Allergic Asthma: A Summary from Genetic Basis, Mouse Studies, to Diagnosis and Treatment
Bibliographic record
Abstract
Asthma is an allergic disease that affects approximately 300 million people worldwide. Two of its phenotypes routinely assessed at the clinic include airway hyperresponsiveness and IgE production. They can be measured in a non-invasive manner and have been used for genetic studies. The genetic complexity of asthma and its phenotypes makes it difficult to map their genetic contributors. Human studies require large sample sizes and proper segregation of the population to control for potential confounding factors. As an alternative, asthma genetics can be studied in mice due to the high degree of homology in the genome and immune response between mice and humans. The variety of mouse strains and allergic asthma protocols allow to study different aspects of the disease while controlling for the genetic background. Studying the genetic basis of asthma phenotypes has helped gain a better understanding of the disease mechanism. Candidate genes identified from genetic studies have served as targets for the development of new and specialized treatments. New treatments are high in demand as the symptoms of a large number of asthmatics are not properly controlled with the existing treatment guidelines involving corticosteroids, β2-adrenoreceptor agonists, and anti-leukotrienes or leukotriene modifiers. Promising findings have been obtained from studies exploring new treatments targeting specific immune cell mediators, which were identified as candidates in genetic studies, and cell adhesion molecules. In addition to targeting members of the Th1/Th2 inflammatory profile, mediators of the omega-3 fatty acid pathway are also emerging as novel targets of drug intervention for allergic asthma.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".