Dabigatran aggravates topoisomerase I peptide-loaded dendritic cells-induced lung and skin fibrosis.
Bibliographic record
Abstract
OBJECTIVES: Dysregulated coagulation cascade has been implicated in development of fibrosis in systemic sclerosis (SSc). Thrombin, a key mediator of the coagulation pathway, has both proinflammatory and procoagulant properties. Here, we evaluated the efficacy of oral dabigatran, a direct thrombin inhibitor, on topoisomerase I dendritic cells (TOPOIA DCs)-induced lung and skin fibrosis, an experimental model of SSc. METHODS: Mice were repeatedly immunized with TOPOIA DCs. Dabigatran was administered in food either during the onset of fibrotic (late treatment) or inflammatory (early treatment) phase. RESULTS: Early administration of dabigatran caused an aggravation of pulmonary fibrosis associated with signs of severe perivascular inflammation while late treatment was not protective when compared to the untreated TOPOIA DCs group. Thrombin was increased in lungs of TOPOIA DCs immunized group and, paradoxically, further augmented by administration of dabigatran to immunized mice. As in lungs, early and not late drug administration exacerbated skin fibrosis. Moreover, early dabigatran treatment induced a profibrotic and inflammatory skin gene expression signature with upregulated expression of Col5a1, Timp1, Tweakr, Vwf, Il6, Il33, Il4 and Ifng. CONCLUSIONS: Dabigatran aggravated lung and skin fibrosis in a TOPOIA DCs-induced model of SSc-like disease. Therefore, our results argue against the use of dabigatran to treat patients with SSc.
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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 | 0.000 |
| 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) | 0.001 | 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".