Relationship of thrombospondin-1 and thrombospondin-2 with hematological, biochemical and inflammatory markers in COVID-19 patients
Bibliographic record
Abstract
Abstract Objectives Roles of thrombospondin-1 (TSP-1) and thrombospondin-2 (TSP-2) in tissue repair and inflammation are well-documented, but the association of their serum expressions with the pathogenesis of COVID-19 remains unclear. We investigate the roles of TSP-1 and TSP-2 in COVID-19. Methods 106 SARS-CoV-2 infected patients and 23 healthy people were enrolled in our study. COVID-19 patients were divided into two groups as non-severe and severe. TSP-1 and TSP-2 concentrations were measured with an enzyme-linked Immunosorbent Assay, and blood markers were analyzed with routine laboratory techniques. Results COVID-19 patients had significantly higher TSP-1 and TSP-2 levels than healthy controls. TSP-1 and TSP-2 positively correlated with inflammatory markers, including ESR, CRP, PCT, ferritin, and biochemical parameters such as ALT, AST, BUN, CK, and LDH. In addition, TSP-1 and TSP-2 were negatively correlated with hematological markers such as LYM, EOS, and HGB. Receiver operating characteristic analyses revealed that COVID-19 may be predicted with TSP-1 levels over 189.94 ng/mL and TSP-2 levels higher than 0.70 ng/mL. Conclusions Our analysis suggests that TSP-1 and TSP-2 expressions at the systemic level may have clinical importance for COVID-19.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".