In Vitro Antioxidant Properties of Hemp Seed (<i>Cannabis sativa</i> L.) Protein Hydrolysate Fractions
Bibliographic record
Abstract
Abstract Simulated gastrointestinal hydrolysis of hemp seed proteins using pepsin and pancreatin followed by membrane ultrafiltration fractionation yielded fractions with peptide sizes of <1, 1–3, 3–5, and 5–10 kDa. Analysis of in vitro antioxidant properties showed that the hemp seed protein hydrolysate (HPH) exhibited a significantly weaker ( p < 0.05) scavenging of 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) radicals when compared to the fractionated peptides. Metal chelation activity of the HPH was significantly greater ( p < 0.05) than the activities of fractionated peptides. Fractionation of the HPH led to significant ( p < 0.05) improvements in ferric reducing power, DPPH, and hydroxyl radical scavenging radical activities but decreased metal chelation capacity. Peptide fractions with longer chain lengths (3–5 and 5–10 kDa) had better metal chelation and ferric reducing power than the <1, and 1–3 kDa fractions. HPH and all the peptide fractions significantly inhibited ( p < 0.05) linoleic acid oxidation when compared to the control. Glutathione (GSH) had significantly greater ( p < 0.05) ferric reducing power, and scavenging of hydroxyl and DPPH radicals when compared to HPH and fractionated peptides. In contrast, HPH and peptide fractions >3 kDa had significantly higher ( p < 0.05) metal chelation activity than GSH. The results show the potential use of HPH and peptide fractions of defined size for the treatment of oxidative stress‐related diseases.
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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) | 0.001 | 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) | 0.001 | 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".