Genetic dissection of airway responsiveness and its impact on the susceptibility to allergic asthma
Bibliographic record
Abstract
Asthma is a chronic, complex and inflammatory disease affecting both adults and children.The disease is generated by genetic and environmental factors which trigger an uncontrollable immune response to a variety of allergens resulting in symptoms such as shortness of breath, chest tightness, cough, wheezing and phenotypes such as eosinophilia, high IgE levels and increased airway responsiveness.To understand the mechanism underlying the pathogenesis of asthma, it is necessary to identify the factors that trigger, modulate or inhibit the inflammatory response of the airways.Since asthma is an inflammatory disease with several sub-phenotypes, many studies have looked for biomarkers that might predict the development or progression of the disease.These phenotypes should be objective, quantitative, affected by only one set of genetic factors and reproducible in animal models in order to assess them in systematic manner.Despite significant progress in genome-wide association and linkage studies of human populations, numerous genes have been postulated as being responsible for the development and severity of allergic asthma but none of them have fulfilled the criteria of an informative biomarker which could be linked to the development or progression of allergic asthma.IgE levels (p<0.001).Expression of SLPI was associated with a decreased inflammation in the lungs, plasma IgE levels, and lung resistance, whereas the ablation of SLPI had the opposite effect.Treatment with resiquimod increased the expression of SLPI and decreased inflammation in the lungs
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".