Antifungal Potency of Biosynthesized Silver Nanoparticles Derived from Marine Diatoms Against Multidrug‐Resistant <i>Candida auris</i> and <i>Pichia kudriavzevii</i>
Bibliographic record
Abstract
Abstract Candida auris and Pichia kudriavzevii are emerging multidrug‐resistant fungal pathogens that pose a significant threat to public health. The limited efficacy of conventional antifungals against these species has prompted the development of novel antifungal compounds. In recent years, silver nanoparticles (AgNPs) synthesized using marine diatoms have held promise as potent antifungal agents. In this study, three marine diatom species ( Chaetoceros spp., Skeletonema spp., and Thalassiosira spp.) were utilized for the biosynthesis of AgNPs (Ag‐DE/NPs). The biosynthesis was confirmed by a color change of the culture from colorless to brown and further validated by UV–vis spectroscopy, showing distinct surface plasmon resonance peaks at 425, 430, and 440 nm, respectively. Comprehensive characterization using FTIR, XRD, DLS, and SEM revealed the functionalized nature, crystalline structure, particle size, and surface morphology of the Ag‐DE/NPs. The antifungal efficacy of these AgNPs was evaluated against 20 clinical isolates and 2 reference strains of C. auris and P. kudriavzevii , which exhibited high resistance to fluconazole. AgNPs synthesized from Chaetoceros spp. displayed the lowest geometric mean minimum inhibitory concentrations (0.23 µg/mL for C. auris and 0.19 µg/mL for P. kudriavzevii ), showing a >250‐fold greater potency compared to fluconazole and comparable efficacy to amphotericin B. Growth curve analysis and sorbitol supplementation assays indicated that Ag‐DE/NPs disrupt fungal cell walls, while SEM imaging and ergosterol quantitation confirmed membrane damage and sterol depletion. These findings underscore the potential of Ag‐DE/NPs, particularly those synthesized from Chaetoceros spp., as promising candidates for combating drug‐resistant fungal infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".