Novel marine bioactives: application in functional foods, nutraceuticals, and pharmaceuticals
Bibliographic record
Abstract
Functional food, nutraceutical, and pharmaceutical applications of natural products have gained growing attention as there is increasingly awareness of the association between bioactive compounds and improved health. Recently, food and biomedical scientists have focused more on marine resources to isolate bioactives since the marine ecosystem comprises unexploited resources with a wide range of organisms. Marine species produce a wide range of natural products, such as polysaccharides, peptides, polyunsaturated lipids, phenolic compounds, and pigments, with unique structures and diverse biological activities due to their extreme living environments. These active molecules reduce the risk of chronic disease and improve health by exhibiting antioxidant, anticancer, anti-inflammatory, antimicrobial, antidiabetic, anti-obesity, antihypertensive, cardioprotective, and neuroprotective activities. This review summarizes the recent discoveries of bioactive compounds from marine invertebrates (sponges, cnidarians, echinoderms, molluscs, ascidians, and crustaceans), fishes, seaweeds, and marine microorganisms and their potential for functional foods, nutraceuticals, and pharmaceuticals applications.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".