Cathepsin S Contributes to Lung Inflammation in Acute Respiratory Distress Syndrome
Bibliographic record
Abstract
Abstract Rationale Although the cysteine protease cathepsin S has been implicated in the pathogenesis of several inflammatory lung diseases, its role has not been examined in the context of acute respiratory distress syndrome, a condition that still lacks specific and effective pharmacological treatments. Objectives To characterize the status of cathepsin S in acute lung inflammation and examine the role of cathepsin S in disease pathogenesis. Methods Human and mouse model BAL fluid samples were analyzed for the presence and activity of cathepsin S and its endogenous inhibitors. Recombinant cathepsin S was instilled directly into the lungs of mice. The effects of cathepsin S knockout and pharmacological inhibition were examined in two models of acute lung injury. Protease-activated receptor-1 antagonism was used to test a possible mechanism for cathepsin S–mediated inflammation. Measurements and Main Results Pulmonary cathepsin S concentrations and activity were elevated in acute respiratory distress syndrome, a phenotype possibly exacerbated by the loss of the endogenous antiprotease cystatin SN. Direct cathepsin S instillation into the lungs induced key pathologies of acute respiratory distress syndrome, including neutrophilia and alveolar leakage. Conversely, in murine models of acute lung injury, genetic knockdown and prophylactic or therapeutic inhibition of cathepsin S reduced neutrophil recruitment and protein leakage. Cathepsin S may partly mediate its pathogenic effects via protease-activated receptor-1, because antagonism of this receptor abrogated cathepsin S–induced airway inflammation. Conclusions Cathepsin S contributes to acute lung injury and may represent a novel therapeutic target for acute respiratory distress syndrome.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".