Anti-inflammatory effects of egg yolk livetins (α, β, and γ-livetin) fraction and its enzymatic hydrolysates in lipopolysaccharide-induced RAW 264.7 macrophages
Bibliographic record
Abstract
In this study, the egg yolk livetins (α, β, and γ-livetin) fraction and its hydrolysates, prepared by pepsin and Alcalase, were evaluated for their anti-inflammatory effects using lipopolysaccharide (LPS)-induced RAW 264.7 macrophages as an in vitro model. Enzymatic hydrolysis by pepsin and Alcalase successfully transformed the large molecular weight livetins into low molecular mass peptides mostly below 10kDa. Results revealed that livetins and its hydrolysates (peptides) treatment significantly reduced the inflammatory responses as evidenced by inhibition of production of nitric oxide (NO) (22.7-39.2%), pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α) (36.9-43.2%), interleukin-1β (IL-1β) (26.1-50.9%) and interleukin-6 (IL-6) (60.4-69.0%), and the expression of inducible nitric oxide synthase (iNOS) (58.6-62%). Alcalase hydrolysate showed more effects in inhibiting prostaglandin-E2 (PGE2) production (30.3%) as well as expression of cyclooxygenase-2 (COX-2) (55.7%). In addition, effect of livetins and its hydrolysates on phagocytic capacity of the macrophages was also evaluated. The results indicate that livetins and its enzymatic hydrolysates significantly (p<0.001, 0.05) enhanced the phagocytic activity of the macrophages. The results suggest that egg yolk livetins and its hydrolysates with anti-inflammatory activity can potentially be used in health food/nutraceutical/pharmaceutical industry for various applications.
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".