Repeated lung function measurements do not accentuate experimental lung fibrosis in mice
Bibliographic record
Abstract
Besides imaging techniques, assessment of lung function is an important clinical tool for the diagnosis and monitoring of chronic lung diseases, including idiopathic pulmonary fibrosis (IPF). In mice, lung function maneuvers are usually performed using algorithm-based ventilation strategies including forced oscillation technique (FOT), negative pressure-driven forced expiratory (NPFE) and pressure-volume (PV) maneuvers via the FlexiVent system. Lung function tests (LFTs) in mice require a tracheotomy and therefore are performed as end-point measurement only. For this reason, several mice are needed for each time point to be analyzed. Repeated LFT would allow monitoring of a disease process in one and the same individual while reducing numbers of laboratory animals. However, its feasibility in mice and impact on developing lung fibrosis has not been studied so far. Using orotracheal cannulation without surgical exposure of the trachea, we examined the tolerability to repeated LFT (up to four times) in one and the same mouse, both under healthy conditions and in a model of AdTGF-β1 induced lung fibrosis. Together, our data show that repeated LFTs were feasible and well tolerated in the same mice with developing lung fibrosis and allowed a detailed longitudinal evaluation of lung mechanics. Implementation of reproducible, repeated lung function analyses will help to reduce numbers of animals in experimental studies.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".