High Hydrostatic Pressure-Assisted Enzymatic Treatment Improves Antioxidant and Anti-inflammatory Properties of Phosvitin
Bibliographic record
Abstract
BACKGROUND: Phosvitin (PV) is a highly-phosphorylated metal-binding protein in egg yolk. Phosphoserine clusters make PV resistant to enzymatic digestion, which might be nutritionally undesirable. OBJECTIVE: This study was designed to determine the effects of high hydrostatic pressure and enzymatic hydrolysis (HHP-EH) on the antioxidant and anti-inflammatory properties of PV hydrolysates (PVHs). METHODS: PV was hydrolyzed by alcalase, elastase, savinase, thermolysin, and trypsin at 0.1, 50, and 100 MPa pressure levels. PVHs were evaluated for degree of hydrolysis, molecular weight distribution patterns, antioxidant and anti-inflammatory properties in chemical and cellular models. The effect of PVH on gene expression of pro-inflammatory cytokines (TNF-α and IL-1β) was also evaluated using real time-PCR. The hydrolysate with most potent antioxidant and anti-inflammatory properties was subjected to LC-MS/MS analysis to identify the peptide sequence. RESULTS: Hydrolysates produced at 100 MPa exhibited higher degree of hydrolysis and greater reducing power and free radical scavenging activity compared to those obtained at atmospheric pressure. After adjusting the phosphate content, alcalase- and trypsin-digested PVHs showed superior iron chelation capacity (69-73%), regardless of pressure. Both alcalase- and trypsin-digested PVHs significantly inhibited nitric oxide production by RAW264.7 macrophage cells. LPS-stimulated up-regulation of proinflammatory cytokines was also suppressed by alcalase-digested PVH. CONCLUSION: The HHP-EH method could play a promising role in the production of bioactive peptides from hydrolysis-resistant proteins. HHP-assisted PVH may be useful in preparing a potential pharmaceutical with antioxidant and anti-inflammatory properties.
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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) | 0.001 | 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".