Genetic and Pharmacological Evaluation of Cathepsin S in a Mouse Model of Asthma
Bibliographic record
Abstract
Cathepsin S (Cat S) is predominantly expressed in antigen-presenting cells and is up-regulated in several preclinical models of antigen-induced inflammation, suggesting a role in the allergic response. Prophylactic dosing of an irreversible Cat S inhibitor has been shown to attenuate pulmonary eosinophilia in mice, supporting the hypothesis that Cat S inhibition before the initiation of airway inflammation is beneficial in airway disease. In addition, Cat S has been shown to play a role in more distal events in the allergic response. To determine where Cat S inhibition may affect the allergic response, we used complementary genetic and pharmacological approaches to investigate the role of Cat S in the early and downstream allergic events in a murine model of antigen-induced lung inflammation. Cat S knockout mice did not develop ovalbumin-induced pulmonary inflammation, consistent with a role for Cat S in the development of the allergic response. Alternatively, wild-type mice were treated with a reversible, highly selective Cat S inhibitor in prophylactic and therapeutic dosing paradigms and assessed for changes in airway inflammation. Although both treatment paradigms resulted in potent Cat S inhibition, only prophylactic Cat S inhibitor dosing blocked lung inflammation, consistent with our findings in Cat S knockout mice. The findings indicate that although Cat S is up-regulated in allergic models, it does not appear to play a significant role in the downstream effector inflammatory phase in this model; however, our results demonstrate that Cat S inhibition in a prophylactic paradigm would ameliorate airway inflammation.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".