Technology for the Production and Utilization of Food Protein-Derived Antihypertensive Peptides: A Review
Bibliographic record
Abstract
Angiotensin converting enzyme (ACE)-inhibitory drugs have been used as therapeutic tools in the clinical management of hypertension and associated cardiovascular disorders. Food-derived ACE-inhibitory peptides have lower potency than similar acting drugs but the peptides usually have no adverse side effects and there is virtually no risk of overdosing that is associated with drugs. This review summarizes several patents that have reported the development of technologies for the production of potent food protein-derived hydrolysates and peptides, which can be used to formulate antihypertensive functional foods and nutraceuticals. A common process to all the patents is the use of proteases to split large inactive proteins into smaller bioactive peptides. Ultrafiltration may be combined with liquid chromatography methods to separate the peptides according to size alone or a combination of size and charge density, respectively. Efficacy of the protein hydrolysates or peptide fractions is evaluated first in an in vitro system and may then be confirmed by measuring their hypotensive ability in an appropriate animal model such as the spontaneously hypertensive rats. Finally, protein hydrolysates or peptide fractions that have hypotensive ability may then be used to formulate foods, beverages or pills that can be taken as therapeutic tools against hypertension.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".