Clara Cell Secretory Protein Is Reduced in Equine Recurrent Airway Obstruction
Bibliographic record
Abstract
Horses are prone to recurrent airway obstruction (RAO), an inflammatory lung disease induced by repeated exposure to environmental mold, dust, and bacterial components. Active disease manifests with mucus hyperproduction, neutrophilic inflammation, bronchoconstriction, and coughing. Chronically affected animals have lung remodeling characterized by smooth muscle hyperplasia, collagen deposition, lymphoid hyperplasia, and impaired aerobic performance. Clara cell secretory protein (CCSP) counters inflammation in the lung, hence we hypothesized that CCSP depletion is a key feature of RAO in horses. Recombinant equine CCSP and specific antiserum were produced, and percutaneous lung biopsies were obtained from 3 healthy horses and from 3 RAO-affected horses before and after induction of RAO. CCSP relative gene expression in tissue, as well as protein concentration in lung lavage fluid, was determined. Immunocytochemical analysis, using both light and immunogold ultrastructural methods, demonstrated reduced CCSP staining in lung tissue of animals with RAO. Immunogold label in Clara cell granules was less in animals with chronic RAO than in normal animals, and absent in animals that had active disease. Median lung lavage CCSP concentration was 132 and 129 ng/ml in healthy horses, and 62 and 24 ng/ml in RAO horses before and after challenge, respectively. CCSP lung gene expression was significantly higher in healthy animals than in animals with chronic RAO. Together, these preliminary findings suggest that reduced production of CCSP and subcellular changes in Clara cells are features of chronic environmentally induced lung inflammation in horses.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".