In vivo measurements of lung function using respiratory-gated micro-computed tomography in a smoke-exposure model of chronic obstructive pulmonary disease
Bibliographic record
Abstract
Purpose: respiratory-gated micro computed tomography (micro-CT) imaging can noninvasively provide structural and functional information about the lungs in a cigarette-exposure model of chronic obstructive pulmonary disease in mice. Approach: Female C57BL/6 mice were exposed to cigarette smoke or ambient air for 1, 3, or 6 months. Each mouse received a respiratory-gated micro-CT scan at baseline and another scan following the exposure period, while anaesthetized and free-breathing. Images were obtained representing end-expiration and peak inspiration, and measurements were performed to characterize the lung structure and compute functional metrics. Following the final micro-CT session, the mice were euthanized and the lungs prepared for histology. Results: Following 6 months of smoke-exposure, the mice exhibited larger increases in end-expiration lung volume and functional residual capacity, and a reduction in weight gain when compared with air-exposed mice. The histogram of CT numbers in the lung obtained during end-expiration also showed a shift to lower CT numbers following 6 months of smoke-exposure, indicating increased air content within the lungs. The metrics suggested air-trapping in the lung, which is consistent with emphysema. In the 3-month exposure group, only the reduction in weight gain was significant compared with the air-exposed group. Histological analysis confirmed that the 6-month smoke-exposed mice likely developed centrilobular emphysema as measured by the mean linear intercept. Conclusions: Respiratory-gated micro-CT imaging of free-breathing mice at multiple respiratory phases is noninvasive and provides additional information about lung structure and function that complements postmortem techniques and could be used to monitor changes over time.
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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 | 0.000 |
| 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) | 0.001 | 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".