Alginate Oligosaccharides: Production, Biological Activities, and Potential Applications
Bibliographic record
Abstract
Alginate, a group of polyuronic saccharides, has been widely used in both pharmaceutical and food industries due to its unique physicochemical properties as well as beneficial health effects. However, the potential applications of alginate are restricted because of its low water solubility and high solution viscosity when significant concentrations are needed, particularly in food products. Alginate oligosaccharides (AOS), oligomers containing 2 to 25 monomers, can be obtained via hydrolysis of glycosidic bonds, organic synthesis, or through biosynthesis. Generally, AOS have shorter chain lengths and thus improved water solubility when compared with higher molecular weight alginates of the same monomers. These oligosaccharides have attracted interest from both basic and applied researchers. AOS have unique bioactivity and can impart health benefits. They have shown immunomodulatory, antimicrobial, antioxidant, prebiotic, antihypertensive, antidiabetic, antitumor, anticoagulant, and other activities. As examples, they have been utilized as prebiotics, feed supplements for aquaculture, poultry, and swine, elicitors for plants and microorganisms, cryoprotectors for frozen foods, and postharvest treatments. This review comprehensively covers methods for AOS production from alginate, such as physical/chemical methods, enzymatic methods, fermentation, organic synthesis, and biosynthesis. Moreover, current progress in structural characterization, potential health benefits, and AOS metabolism after ingestion are summarized in this review. This review will discuss methods for producing and modified AOS with desirable structures that are suited for novel 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 | 0.001 |
| 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) | 0.001 | 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".