Sulfated Polysaccharides from Seaweeds: A Promising Strategy for Combatting Viral Diseases—A Review
Bibliographic record
Abstract
The limited availability of treatments for many infectious diseases highlights the need for new treatments, particularly for viral infections. Natural compounds from seaweed are attracting increasing attention for the treatment of various viral diseases, and thousands of novel compounds have been isolated for the development of pharmaceutical products. Seaweed is a rich source of natural bioactive compounds, including polysaccharides. The discovery of algal polysaccharides with antiviral activity has significantly increased in the past few decades. Furthermore, unique polysaccharides isolated from seaweeds, such as carrageenan, alginates, fucoidans, galactans, laminarians, and ulvans, have been shown to act against viral infections. The antiviral mechanisms of these agents are based on their inhibition of DNA or RNA synthesis, viral entry, and viral replication. In this article, we review and provide an inclusive description of the antiviral activities of algal polysaccharides. Additionally, we discuss the challenges and opportunities for developing polysaccharide-based antiviral therapies, including issues related to drug delivery and formulation. Finally, this review highlights the need for further research for fully understanding the potential of seaweed polysaccharides as a source of antiviral agents and for developing effective treatments for viral diseases.
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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 | 0.001 |
| Research integrity | 0.001 | 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".