Inhalant Mediated Allergy: Immunobiology, Clinical Manifestations and Diagnosis
Bibliographic record
Abstract
Inhalant allergen-mediated respiratory diseases, including asthma and allergic rhinitis, have become increasing global health issues. While air pollution is believed to favor allergic sensitization and intensify clinical symptoms of allergy, allergen sensitization can vary highly with geographical location, climate, and lifestyle differences. Pollen sensitization is higher in European countries, while dust mite is more common in regions with high humidity. Domestic pet sensitization is on the rising trend in industrialized nations, but the paradoxical effect of intensive cat exposure in early childhood is also observed. Clinical management of inhalant allergic diseases has greatly benefited from the immunological and mechanistic understanding of pathophysiology. In this review, we discuss the current knowledge on inhalant mediated allergic disorders with emphasis on (1) the major immune cells and relevant chemokines and cytokines in the sensitization and effector phase with aeroallergen exposure, (2) their manifestation in asthma and allergic rhinitis, (3) characterization of inhalant allergens, (4) chemical contributions to the development of allergic diseases, and (5) clinical diagnosis of aeroallergen sensitization and management of inhalant allergy. Knowledge on the role of Th2 skewing, IgE, basophil, mast cells, and eosinophils in respiratory allergic diseases are fundamental in the diagnosis and management of these disorders. Skin test, basophil activation test, and specific IgE component-resolved diagnostics are used for diagnosis and facilitate further management. Advances in the development of biologics and allergen-specific immunotherapy will strategize the future approaches in the clinical care of respiratory allergic diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".