Technical and physiological determinants of airway smooth muscle mass in endobronchial biopsy samples of asthmatic horses
Bibliographic record
Abstract
Morphometric analyses of endobronchial biopsies are commonly performed in asthma research but little is known about the technical and physiological parameters contributing to measurement variability. We investigated factors potentially affecting biopsy size, quality, and airway smooth muscle (ASM) content in heaves, an asthma-like disease of horses. Horses with heaves in clinical exacerbation (n = 6) or remission (n = 6) from the disease and six controls were studied using a crossover design. The effect of disease status, age, bronchodilation, biopsy forceps type, and carina size on total biopsy area (Atot), ASM area (AASM), ASM% (AASM/Atot), and histologic quality were assessed. Concordance among different measuring techniques was also assessed. Compared with other groups, horses with heaves in exacerbation yielded larger biopsies (P < 0.05). Better quality biopsies were obtained from carinae of small size compared with large ones (P = 0.02), and carina size and forceps type significantly affected the ASM content of the biopsy (interaction, P < 0.05). AASM increased with age only in heaves-affected horses (r = 0.9, P < 0.05), and ASM% was negatively correlated with pulmonary resistance at 5 Hz in heaves-affected horses (r = -0.74, P = 0.01), likely because of the increased thickness of the extracellular matrix layer in this group (P = 0.01). In conclusion, disease status, carina thickness, and the forceps used may significantly affect biopsy size, quality, and ASM content. Endobronchial biopsies are not appropriate samples for ASM quantification in heaves, and studies measuring ASM mass should not be compared when measuring techniques differ.
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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 | 0.002 |
| 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 | 0.000 | 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".