Anti-Inflammatory and Antioxidant Properties of Peptides Released from β-Lactoglobulin by High Hydrostatic Pressure-Assisted Enzymatic Hydrolysis
Bibliographic record
Abstract
BACKGROUND: β-lactoglobulin hydrolysates (BLGH) have shown antioxidant, antihypertensive, antimicrobial, and opioid activity. In the current study, an innovative combination of high hydrostatic pressure and enzymatic hydrolysis (HHP-EH) was used to increase the yield of short bioactive peptides, and evaluate the anti-inflammatory and antioxidant properties of the BLGH produced by the HHP-EH process. METHOD: BLG was enzymatically hydrolyzed by different proteases at an enzyme-to-substrate ratio of 1:100 under HHP (100 MPa) and compared with hydrolysates obtained under atmospheric pressure (AP-EH at 0.1 MPa). The degree of hydrolysis (DH), molecular weight distribution, and the antioxidant and anti-inflammatory properties of hydrolysates in chemical and cellular models were evaluated. RESULTS: BLGH obtained under HHP-EH showed higher DH than the hydrolysates obtained under AP-EH. Free radical scavenging and the reducing capacity were also significantly stronger in HHP-BLGH compared to AP-BLGH. The BLGH produced by alcalase (Alc) (BLG-Alc) showed significantly higher antioxidant properties among the six enzymes examined in this study. The anti-inflammatory properties of BLG-HHP-Alc were observed in lipopolysaccharide-stimulated macrophage cells by a lower level of nitric oxide production and the suppression of the synthesis of pro-inflammatory cytokines. Peptide sequencing revealed that 38% of the amino acids in BLG-HHP-Alc are hydrophobic and aromatic residues, which contribute to its anti-inflammatory properties. CONCLUSIONS: Enzymatic hydrolysis of BLG under HHP produces a higher yield of short bioactive peptides with potential antioxidant and anti-inflammatory effects.
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) | 0.001 | 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".