Micro-CT and <sup>18</sup> F-FDG micro-PET of pulmonary fibrosis in mice induced by adenoviral gene transfer of TGF-β1
Bibliographic record
Abstract
Introduction: The morphological and functional information provided by micro-CT and micro-PET allows monitoring of acute and chronic disease states in small laboratory animals. We examined in-vivo micro-CT and micro-PET as non-invasive tools to assess pulmonary fibrosis in mice. Material/Methods: Pulmonary fibrosis was induced in mice by intratracheal delivery of an adenoviral gene vector encoding biologically active TGF-β1. Respiratory gated and ungated micro-CT was performed in 18 mice at 1 to 4 weeks after pulmonary adenoviral gene vector delivery. In 5 additional mice 18 F-FDG micro-PET and micro-CT was performed. Imaging was correlated to histopathology and findings in animals exposed to a control vector. Radiation doses were measured using thermoluminescence dosimeters. Results: Significant correlation between Ashcroft histology scoring and micro-CT was found for visual assessment scoring (p<0.001) and automated quantification by a region growing segmentation algorithm (p=0.004 for gated and p=0.006 for ungated exams). 18 F-FDG micro-PET showed slight increase of glucose metabolism in the consolidated lung areas determined by micro-CT, which was coregistered to the micro-PET data using anatomical landmarks. Radiation doses for micro-CT ranged from 174 to 277 mSv. For micro-PET an expected dose of 140 mSv was calculated from the measurements. Conclusion: Micro-CT and micro-PET allow valid visualisation of morphology and metabolism for the assessment of fibrosis in mice. The measured radiation doses allow serial examinations without deterministic radiation effects.
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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 | 0.001 |
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".