<i>Ex vivo</i> micro-computed tomography analysis of bleomycin-induced lung fibrosis for preclinical drug evaluation
Bibliographic record
Abstract
Research into the pathogenesis underlying the development of idiopathic pulmonary fibrosis is hampered by a repertoire of animal models that fail to recapitulate all the features of the human disease. Better use and understanding of what the animal models represent may improve clinical predictability. We interrogated ex vivo micro-computed tomography (CT) as a novel end-point measure in the mouse model of bleomycin-induced lung fibrosis (BILF), and to evaluate a therapeutic dosing regimen for preclinical drug evaluation. A detailed characterisation of BILF was performed using standard end-point measures (lung hydroxyproline and histology). High resolution micro-CT (∼13.7 μm voxel size) was evaluated for quantifying the extent and severity of lung fibrosis. The period from 14 to 28 days following bleomycin instillation represents progression of established fibrosis. A therapeutic dosing regimen during this period was validated using a transforming growth factor-β receptor-1 kinase inhibitor, and micro-CT provided a highly sensitive and quantitative measure of fibrosis. Moreover, fibrotic lesions did not completely resolve, but instead persisted for ≥6 months following a single insult with bleomycin. Ex vivo micro-CT analysis of BILF allows robust evaluation of therapeutic dosing once fibrosis is already well established, requiring fewer mice than conventional biochemical end-points.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".