An in vitro and ex vivo Photodynamic Therapy Study of Methylene Blue and Natural Extracts in Association with Nail Penetration Carrier Against Trichophyton rubrum Infections
Bibliographic record
Abstract
Human fungal superficial infections are mainly caused by dermatophytes. These infections are distributed worldwide, common for people of all ages, in both sexes. The current treatments include taking oral antifungal drugs and topical therapy. However, treatments of superficial infections can be challenging in children and elderly mainly due to compliance issues and associated potential health risks and side-effects. Photo dynamic therapy (PDT) is a novel approach to treat fungal superficial infections. In this approach, light is used to excite a photosensitizer to turn readily available oxygen into reactive oxygen species (ROS) to kill the pathogen. In this research, we have used the pathogenic dermatophyte Trichophyton rubrum as a model to screen for photosensitizers and identify the best combinations of photosensitizer X carrier X light exposure time against T. rubrum. I obtained the In vitro photosensitizers’ Minimum Inhibitory Concentration (MIC), Minimum Fungicidal Concentration (MFC), carrier inhibitory and fungicidal combinations experimental results. In addition, ex vivo experimental results for photosensitizer and carrier systematic treatments are presented with both nail pieces and nail well apparatus. The in vitro results confirm the fungicidal ability of photosensitizer Methylene Blue and natural extracts Inula, Propolis and St. John’s Wort to T. rubrum. For ex vivo experiments, among the three natural extracts, only Inula showed promising fungicidal effect on nail pieces. Methylene Blue and carrier, Methylene Blue plus Inula and carrier combinations at certain concentrations all showed strong nail penetration ability and fungicidal effect against T. rubrum infection. These results suggest promising avenues for further clinical research and application of PDT.
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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 | 0.001 |
| 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".