Functional lung imaging identifies peripheral ventilation changes in mice with muco-obstructive lung disease
Bibliographic record
Abstract
Abstract β-ENaC-Tg mice serve as a relevant model of muco-obstructive lung disease, with impaired mucociliary clearance, mucus obstruction, chronic airway inflammation, structural lung damage, and altered lung function. The aim of this study was to undertake a comprehensive lung function and mechanics analysis of the adult β-ENaC-Tg model. β-ENaC-Tg and wild-type littermates underwent X-ray Velocimetry (XV) scans using a Permetium XV scanner (4DMedical, Melbourne, Australia). For comparative lung mechanics, lung function assessments were conducted with a flexiVent system. XV imaging demonstrated elevated ventilation defect percentage, mean specific ventilation, and ventilation heterogeneity in β-ENaC-Tg mice. Spatial analysis of ventilation maps indicated increased ventilation variability in the peripheral lung regions, as well as an increased proportion of under-ventilated areas. The flexiVent analysis indicated that compared to wild-types, β-ENaC-Tg mice have a significantly more compliant lungs with increased inspiratory capacity, reduced tissue elastance and increased hysteresivity (heterogeneity), suggesting loss of parenchymal integrity. This research highlights the utility of XV imaging in evaluating ventilation defects in the β-ENaC-Tg model and provides a comprehensive lung function analysis.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".