Production of Bioactive Peptides from Microalgae and Their Biological Properties Related to Cardiovascular Disease
Bibliographic record
Abstract
Microalgae are a substantial group of unicellular prokaryotic and eukaryotic marine organisms. Due to their high protein content of 50–70%, microalgae have the potential to become a sustainable alternative protein source, as well as aiding in the development of bioactive peptide-based nutraceuticals. A series of major steps are involved in the production of peptides from microalgae, which include the disruption of the microalgal cell wall, the hydrolysis of proteins, and the extraction or isolation of peptides derived from hydrolysis. Physical methods of cell wall disruptions are favored due to the ability to obtain high-quality protein fractions for peptide production. Bioactive peptides are protein fragments of two to twenty amino acid residues that have a beneficial impact on the physiological functions or conditions of human health. Strong scientific evidence exists for the in vitro antioxidant, antihypertensive, and anti-atherosclerotic properties of microalgal peptides. This review is aimed at summarizing the methods of producing microalgal peptides, and their role and mechanisms in improving cardiovascular health. The review reveals that the validation of the physiological benefits of the microalgal peptides in relation to cardiovascular disease, using human clinical trials, is required.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".