Noninvasive and Invasive Pulmonary Function in Mouse Models of Obstructive and Restrictive Respiratory Diseases
Bibliographic record
Abstract
Pulmonary function analysis is an important tool in the evaluation of mouse respiratory disease models, but much controversy still exists on the validity of some tests. Most commonly used pulmonary function variables of humans are not routinely applied in mice, and the question of which pulmonary function is optimal for the monitoring of a particular disease model remains largely unanswered. Our study aimed to delineate the potential and restrictions of existing pulmonary function techniques in different respiratory disease models, and to determine some common variables between humans and mice. A noninvasive (unrestrained plethysmography) and two invasive pulmonary function devices (forced maneuvers system from Buxco Research Systems [Wilmington, NC] and forced oscillation technique from SCIREQ [Montreal, PQ, Canada]) were evaluated in well-established models of asthma (protein and chemical induced): a model of elastase-induced pulmonary emphysema, and a model of bleomycin-induced pulmonary fibrosis. In contrast to noninvasive tests, both invasive techniques were efficacious for the quantification of parenchymal disease via changes in functional residual capacity, total lung capacity, vital capacity, and compliance of the respiratory system. Airflow obstruction and airflow limitation at baseline were only present in emphysema, but could be significantly induced after methacholine challenge in mice with asthma, which correlated best with an increase of respiratory resistance. Invasive pulmonary functions allow distinction between respiratory diseases in mice by clinically relevant variables, and should become standard in the functional evaluation of pathological disease models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.004 |
| 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".