Micro-computed tomography of pulmonary fibrosis in mice induced by adenoviral gene transfer of biologically active transforming growth factor-β1
Bibliographic record
Abstract
BACKGROUND: Micro-computed tomography (micro-CT) is a novel tool for monitoring acute and chronic disease states in small laboratory animals. Its value for assessing progressive lung fibrosis in mice has not been reported so far. Here we examined the importance of in vivo micro-CT as non-invasive tool to assess progression of pulmonary fibrosis in mice over time. METHODS: Pulmonary fibrosis was induced in mice by intratracheal delivery of an adenoviral gene vector encoding biologically active TGF-β1 (AdTGF-β1). Respiratory gated and ungated micro-CT scans were performed at 1, 2, 3, and 4 weeks post pulmonary adenoviral gene or control vector delivery, and were then correlated with respective histopathology-based Ashcroft scoring of pulmonary fibrosis in mice. Visual assessment of image quality and consolidation was performed by 3 observers and a semi-automated quantification algorithm was applied to quantify aerated pulmonary volume as an inverse surrogate marker for pulmonary fibrosis. RESULTS: We found a significant correlation between classical Ashcroft scoring and micro-CT assessment using both visual assessment and the semi-automated quantification algorithm. Pulmonary fibrosis could be clearly detected in micro-CT, image quality values were higher for respiratory gated exams, although differences were not significant. For assessment of fibrosis no significant difference between respiratory gated and ungated exams was observed. CONCLUSIONS: Together, we show that micro-CT is a powerful tool to assess pulmonary fibrosis in mice, using both visual assessment and semi-automated quantification algorithms. These data may be important in view of pre-clinical pharmacologic interventions for the treatment of lung fibrosis in small laboratory animals.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".