Comparison of house dust mite sensitization profiles in allergic adults from Canada, Europe, South Africa and USA
Bibliographic record
Abstract
BACKGROUND: Sensitization to house dust mite (HDM) is a leading cause of allergic rhinitis and asthma. Despite more than 30 HDM-derived allergens having been identified to date, specific therapeutic approaches do not yet take into account the local sensitization profiles of patients. This study aimed to identify patterns of HDM sensitization in HDM-allergic adults living in distinct geographic areas, to inform the development of targeted diagnostic and therapeutic tools. METHODS: Serum samples from 685 HDM-allergic subjects from Canada, Europe, South Africa, and the USA were tested for levels of IgE specific for 17 micro-arrayed HDM allergens by ImmunoCAP Immuno Solid-phase Allergen Chip (ISAC) technology. RESULTS: The results confirmed significant geographical variability in sensitization patterns and levels of IgE. In all areas, the major sensitizers were the group 1 and group 2 allergens and Der p 23. Der p 23 was a frequent sensitizer: 64% of the subjects had IgE specific for Der p 23, and 2.3% were monosensitized to it. In South Africa, Der p 23 was the dominant HDM allergen (86% prevalence) and Der p 7 achieved major allergen status (56%). IgE sensitization to HDM was influenced by asthmatic status, levels of allergen exposure, age, race-ethnicity and smoking status, but not by BMI. CONCLUSION: Sensitization profiles to HDM allergens differ considerably among distinct geographic areas, with Der p 7 and Der p 23 being major sensitizers in South Africa. Such heterogeneity should be taken into account in the diagnosis and treatment of HDM-allergic patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".