Antiviral discovery in toxic cyanobacteria: Low hanging fruit in the age of pandemics
Bibliographic record
Abstract
The power of novel vaccination technologies and their rapid development were elucidated clearly during the COVID-19 pandemic. At the same time, it also became clear that there is an urgent need to discover and manufacture new antivirals that target emerging viral threats. Toxic species of cyanobacteria produce a range of bioactive compounds that makes them good candidates for drug discovery. Nevertheless, few studies demonstrate the antiviral potential of cyanobacteria. This is partly due to the lack of specific and simple protocols designed for the rapid detection of antiviral activity in cyanobacteria and partly because specialized facilities for work with pathogenic viruses are few and far between. We therefore developed an easy method for the screening of cyanobacterial cultures for antiviral activity and used our private culture collection of non-pathogenic virus isolates to show that antiviral activity is a prominent feature in the cyanobacterium Microcystis aeruginosa. In this proof-of-concept study, we show that M. aeruginosa extracts from three different cyanobacterial strains delay infection of diatom-infecting single-stranded DNA and single-stranded RNA viruses by up to 2 days. Our work shows the ease with which cyanobacteria from culture collections can be screened for antiviral activity and highlights the potential of cyanobacteria as an excellent source for the discovery of novel antiviral compounds, warranting further investigation.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".