Comparative in-silico Study of The Major Prolamin Protein of Wheat and Rice in Relation to Celiac Disease
Bibliographic record
Abstract
Celiac disease, an autoimmune disease, practically an immune response to consuming gluten protein popularly present in wheat, rye and barley, resulting “leaky gut “an inflammation of small intestine inner lining. Since wheat is enough infamous for being responsible for either celiac disease or gluten sensitivity, rice has become the only reliable source of staple around the world among people with gluten sensitivity. Glutenin and gliadin are thought to be responsible for the problem and glutenin is thought to be non-toxic. Rice proteins is in rich glutelin (oryzenin) and prolamins. The most common type of glutelin is glutenin and more recent studies reveal that in primary amino acid structure, there’s great similarities and homogeneities between glutelin and gliadin. Not only that the comparison between the prolamin structure can be an indicator how it is related to even avenin sensitivity. Hence, having celiac disease can be triggered by consuming rice. Sequence alignment of proteins and phylogenetic tree has been done with these proteins to understand their homology and prediction of 3D structure. The superimposed structures of the proteins revealed the structural similarities and deviations as well as other enigmatic links with other allergens like cocosin.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".