Sea foam as a source of fungal inoculum for the isolation of biologically active natural products
Bibliographic record
Abstract
Due to a rate increase in the resistance of microbial pathogens to currently used antibiotics, there is a need in society for the discovery of novel antimicrobials. Historically, fungi are a proven source for antimicrobial compounds. The main goals of this study were to investigate the fungal diversity associated with sea foam collected around the coast of Prince Edward Island and the utility of this resource for the production of antimicrobial natural products. Obtained isolates were identified using ITS and nLSU rDNA sequences, fermented on four media, extracted and fractions enriched in secondary metabolites were screened for antimicrobial activity. The majority of the isolates obtained were ascomycetes, consisting of four recognized marine taxa along with other ubiquitous genera and many ‘unknown’ isolates that could not be identified to the species level using rDNA gene sequences. Secondary metabolite isolation efforts lead to the purification of the metabolites epolones A and B, pycnidione and coniothyrione from a strain of Neosetophoma samarorum; brefeldin A, leptosin J and the metabolite TMC-264 from an unknown fungus (probably representative of an Edenia sp.); and 1-hydroxy-6-methyl-8-hydroxymethylxanthone, chrysophanol and chrysophanol bianthrone from a Phaeospheria spartinae isolate. The biological activity of each of these metabolites was assessed against a panel of microbial pathogens as well as several cell lines.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".