Differentiated extracts from freshwater and terrestrial mollusks inhibit virulence factor production in Cryptococcus neoformans
Bibliographic record
Abstract
Abstract The human fungal pathogen, Cryptococcus neoformans, is responsible for deadly infections among immunocompromised individuals with the evolution of antifungal resistance driving the need for discovery of new compounds that inhibit fungal virulence factors, rather than kill the pathogen. Recently, exploration into natural sources (e.g., plants, invertebrates, microbes) of antifungal agents has garnered attention by integrating a One Health approach for new compound discovery. Here, we explore extracts from three mollusk species (freshwater and terrestrial) and evaluate effects against the growth and virulence factor production (i.e., thermotolerance, melanin, capsule, and biofilm) in C. neoformans. We demonstrate that clarified extracts of Planorbella pilsbryi have a fungicidal effect on cryptococcal cells in a comparable way to fluconazole. Similarly, all extracts of Cipangopaludina chinensis not only affect cryptococcal thermotolerance but also impair biofilm and capsule production with clarified extracts of Cepaea nemoralis also conveying the latter effect. Next, inhibitory activity of extracts against peptidases related to these virulence factors combined with quantitative proteomics defined distinct proteome signatures and proposed proteins driving the observed anti-virulence properties. Overall, this work highlights the potential of compounds derived from natural sources to inhibit virulence factor production in a clinically important fungal pathogen.
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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) | 0.001 | 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".