The Anti-Viral Applications of Marine Resources for COVID-19 Treatment: An Overview
Bibliographic record
Abstract
The ongoing pandemic has led to an urgent need for novel drug discovery and potential therapeutics for Sars-CoV-2 infected patients. Although Remdesivir and the anti-inflammatory agent dexamethasone are currently on the market for treatment, Remdesivir lacks full efficacy and thus, more drugs are needed. This review was conducted through literature search of PubMed, MDPI, Google Scholar and Scopus. Upon review of existing literature, it is evident that marine organisms harbor numerous active metabolites with anti-viral properties that serve as potential leads for COVID-19 therapy. Inorganic polyphosphates (polyP) naturally found in marine bacteria and sponges have been shown to prevent viral entry, induce the innate immune response, and downregulate human ACE-2. Furthermore, several marine metabolites isolated from diverse sponges and algae have been shown to inhibit main protease (Mpro), a crucial protein required for the viral life cycle. Sulfated polysaccharides have also been shown to have potent anti-viral effects due to their anionic properties and high molecular weight. Likewise, select marine sponges produce bromotyrosines which have been shown to prevent viral entry, replication and protein synthesis. The numerous compounds isolated from marine resources demonstrate significant potential against COVID-19. The present review for the first time highlights marine bioactive compounds, their sources, and their anti-viral mechanisms of action, with a focus on potential COVID-19 treatment.
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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 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".