Addressing proteolytic efficiency in enzymatic degradation therapy for celiac disease
Bibliographic record
Abstract
Celiac disease is triggered by partially digested gluten proteins. Enzyme therapies that complete protein digestion in vivo could support a gluten-free diet, but the barrier to completeness is high. Current options require enzyme amounts on the same order as the protein meal itself. In this study, we evaluated proteolytic components of the carnivorous pitcher plant (Nepenthes spp.) for use in this context. Remarkably low doses enhance gliadin solubilization rates, and degrade gliadin slurries within the pH and temporal constraints of human gastric digestion. Potencies in excess of 1200:1 (substrate-to-enzyme) are achieved. Digestion generates small peptides through nepenthesin and neprosin, the latter a novel enzyme defining a previously-unknown class of prolyl endoprotease. The digests also exhibit reduced TG2 conversion rates in the immunogenic regions of gliadin, providing a twin mechanism for evading T-cell recognition. When sensitized and dosed with enzyme-treated gliadin, NOD/DQ8 mice did not show intestinal inflammation, when compared to mice challenged with only pepsin-treated gliadin. The low enzyme load needed for effective digestion suggests that gluten detoxification can be achieved in a meal setting, using metered dosing based on meal size. We demonstrate this by showing efficient antigen processing at total substrate-to-enzyme ratios exceeding 12,000:1.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".