Anti-Inflammatory and Antioxidant Properties of Casein Hydrolysate Produced Using High Hydrostatic Pressure Combined with Proteolytic Enzymes
Bibliographic record
Abstract
Casein-derived peptides are shown to possess radical scavenging and metal chelating properties. The objective of this study was to evaluate novel anti-inflammatory properties of casein hydrolysates (CH) produced by an eco-friendly process that combines high hydrostatic pressure with enzymatic hydrolysis (HHP-EH). Casein was hydrolysed by different proteases, including flavourzyme (Fla), savinase (Sav), thermolysin (Ther), trypsin (Try), and elastase (Ela) at 0.1, 50, 100, and 200 MPa pressure levels under various enzyme-to-substrate ratios and incubation times. Casein hydrolysates were evaluated for the degree of hydrolysis (DH), molecular weight distribution patterns, and anti-inflammatory properties in chemical and cellular models. Hydrolysates produced using HHP-EH exhibited higher DH values and proportions of smaller peptides compared to atmospheric pressure-enzymatic hydrolysis (AP-EH). Among five enzymes, Fla-digested HHP-EH-CH (HHP-Fla-CH) showed significantly higher antioxidant properties than AP-Fla-CH. The anti-inflammatory properties of HHP-Fla-CH were also observed by significantly reduced nitric oxide and by the suppression of the synthesis of pro-inflammatory cytokines in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophage cells. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) revealed that 59% of the amino acids of the peptides in HHP-Fla-CH were composed of proline, valine, and leucine, indicating the potential anti-inflammatory properties. In conclusion, the HHP-EH method provides a promising technology to produce bioactive peptides from casein in an eco-friendly process.
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".