Antiviral Effects and Mechanisms of Action of Water Extracts and Polysaccharides of Microalgae and Cyanobacteria
Bibliographic record
Abstract
Microalgae (MA) and cyanobacteria (CB) are currently attracting much attention from scientists due to the high biological activity of many secondary metabolites of these aquatic organisms. This review presents up-to-date modern data on the prospects for using polysaccharides (PS) of these marine aquatic organisms as effective and practically safe antiviral agents. These natural biopolymers are polyvalent compounds, which allows them to bind to several complementary biological target receptors. Particular emphasis is placed on the exopolysaccharides (EPS) Spirulina sp. (Arthrospira sp.), Porphyridium sp., Chlorella sp., and Euglena sp., whose antiviral activity makes them promising for the creation of drugs, biologically active food supplements, and products for functional nutrition. The mechanisms of the biological action of PS and the targets of these compounds are presented with a brief description of PS's anti-inflammatory, immunomodulatory and antioxidant actions, which make the most significant contribution to the antiviral effects. The authors hope to draw the attention of researchers to the use of water extracts and polysaccharides of microalgae and cyanobacteria as potential broad-spectrum antiviral agents that can become the basis for new antivirus strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".