rAed a 4: A New 67-kDa <b><i>Aedes aegypti</i></b> Mosquito Salivary Allergen for the Diagnosis of Mosquito Allergy
Bibliographic record
Abstract
BACKGROUND: Accurate diagnosis of mosquito allergy has been hampered by the laborious task of obtaining mosquito salivary allergens. We have previously studied 3 recombinant (r) Aedes aegypti mosquito salivary allergens: rAed a 1, rAed a 2 and rAed a 3. Here, we report the expression, purification, identification and evaluation of rAed a 4, a 67-kDa α-glucosidase. METHODS: rAed a 4 was expressed using a baculovirus/insect cell system, purified by a combination of anion- and cation-exchange chromatography, and identified by immunoblotting. A. aegypti saliva extract was prepared in our laboratory. An indirect enzyme-linked immunosorbent assay (ELISA) was developed to measure rAed a 4-specific immunoglobulin E (IgE) and IgG antibodies in sera from 13 individuals with a positive mosquito-bite test from a laboratory-reared mosquito. Sera from 18 individuals with a negative bite test served as controls. RESULTS: Purified rAed a 4 bound to the IgE in mosquito-allergic sera, as detected by ELISA and immunoblotting. The binding of rAed a 4 to IgE could be inhibited in a dose-dependent manner by the addition of an A. aegypti extract. Mosquito-allergic individuals had significantly higher mean levels of rAed a 4-specific IgE and IgG than controls. Using the mean of the controls ± 2 SD as a cut-off level, 46% of the 13 allergic individuals had a positive IgE, while none of the controls was positive (p < 0.001). CONCLUSIONS: Aed a 4 is a major allergen in mosquito saliva. Its recombinant form has the hydrolase function and can be used for the diagnosis of mosquito allergy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".