Antioxidant Potential of Sea Cucumbers and Their Beneficial Effects on Human Health
Bibliographic record
Abstract
Sea cucumbers are considered a luxury food item and used locally in traditional medication due to their impressive nutritional profile and curative effects. Sea cucumbers contain a wide range of bioactive compounds, namely phenolics, polysaccharides, proteins (collagen and peptides), carotenoids, and saponins, demonstrating strong antioxidant and other activities. In particular, phenolic compounds, mainly phenolic acids and flavonoids, are abundant in this marine invertebrate and exhibit antioxidant activity. Protein hydrolysates and peptides obtained from sea cucumbers exhibit antioxidant potential, mainly dependent on the amino acid compositions and sequences as well as molecular weight, displayed for those of ≤20 kDa. Moreover, the antioxidant activity of sea cucumber polysaccharides, including fucosylated chondroitin sulfate and fucan, is a combination of numerous factors and is mostly associated with molecular weight, degree of sulfation, and type of major sugars. However, the activity of these bioactive compounds typically depends on the sea cucumber species, harvesting location, food habit, body part, and processing methods employed. This review summarizes the antioxidant activity of bioactive compounds obtained from sea cucumbers and their by-products for the first time. The mechanism of actions, chemical structures, and factors affecting the antioxidant activity are also discussed, along with the associated health benefits.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".