Structural and Antihypertensive Properties of Enzymatic Hemp Seed Protein Hydrolysates
Bibliographic record
Abstract
The aim of this work was to produce antihypertensive protein hydrolysates through different forms of enzymatic hydrolysis (2% pepsin, 4% pepsin, 1% alcalase, 2% alcalase, 2% papain, and 2% pepsin + pancreatin) of hemp seed proteins (HSP). The hemp seed protein hydrolysates (HPHs) were tested for in vitro inhibitions of renin and angiotensin-converting enzyme (ACE), two of the enzymes that regulate human blood pressure. The HPHs were then administered orally (200 mg/kg body weight) to spontaneously hypertensive rats and systolic blood pressure (SBP)-lowering effects measured over a 24 h period. Size exclusion chromatography mainly showed a 300-9560 Da peptide size range for the HPHs, while amino acid composition data had the 2% pepsin HPH with the highest cysteine content. Fluorescence spectroscopy revealed higher fluorescence intensities for the peptides when compared to the unhydrolyzed hemp seed protein. Overall, the 1% alcalase HPH was the most effective (p < 0.05) SBP-reducing agent (-32.5 ± 0.7 mmHg after 4 h), while the pepsin HPHs produced longer-lasting effects (-23.0 ± 1.4 mmHg after 24 h). We conclude that an optimized combination of the fast-acting HPH (1% alcalase) with the longer-lasting HPHs (2% and 4% pepsin) could provide daily effective SBP reductions.
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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".