What makes protein indigestible from tissue‐related, cellular, and molecular aspects?
Bibliographic record
Abstract
This paper gives an insight into key factors, which impair enzymatic protein digestion. By nature, some proteins in raw products are already poorly digestible because of structural peculiarities, or due to their occurrence in plant cytoplasmic organelles or in cell membranes. In plant-based protein, molecular and structural changes can be induced by genetic engineering, even if protein is not a target compound class of the genetic modification. Other proteins only become difficult to digest due to changes that occur during the processing of proteinaceous products, such as extruding, boiling, or acidic or alkaline treatment. The utilization of proteinaceous raw materials in industrial fermentations can also have negative impacts on protein digestibility, when reused as fermentation by-products for animal nutrition, such as brewers' grains. After consumption, protein digestion can be impeded in the intestine by the presence of antinutritional factors, which are ingested together with the food or feedstuff. It is concluded that the encircling matrix, but also molecular, chemical, and structural peculiarities or modifications to amino acids and proteins obstruct protein digestion by common proteolytic enzymes in humans and animals.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".