Anti-Inflammatory and Antioxidant Properties of Peptides Released from β-Lactoglobulin by High Hydrostatic Pressure-Assisted Enzymatic Hydrolysis
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it