Identification of New Anti-inflammatory Peptides from Zein Hydrolysate after Simulated Gastrointestinal Digestion and Transport in Caco-2 Cells
Bibliographic record
Abstract
Chronic inflammation is an underlying contributor to various chronic diseases. The objectives of this study were to investigate the anti-inflammatory activity of zein hydrolysate after simulated gastrointestinal digestion and Caco-2 cell absorption and to identify novel anti-inflammatory peptides after transport across Caco-2 cells. Three zein hydrolysates were prepared and further digested using gastrointestinal proteases; their transports were studied in Caco-2 cells. Anti-inflammatory activity was studied in endothelial EA.hy926 cells. Three zein hydrolysates and their digests significantly decreased the expression of tumor necrosis factor-α (TNF-α) induced pro-inflammatory vascular cell adhesion molecule-1 (VCAM-1) by 37.3-66.0%. Eleven novel peptides with 5-9 amino acid residues were sequenced; three peptides showed strong anti-inflammatory activity by inhibiting the VCAM-1 by 54-38.9% and intercellular cell adhesion molecule-1 (ICAM-1) by 36.5-28.6% at 0.2 mM. A new approach to identify novel anti-inflammatory peptides that could survive gastrointestinal digestion and absorption was developed.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".