Uncovering novel therapeutic clues for hypercoagulable active ulcerative colitis: novel findings from old data
Bibliographic record
Abstract
Background: Hypercoagulability has been shown to act as an important component of ulcerative colitis (UC) pathogenesis and disease activity, and is strongly correlated with the occurrence of venous thromboembolism (VTE). This study aimed at providing novel therapeutic clues for hypercoagulable active UC. Methods: The coagulation score model was developed using VTE cohorts, and the predictive performance of this model was evaluated by coagulation subtypes of UC patients, which were clustered by the unsupervised method. Subsequently, the response of UC of distinct coagulation types, as identified by the coagulation scoring model, to different biological agents was evaluated. Immunoinflammatory cells and molecules that were associated with hypercoagulable active UC were explored by employing gene set variation analysis, single-sample gene set enrichment analysis, univariate logistic regression analysis, and immunohistochemistry. Results: A coagulation scoring model was established, which includes five key coagulation factors (ARHGAP35, CD46, BTK, C1QB, and F2R), and accurately distinguished the coagulation subtypes of UC. When comparing anti-TNF-α agents with other biological agents after determining the model, especially golimumab, it showed more effective treatment for hypercoagulable active UC. CXCL8 has been identified as playing an important role in the tightly interconnected network between the immune-inflammatory system and coagulation system in UC. Immunohistochemical analysis showed that the expression of CXCL8, BTK, C1QB, and F2R was upregulated in active UC. Conclusions: Anti-TNF-α agents have significant therapeutic effects on hypercoagulable active UC, and the strong association between CXCL8, hypercoagulation, and disease activity provides a novel therapeutic insight into hypercoagulable active UC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".