Immunoblotting Analysis of Fruit Proteins in Mexican Pediatric Patients Suggests the Existence of New Allergens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 it