Prospects of brown seaweed polysaccharides (BSP) as prebiotics and potential immunomodulators
Bibliographic record
Abstract
Prebiotics enhance immune response through the modulation of intestinal microbial activities, production of short chain fatty acids (SCFA), direct interaction with toll-like receptors and mucin production. These non-digestible food components are known to be resistant to enzymatic hydrolysis by digestive enzymes and are utilized as carbon source for the growth of beneficial bacteria population through the process of fermentation. Brown seaweed polysaccharides (BSP) have been described as emerging prebiotics due to their potential to stimulate gut microbiota activities at in vitro and in vivo stages. This review therefore examines evidence of the relationship between the prebiotic capacity of BSP, their structure, extraction, and possible mechanisms of immunomodulation. Practical applications Bio-functional ingredients have been widely explored for numerous health benefits. Of interest to this review are polysaccharides of brown seaweed which have great prebiotic prospects. Prebiotics are important bio-functional ingredients having the potential to improve immune health. An understanding of the prebiotic and immunomodulatory potential of BSP provides the food industry a novel alternative source of prebiotics. Excerpts from this review will provide background knowledge and advance scientific research into prospects of BSP as prebiotics and a possible commercialization of BSP products.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".