Of Coronavirus Disease 2019 Mortality And Identify Vascular Barrier Stabilizing Therapies
Bibliographic record
Abstract
Background: Endothelial cell (EC) activation, endotheliitis, vascular permeability, and thrombosis have been observed in patients with severe COVID-19, indicating that the vasculature is affected during the acute stages of SARS-CoV-2 infection. It remains unknown whether circulating vascular markers are sufficient to predict clinical outcomes, unique to COVID-19 related pathology, and if vascular permeability can be therapeutically targeted. Methods: This is a secondary analysis of a prospectively recruited longitudinal multicenter cohort study enrolling 241 patients with suspected SARS-CoV-2 infection. The prevalence of circulating inflammatory, cardiac, EC activation, and the entirety of the microRNA transcriptome was evaluated, and the prognostic value assessed using a Random Forest model machine learning approach. Ex vivo experiments were performed to assess EC permeability responses to patient plasma and to derive modulated gene regulatory networks from which rational therapeutic selection could be inferred. Results: Multiple inflammatory and EC activation biomarkers were associated with mortality in COVID-19 patients and in severity-matched SARS-CoV-2-negative patients. In contrast, dysregulation of particular microRNAs at presentation was specific for poor COVID-19-related outcomes and revealed disease-relevant pathways. Integrating datasets (i.e., clinical, protein, and microRNA) using a machine learning approach further enhanced clinical risk prediction for in-hospital mortality. Exposure of ECs to COVID-19 patient plasma resulted in severity-specific gene expression responses and EC barrier dysfunction. ECs treated with patient plasma showed increased vascular permeability, which was ameliorated by treatment with the vascular stabilizing molecules angiopoietin-1 mimetic or recombinant Slit2-N, but not nangibotide or dexamethasone. Conclusions: Integration of multi-omics data identified microRNA and vascular biomarkers prognostic of in-hospital mortality in COVID-19 patients and revealed that vascular stabilizing therapies should be explored as a treatment for endothelial dysfunction in COVID-19, and other severe diseases where endothelial dysfunction has a central role in pathogenesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".