Purification and characterization of antioxidant peptides from cooked eggs using a dynamic in vitro gastrointestinal model in vascular smooth muscle A7r5 cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it