Back to the future: re-establishing guinea pig<i>in vivo</i>asthma models
Bibliographic record
Abstract
Research using animal models of asthma is currently dominated by mouse models. This has been driven by the comprehensive knowledge on inflammatory and immune reactions in mice, as well as tools to produce genetically modified mice. Many of the identified therapeutic targets influencing airway hyper-responsiveness and inflammation in mouse models, have however been disappointing when tested clinically in asthma. It is therefore a great need for new animal models that more closely resemble human asthma. The guinea pig has for decades been used in asthma research and a comprehensive table of different protocols for asthma models is presented. The studies have primarily been focused on the pharmacological aspects of the disease, where the guinea pig undoubtedly is superior to mice. Further reasons are the anatomical and physiological similarities between human and guinea pig airways compared with that of the mouse, especially with respect to airway branching, neurophysiology, pulmonary circulation and smooth muscle distribution, as well as mast cell localization and mediator secretion. Lack of reagents and specific molecular tools to study inflammatory and immunological reactions in the guinea pig has however greatly diminished its use in asthma research. The aim in this position paper is to review and summarize what we know about different aspects of the use of guinea pig in vivo models for asthma research. The associated aim is to highlight the unmet needs that have to be addressed in the future.
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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) | 0.001 | 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 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| 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".