Polysaccharides from echinoderms: unlocking health benefits and food applications – a review
Bibliographic record
Abstract
Echinoderms (phylum Echinodermata), including sea cucumbers, sea urchins, and starfish, are found in the marine environment. They have no freshwater or terrestrial representatives and inhabit the entire depth of the ocean. The phylum contains more than 7000 living species. Their bodies contain nutrients like proteins (peptides, collagen, and protein hydrolysates), lipids (polyunsaturated fatty acids), saponins (frondoside A), carotenoids (canthaxanthin and astaxanthin), phenolics (flavonoids and phenolic acids), vitamins, and minerals. Besides, these are the leading sources of unique polysaccharides, such as fucosylated chondroitin sulfate, sulfated fucans, and glycosaminoglycans, which possess a wide range of bioactivities. This review intends to explore the health-promoting properties of these polysaccharides, highlighting their anti-inflammatory, anticoagulant, antioxidant, antitumor, anticancer, and other effects along with their mechanisms of action. Their heterogeneous structural composition and remarkable biological activity make them promising candidates for many applications in the functional foods and nutraceuticals area. Furthermore, this review discusses the major challenges and future prospects of polysaccharides from marine echinoderms, focusing on their extraction, purification, characterization, and structural diversity. In addition, the potential of echinoderm polysaccharides as novel nutrients that can contribute to human health is described and it also highlights the growing desire for natural food products in health promotion and disease risk reduction.
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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 | 0.002 |
| 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".