A comprehensive review on mango allergy: Clinical relevance, causative allergens, cross‐reactivity, influence of processing techniques, and management strategies
Bibliographic record
Abstract
Mangoes (Mangifera indica) are widely prized for their abundant nutritional content and variety of beneficial bioactive compounds and are popularly utilized in various foods, pharmaceuticals, and cosmetics industries. However, it is important to note that certain proteins present in mango can trigger various allergic reactions, ranging from mild oral allergy syndrome to severe life-threatening anaphylaxis. The immunoglobulin E-mediated hypersensitivity of mango is mainly associated with three major allergenic proteins: Man i 1 (class IV chitinase), Man i 2 (pathogenesis-related-10 protein; Bet v 1-related protein), and Man i 4 (profilin). Food processing techniques can significantly affect the structure of mango allergens, reducing their potential to cause allergies. However, it is worth mentioning that complete elimination of mango allergen immunoreactivity has not been achieved. The protection of individuals sensitized to mango should be carefully managed through an avoidance diet, immediate medical care, and long-term oral immunotherapy. This review covers various aspects related to mango allergy, including prevalence, pathogenesis, symptoms, and diagnosis. Furthermore, the characterization of mango allergens and their potential cross-reactivity with other fruits, vegetables, plant pollen, and seeds were discussed. The review also highlights the effects of food processing on mango and emphasizes the available strategies for managing mango 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".