Immunoblotting Analysis of Fruit Proteins in Mexican Pediatric Patients Suggests the Existence of New Allergens
Bibliographic record
Abstract
BACKGROUND: Food allergies are chronic diseases that compromise quality of life and can be potentially fatal due to anaphylaxis. The WHO estimates a 1-11% global prevalence, which has been increasing in recent years. They are considered, along with obesity, to be the two noninfectious pandemics. The WHO databases (WHO/IUIS) contain 403 food allergens, most of which have been reported from North America (Canada and the USA), Europe, and Asia, while reports of allergens from Latin America are scarce. Allergies have population and geographical specificities; therefore, identifying the main clinically relevant food allergens and potential new, undescribed components affecting Latin America is essential. This work aims to contribute to this field. METHODS: we gathered data from 16 allergic Mexican pediatric patients to fruits from the Rosaceae (pear and peach) and Musaceae (banana) families, as well as an allergic adult to Lauraceae (avocado). These fruits are prevalent allergens in Latin America. RESULTS: the data suggest that patients reacted to 20 different allergenic proteins reported in different allergen databases. Furthermore, we identified 16 previously unreported immunoreactive proteins, suggesting their potential role as new allergens. CONCLUSION: this preliminary work is particularly relevant, as it can influence the specific diagnosis of allergens most frequently affecting the pediatric population.
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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".