Three-dimensional computed tomography imaging in an animal model of emphysema
Bibliographic record
Abstract
Emphysema is a major health problem and novel drugs are needed. Animal disease models are pivotal in their development, but the validity and sensitivity of current tools for the evaluation of drug efficacy is limited. The usefulness of micro computed tomography (CT) as an innovative tool to assess emphysema in a mouse model was investigated. Serial CT scans were performed in bi-weekly intervals in Smad3 knockout (KO) mice, which spontaneously develop airspace enlargement. Lung density was quantified in two- and three-dimensional images and correlated to mean linear intercept and lung compliance. CT scans of Smad3 KO lungs revealed a significant decrease in lung density at age 8 weeks and a further progression at age 14 weeks with respect to age-matched wild-type (WT) animals. Emphysema could be reliably assessed with both the two- and three-dimensional approach, but the three-dimensional approach was superior, due to normalisation to lung volumes and less variability. Lung compliance by week 14 was 0.053+/-0.005 and 0.034+/-0.002% of maximum volume.cmH(2)O(-1) for KO and WT mice, respectively, reflecting significant physiologically relevant emphysema. Small animal computed tomography imaging and density quantification in a reconstructed three-dimensional image is a useful tool for quantifying emphysematous changes in an animal disease model. It adds significant information to conventional assessment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".