Repetitive invasive lung function maneuvers do not accentuate experimental fibrosis in mice
Bibliographic record
Abstract
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 use algorithm-based ventilation strategies including forced oscillation technique (FOT), negative pressure-driven forced expiratory (NPFE) and pressure-volume (PV) maneuvers via the FlexiVent system. This lung function test (LFT) is usually performed as end-point measurement only, requiring several mice for each time point to be analyzed. Repetitive lung function maneuvers would allow monitoring of a disease process within the same individual while reducing the 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 repetitive lung function maneuvers (up to four times) in one and the same mouse, both under healthy conditions and in a model of AdTGF-β1 induced lung fibrosis. In essence, we found that repetitive invasive lung function maneuvers were well tolerated and did not accentuate experimental lung fibrosis in mice. This study contributes to the 3R principle aiming to reduce the numbers of experimental animals used in biomedical research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".