New insights in biologically active proteins and peptides derived from hen egg
Bibliographic record
Abstract
Bioactive peptides are specific protein fragments that positively impact the body's function or condition and ultimately may influence health. These peptides are inactive within the sequence of the parent protein and can be released during proteolysis or fermentation. They may exert a number of different activities in vivo, affecting cardiovascular, endocrine, immune and nervous system in addition to nutrient utilization. Hen eggs have traditionally been recognized as an excellent source of protein, vitamins and minerals. Research in the past decade, however, has produced a substantial amount of evidence indicating that hen egg proteins and peptides may exert several diverse biological effects, above and beyond fulfilling basic nutritional requirements. Several biological activities have now been associated with hen egg proteins, including novel antimicrobial activities, immunomodulatory, anti-cancer, and anti-hypertensive activities, highlighting the importance of hen egg proteins in human health, and disease prevention and treatment. Continued research to identify new and existing biological functions of hen egg proteins and their derivatives will help to define new methods to further improve the value of eggs, as a source of numerous biologically active compounds with specific benefits for human and animal health, and secure their role in the therapy and prevention of chronic and infectious disease.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".