Effect of the oral thrombin inhibitor dabigatran on allergic lung inflammation induced by repeated house dust mite administration in mice
Bibliographic record
Abstract
Asthma is a chronic disease of the airways; asthma patients are hampered by recurrent symptoms of dyspnoea and wheezing caused by bronchial obstruction. Most asthma patients suffer from chronic allergic lung inflammation triggered by allergens such as house dust mite (HDM). Coagulation activation in the pulmonary compartment is currently recognized as a feature of allergic lung inflammation, and data suggest that coagulation proteases further drive inflammatory mechanisms. Here, we tested whether treatment with the oral thrombin inhibitor dabigatran attenuates allergic lung inflammation in a recently developed HDM-based murine asthma model. Mice were fed dabigatran (10 mg/g) or placebo chow during a 3-wk HDM airway exposure model. Dabigatran treatment caused systemic thrombin inhibitory activity corresponding with dabigatran levels reported in human trials. Surprisingly, dabigatran did not lead to inhibition of HDM-evoked coagulation activation in the lung as measured by levels of thrombin-antithrombin complexes and D-dimer. Repeated HDM administration caused an influx of eosinophils and neutrophils into the lungs, mucus production in the airways, and a T helper 2 response, as reflected by a rise in bronchoalveolar IL-4 and IL-5 levels and a systemic rise in IgE and HDM-IgG1. Dabigatran modestly improved HDM-induced lung pathology (P < 0.05) and decreased IL-4 levels (P < 0.01), without influencing other HDM-induced responses. Considering the limited effects of dabigatran in spite of adequate plasma levels, these results argue against clinical evaluation of dabigatran in patients with asthma.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".