Modeling Asthma in Mice
Bibliographic record
Abstract
Clinical reports of areas of damaged airway epithelium associated with shed epithelial cells in bronchoalveolar lavage fluid, aberrant epithelial repair processes, and altered cytokine and growth factor release have highlighted some fundamental differences between the airway epithelium in individuals with and without asthma. However, the consequences of these epithelial changes are not clearly defined, and may be difficult to assess in the clinic. In this Review, we answer the two questions. (1) What in vivo models and methods have been used to inform us about airway epithelium damage, repair, and immune responses? Our response focuses on genetic influences as well as allergen exposure, environmental/chemical, and mechanical models. (2) How can we improve on existing mouse models to understand changes in airway epithelium biology in asthma? In answering the second question, we include exciting recent studies that have combined multiple exposure methods and/or epithelium-centric outcome measurements. By addressing these two questions, we propose that future interrogation of epithelial responses of both existing and nascent mouse models may provide greater understanding of the mechanisms underlying airway inflammation and remodeling in asthma with hope of generating novel therapeutic targets.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".