Chlorophyllin-based Photodynamic Inactivation against Candidozyma auris planktonic cells and dynamic biofilm
Bibliographic record
Abstract
Abstract Candidozyma auris likely gained thermotolerance as a result of climate change and emerged from its origin in Indian wetlands to a severe healthcare threat because of its resistance to common antifungals and antiseptics in only a few decades. This development identifies the yeast as a perfect example for the relevance of the One Health concept for human well-being. Here, we compare the effectiveness of Photodynamic Inactivation (PDI) based on the economic and ecofriendly natural photosensitizer sodium–magnesium–chlorophyllin (Chl) to that of a synthetic chlorin e6 derivative carrying cationic moieties, B17-0024, suggesting both as alternative to common disinfectants against C. auris. Experiments were conducted on planktonic cells and—for the very first time—on dynamic biofilms using the CDC bioreactor. Treatment of planktonic cells with 50 µM Chl and blue light (395 nm, 7.5 J cm −2 , 15 min drug to light interval) achieved a 7 log 10 step reduction of viable C. auris . B17-0024 induced a more than 5 log 10 step photokilling at 10 µM. Illumination of the same concentrations with red light (600–700 nm, 30 J cm −2 ) resulted in a relative inactivation of 7 log 10 steps for Chl and 6 log 10 steps for B17-0024. Dynamic biofilm samples were illuminated with 3.33-times higher radiant exposure (25 J cm −2 at 395 nm or 100 J cm −2 at 600–700 nm). The antimicrobial effect of a 99.9% reduction of C. auris was exceeded with 10 µM B17-0024 and blue light illumination and with 50 µM Chl and B17-0024 activated by red light. Biofilms were completely eradicated when doubling the photosensitizer concentrations. Our results demonstrate that PDI based on Chl represents a rapid and effective tool to eliminate emerging pathogens even if resistant to conventional treatment. Due to its low costs and eco-friendliness PDI based on Chl may be applicable for disinfection of larger areas in hospitals. Graphical abstract
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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".