<i>Triticeae</i> and Gluten: Genetic Bases of Wheat Allergies and Sensitivities <i>Triticeae</i>
Bibliographic record
Abstract
The Triticeae tribe, including major cereals such as wheat, oats, and rye, plays a crucial role in global agriculture and human nutrition. Gluten, the main protein complex in wheat, is indispensable in food processing due to its unique viscoelastic properties but is also a major concern for health issues such as celiac disease (CD), non-celiac wheat sensitivity (NCWS), and wheat allergies. This study aims to review the genetic bases of wheat allergies and sensitivities, including the structure and function of gluten proteins, the genetic variability of allergenic gluten proteins, and diagnostic methods. By exploring the mechanisms of NCWS and the genetic predisposition to CD, this study will also introduce the latest advances in genomic research and molecular breeding techniques, particularly the application of CRISPR/Cas9 gene editing technology in reducing wheat allergens. The goal is to integrate genomic approaches and advanced breeding techniques to develop hypoallergenic wheat varieties, thereby improving the safety and quality of wheat products to meet the growing consumer demand for allergen-free foods. This study hopes to provide new insights into the understanding of wheat allergies and sensitivities and offer scientific basis and practical guidance for developing safer and higher-quality wheat products in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".