Micro-computed tomography imaging of a rodent model of Chronic Obstructive Pulmonary Disease (COPD)
Bibliographic record
Abstract
Chronic obstructive pulmonary disease is projected to become the 3rd leading cause of death worldwide by 2030. Currently, 200 million people worldwide have been diagnosed with COPD, and many more are living with undiagnosed disease. COPD has no cure and no drugs that lead to improvements in long-term survival. Drug discovery is challenging due to a poor understanding of COPD pathogenesis. To study COPD, rodent models have been developed, with daily exposures to tobacco cigarette smoke over a 6-month period inducing symptoms. Measurements are typically done on histological slides, assessing airway wall thickening and markers of emphysema. These post-mortem techniques are unable to assess how the disease is progressing or how these observed structural changes impact lung function. To identify changes in lung structure and function in a smoking exposure model, we used respiratory-gated micro-computed tomography (micro-CT) and image-based measurements of lung structure and function. Micro-CT imaging was performed in anesthetized, free-breathing mice at baseline. The mice were then subjected to 6-months of exposure to tobacco cigarettes or ambient air, and rescanned. We also performed post-mortem lung compliance tests on 3-month smoke-exposed and age-matched control mice. Significant differences between smoke-exposed and control mice were observed for lung volume and functional residual capacity, which correlate well with the results of the lung compliance testing. In vivo respiratory-gated micro-CT in free-breathing animals is sensitive to changes in lung structure and function resulting from exposure to tobacco cigarette smoke, and is an effective tool to monitor the development of COPD in rodent models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".