The Neglected Marine Fungi, Sensu stricto, and Their Isolation for Natural Products’ Discovery
Bibliographic record
Abstract
Despite the rapid development of molecular techniques relevant for natural product research, culture isolates remain the primary source from which natural products chemists discover and obtain new molecules from microbial sources. Techniques for obtaining and identifying microbial isolates (such as filamentous fungi) are thus of crucial importance for a successful natural products’ discovery program. This review is presented as a “best-practices guide” to the collection and isolation of marine fungi for natural products research. Many of these practices are proven techniques used by mycologists for the isolation of a broad diversity of fungi, while others, such as the construction of marine baiting stations and the collection and processing of sea foam using dilution to extinction plating techniques, are methodological adaptations for specialized use in marine/aquatic environments. To this day, marine fungi, Sensu stricto, remain one of the few underexplored resources of natural products. Cultivability is one of the main limitations hindering the discovery of natural products from marine fungi. Through encouraged collaboration with marine mycologists and the sharing of historically proven mycological practices for the isolation of marine fungi, our goal is to provide natural products chemists with the necessary tools to explore this resource in-depth and discover new and potentially novel natural products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".