In vivo characterization of lung morphology and function in anesthetized free-breathing mice using micro-computed tomography
Bibliographic record
Abstract
Lung morphology and function in human subjects can be monitored with computed tomography (CT). Because many human respiratory diseases are routinely modeled in rodents, a means of monitoring the changes in the structure and function of the rodent lung is desired. High-resolution images of the rodent lung can be attained with specialized micro-CT equipment, which provides a means of monitoring rodent models of lung disease noninvasively with a clinically relevant method. Previous studies have shown respiratory-gated images of intubated and respirated mice. Although the image quality and resolution are sufficient in these studies to make quantitative measurements, these measurements of lung structure will depend on the settings of the ventilator and not on the respiratory mechanics of the individual animals. In addition, intubation and ventilation can have unnatural effects on the respiratory dynamics of the animal, because the airway pressure, tidal volume, and respiratory rate are selected by the operator. In these experiments, important information about the symptoms of the respiratory disease being studied may be missed because the respiration is forced to conform to the ventilator settings. In this study, we implement a method of respiratory-gated micro-CT for use with anesthetized free-breathing rodents. From the micro-CT images, quantitative analysis of the structure of the lungs of healthy unconscious mice was performed to obtain airway diameters, lung and airway volumes, and CT densities at end expiration and during inspiration. Because the animals were free breathing, we were able to calculate tidal volume (0.09 +/- 0.03 ml) and functional residual capacity (0.16 +/- 0.03 ml).
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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".