SARS-CoV-2 mediated monocytes dysregulation may contribute to increase risk of cardiovascular risk in severe COVID-19 patients
Bibliographic record
Abstract
Abstract Severe Acute Respiratory Syndrome Coronavirus 2 (SARS□CoV□ 2) is a highly pathogenic corona virus which causes COVID-19 and resulted in millions of deaths and led to a global public health emergency. An effective and appropriate immune response is essential to control and eliminate viral infections, however, dysregulated immune responses may lead to immunopathology in viral infections. Clinical data showed that COVID-19 may promote the development of cardiovascular disorders (CVDs). However, exact mechanism associated with CVDs in COVID-19 patients is currently unknown. Monocytes are one of the major immune cell subsets that contribute to host defense against many infections but also associated with immunopathology and development of CVDs. Thus, in this study we investigated the role of monocyte subsets in COVID-19 associated CVDs. By using high dimensional flowcytometry, immune biomarker assays and multi variant data analysis in healthy donors (n=17), severe-COVID-19 patients (n=20) (ICU) and COVID-19 recovered individuals (n=30), we found elevated intermediate monocytes and Monocyte chemoattractant protein-1, one of the most important chemokines that regulates migration and infiltration of monocytes to the subendothelial space, where they may become foam cells. Spearman correlation network analysis showed that strong correlation between increase intermediate monocytes in the whole blood and plasma cardimetabolic biomarkers such as fatty acid binding protein 4, C-reactive protein, soluble CD14 and LPS binding protein in ICU patients. Our data show for the first time that the possible role of monocytes in the development of CVDs in ICU patients.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".