Search for Hydrophilic Marine Fungal Metabolites: A Rational Approach for Their Production and Extraction in a Bioactivity Screening Context
Bibliographic record
Abstract
In the search for bioactive natural products, our lab screens hydrophobic extracts from marine fungal strains. While hydrophilic active substances were recently identified from marine macro-organisms, there was a lack of reported metabolites in the marine fungi area. As such, we decided to develop a general procedure for screening of hydrophobic metabolites. The aim of this study was to compare different processes of fermentation and extraction, using six representative marine fungal strains, in order to define the optimized method for production. The parameters studied were (a) which polar solvent to select, (b) which fermentation method to choose between solid and liquid cultures, (c) which raw material, the mycelium or its medium, to extract and (d) which extraction process to apply. The biochemical analysis and biological evaluations of obtained extracts led to the conclusion that the culture of marine fungi by agar surface fermentation followed by the separate extraction of the mycelium and its medium by a cryo-crushing and an enzymatic digestion with agarase, respectively, was the best procedure when screening for hydrophilic bioactive metabolites. During this development, several bioactivities were detected, confirming the potential of hydrophilic crude extracts in the search for bioactive 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 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".