Evaluation of allergic lung inflammation by computed tomography in a rat model<i>in vivo</i>
Bibliographic record
Abstract
The ability of micro-computed tomography (CT) to noninvasively evaluate allergic pulmonary inflammation in an experimental model was investigated. In addition, two image segmentation methods and the value of respiratory gating were investigated in the context of this model. Brown Norway rats were exposed to one of four doses of house dust mite (HDM) extract (0, 0.15, 15 or 150 microg) delivered intratracheally every 24 h for 10 days. CT scanning was performed at baseline and after several longitudinal HDM exposures. Both thoracic- and lung-segmentation methods yielded similar results when standardisation practices were employed. While tissue histology correlated well with CT images, cell counts from bronchoalveolar lavage depicted greater inflammation than did density measures from CT images. Evidence from representative CT slices and transaxial density distribution indicated that inflammation was primarily associated with major airways and extended into the periphery from these focal points. Respiratory gating demonstrated that images of the inspiratory state provided greater contrast of inflammatory processes. Lastly, decreases in tidal volumes indicated significant mechanical respiratory changes in animals exposed to both 15 and 150 microg. In summary, CT image segmentation can extract pertinent data on in vivo allergic airway/lung inflammation. Furthermore, respiratory gating provides additional contrast and insight into these quantification practices.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".