Harnessing marine microorganisms in the battle against the influenza virus
Bibliographic record
Abstract
The Orthomyxoviridae family of RNA viruses is responsible for influenza. Both influenza A and B strains induce seasonal influenza illnesses, whereas influenza C typically only results in minor respiratory sickness. Coughing and sneezing can spread the disease, and direct touch can transmit the virus. Although a few authorized anti-influenza drugs accessible such as oseltamivir, amantadine, and rimantadine, are available, due to the expanding drug resistance, they are becoming less efficient and effective. Microorganisms living in the marine environment can produce unique chemical compounds with potent biological activity, including antiviral. Natural antiviral substances can be used against various viruses pathogenic to humans, including influenza. This review aimed to search for potential anti-influenza properties of different substances derived from marine microorganisms. The literature review was conducted using the PubMed scientific database. The authors propose a great variety of substances that could be potentially helpful in the fight against influenza. Starting from abyssomycins, polysaccharides, and spirostaphylotrichin, through violapyrones, polyketides indole diterpenoids, finishing with microalgae and cyanobacteria extracts and others. Some of them directly target the viral adsorption and internalization processes, inhibit viral polymerase activity, or stimulate the immune system of the host. In the future, potential drugs that could be used to improve the treatment of influenza are believed to be obtained from marine sources, which could be used for the creation of innovative pharmaceuticals. The authors of the studies strongly advise additional in vitro and in vivo research on substances with potential antiviral properties.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".