Potent Antifungal Activity of Penta-<i>O</i>-galloyl-β-<scp>d</scp>-Glucose against Drug-Resistant <i>Candida albicans</i>, <i>Candida auris</i>, and Other Non-<i>albicans Candida</i> Species
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Among fungal pathogens, infections by drug-resistant Candida species continue to pose a major challenge to healthcare. This study aimed to evaluate the activity of the bioactive natural product, penta- O -galloyl-β- d -glucose (PGG) against multidrug-resistant (MDR) Candida albicans, MDR Candida auris, and other MDR non- albicans Candida species. Here, we show that PGG has a minimum inhibitory concentration (MIC) of 0.25–8 μg mL –1 (0.265–8.5 μM) against three clinical strains of C. auris and a MIC of 0.25–4 μg mL –1 (0.265–4.25 μM) against a panel of other MDR Candida species. Our cytotoxicity studies found that PGG was well tolerated by human kidney, liver, and epithelial cells with an IC 50 > 256 μg mL –1 (>272 μM). We also show that PGG is a high-capacity iron chelator and that deletion of key iron homeostasis genes in C. albicans rendered strains hypersensitive to PGG. In conclusion, PGG displayed potent anti- Candida activity with minimal cytotoxicity for human cells. We also found that the antifungal activity of PGG is mediated through an iron-chelating mechanism, suggesting that the compound could prove useful as a topical treatment for superficial Candida infections.
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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".