Patterns of Insect Venom Hypersensitivity in Patients with Asthma: Is There a Difference Between Eastern and Western Europe?
Bibliographic record
Abstract
Hous dust mites and pollen are well-known triggers for asthma. However, the prevalence of insect venom hypersensitivity among asthmatic populations remains underexplored and may vary across different regions. Aim of the study: To compare the rate of sensitization to insect venom in asthmatic patients (pts) in two major cities in Eastern and in Western Europe. Study population: Pts with confired diagnosis of asthma, residing in Dnipro (Ukraine) and Leipzig (Germany). Methods: Medical history, total serum IgE, and specific IgE (sIgE) to insect venom allergen extracts and allergen molecules were evaluated. Results: 120 pts from Leipzig (45 men, aged 18-63 years) and 74 pts from Dnipro (41 men, aged 18-70 years) were included in the study. The total serum IgE in the Dnipro pts was 472.4±123.45 kU/L, (4-3846 kU/L). In the Leipzig group, the total serum IgE was lower, with a mean of 276.9±576.2 kU/L, (5-3625 kU/L). Despite a history of allergy, 38 patients (51.35%) in Dnipro and 65 (54.6%) in Leipzig had total serum IgE<100 kU/L. Based on serum sIgE, the rate of hypersensitivity to insect venom allergen molecules in Dnipro and Leipzig was as follows: Api m1 – 4.1% vs 4.2%, Api m10 – 1.4% vs 18%, Ves v 2.8% vs 50.8% and Ves v5 – 9.5% vs 22.5%. Conclusions: This study highlights regional differences in insect venom hypersensitivity among asthmatic patients in Eastern and Western Europe. In Leipzig, higher sensitivity to Api m10 and Ves v was observed. Significant proportion of pts in both regions had IgE<100 kU/L, despite a history of allergy. This underscores the importance of specific diagnostic markers, like sIgE, in assessing sensitization and guiding treatment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".