Assessing the Putative Anticryptococcal Properties of Crude and Clarified Extracts from Mollusks
Bibliographic record
Abstract
Cryptococcus neoformans is an encapsulated human fungal pathogen with a global distribution that primarily infects immunocompromised individuals. The widespread use of antifungals in clinical settings, their use in agriculture, and strain hybridization have led to increased evolution of resistance. This rising rate of resistance against antifungals is a growing concern among clinicians and scientists worldwide, and there is heightened urgency to develop novel antifungal therapies. For instance, C. neoformans produces several virulence factors, including intra- and extra-cellular enzymes (e.g., peptidases) with roles in tissue degradation, cellular regulation, and nutrient acquisition. The disruption of such peptidase activity by inhibitors perturbs fungal growth and proliferation, suggesting this may be an important strategy for combating the pathogen. Importantly, invertebrates such as mollusks produce peptidase inhibitors with biomedical applications and anti-microbial activity, but they are underexplored in terms of their usage against fungal pathogens. In this protocol, a global extraction from mollusks was performed to isolate potential peptidase inhibitors in crude and clarified extracts, and their effects against classical cryptococcal virulence factors were assessed. This method supports the prioritization of mollusks with antifungal properties and provides opportunities for the discovery of anti-virulence agents by harnessing the natural inhibitors found in mollusks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".