Sustained Expression of Plasminogen Activator Inhibitor-1 in Patients Recovered From COVID-19 Disease
Bibliographic record
Abstract
Objectives: The overexpression of plasminogen activator inhibitor-1 (PAI-1) was frequently observed during coronavirus disease 2019 (COVID-19), and it was found to be closely associated with disease severity. We have analyzed the PAI-1 status in fully recovered post-COVID patients. SUBJECTS AND METHODS: In a case-control and cross-sectional study, we compared 377 patients, 30-210 days after PCR-verified COVID-19 and 884 COVID-naive controls. RESULTS: Post-COVID patients ("cases") showed significantly higher plasma PAI-1 concentrations than COVID-naive controls. This difference remained significant even after complex adjustment by multiple regression. On the other hand, since the strongest covariate of increased PAI-1 was antihypertensive treatment, the difference between cases and controls in those who were on antihypertensives completely disappeared. In the subgroup of post-COVID patients only, we also found that highly symptomatic patients or those who required hospitalization in the acute phase showed significantly higher PAI-1 than patients with only mild symptoms of the disease. Similarly, the presence of β mutation increased the relative risk (≈11 times) of high post-COVID concentrations of PAI-1. Similarly, the presence of β mutation increased the relative risk (≈11 times) of high post-COVID concentrations of PAI-1. CONCLUSIONS: Increased values of PAI-1 can persist for several months after complete recovery from COVID-19 (namely, by β variant of the virus), and their expression also corresponded to clinical course of the disease. .
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".