Purification and characterization of antioxidant peptides from cooked eggs using a dynamic in vitro gastrointestinal model in vascular smooth muscle A7r5 cells
Bibliographic record
Abstract
Antioxidant peptides derived from food sources are considered as safer alternatives to commercially available antioxidant drugs. As one of the most abundant protein sources, hen's egg proteins were extensively used to produce antioxidant peptides by enzymatic hydrolysis. Our previous work indicated that gastrointestinal digestion of cooked eggs significantly increased the antioxidant activity due to hydrolysis of egg proteins. To characterize the responsible antioxidant peptides, cooked eggs were digested in a simulated in vitro model of human gastro-intestinal digestion. Prepared digests were fractionated with FPLC (Fast Protein Liquid Chromatography) and RP-HPLC (Reverse-Phase High-Performance Liquid Chromatography) and the antioxidant activity was determined in A7r5 cells (vascular smooth muscle cell line). Further identification of peptides from peptide fractions with the highest antioxidant activity was carried out using LC-MS/MS. Four peptides derived from ovalbumin, DSTRTQ (48-53), DKLPG (61-65), DVYSF (96-100), and ESKPV (205-209), were identified; of which DKLPG did not show antioxidant activity in cells. Enzyme cleave analysis suggested that these four peptides were likely released from ovalbumin only by pepsin non-specific cleaves. It is postulated that egg consumption may exert protection against oxidative stress on human health due to release of antioxidant peptides during digestion.
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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 | 0.000 | 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".