Plant‐derived bioactive peptides: A comprehensive review
Bibliographic record
Abstract
Abstract Bioactive peptides that are derived through the enzymatic hydrolysis of proteins have garnered significant attention for their diverse health‐promoting properties. This article delves into the technology behind plant‐derived bioactive peptide production by emphasizing the principles of enzyme hydrolysis. Furthermore, the review highlights the refinement techniques, employing membrane ultrafiltration and various forms of column chromatography to isolate and purify these peptides effectively. The bioactive properties of protein hydrolysates encompass a range of health benefits, including antihypertensive, antioxidant, anti‐diabetic, anti‐obesity, and neuroprotective effects. Additionally, bioactive peptides demonstrate promise in inhibiting cancer cell growth, supporting bone health, and modulating immune responses. These properties hold immense potential for revolutionizing approaches to various health conditions. Continued research in this field promises further insights and breakthroughs, propelling bioactive peptides to the forefront of nutrition and healthcare innovation. The convergence of advanced technology and the multifaceted bioactivity of peptides present an exciting avenue for improving human well‐being and addressing a wide array of health challenges.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".