I<scp>nteraction of</scp> P<scp>lant</scp> P<scp>olyphenols with</scp> S<scp>alivary</scp> P<scp>roteins</scp>
Bibliographic record
Abstract
Tannins are polyphenols that occur widespread in plant-based food. They are considered to be part of the plant defense system against environmental stressors. Tannins have a number of effects on animals, including growth-rate depression and inhibition of digestive enzymes. Tannins also have an effect on humans: They are, for example, the cause of byssinosis, a condition that is due to exposure to airborne tannin. Their biological effect is related to the great efficiency by which tannins precipitate proteins, an interaction that occurs by hydrophobic forces and hydrogen bonding. Two groups of salivary proteins, proline-rich proteins and histatins, are highly effective precipitators of tannin, and there is evidence that at least proline-rich proteins act as a first line of defense against tannins, perhaps by precipitating tannins in food and preventing their absorption from the alimentary canal. Proline plays an important role in the interaction of proline-rich proteins with tannins. In contrast, it is primarily basic residues that are responsible for the binding of histatins to tannin. The high concentration of tannin-binding proteins in human saliva may be related to the fruit and vegetable diet of human ancestors.
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) | 0.000 | 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) | 0.018 | 0.003 |
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".