Antimicrobial photodynamic therapy in onychomycosis management: A systematic review of clinical trials
Bibliographic record
Abstract
Objectives Onychomycosis is a prevalent fungal nail infection often resistant to conventional antifungal therapies. Antimicrobial photodynamic therapy (aPDT) has emerged as a promising alternative, though its efficacy remains under investigation. This systematic review aimed to assess the effectiveness of aPDT in managing onychomycosis. Methods Conducted according to PRISMA guidelines and registered in PROSPERO (CRD42024520247), this review included clinical trials evaluating the antimicrobial efficacy of aPDT for onychomycosis. Searches were performed in PubMed, Scopus, Web of Science, Embase, Cochrane Library, ClinicalTrials.gov, and gray literature. Two reviewers independently screened, selected, and extracted data on study characteristics. Risk of bias was assessed using the Cochrane tool and Newcastle-Ottawa scale; certainty of evidence was evaluated using GRADE. Results Eighteen studies involving 591 participants (mean age: 54.2 years) met inclusion criteria. The most common light source was diode laser (450–700 nm), and photosensitizers included methylene blue, aminolevulinic acid, and methyl-5-aminolevulinate. aPDT significantly reduced onychomycosis severity (30–90% OSI reduction) and achieved mycological cure rates up to 100% when combined with fractional CO 2 laser. Clinical cure rates ranged from 20% to 80%, with notable improvements in nail appearance. Histological and microbiological analyses confirmed fungal reduction, and patient satisfaction was generally high. Overall, studies showed low risk of bias. The certainty of evidence was moderate for randomized controlled trials and low for non-randomized trials. Conclusion aPDT demonstrates promising potential in onychomycosis management, showing clinical and microbiological efficacy. However, variability in protocols and outcomes requires further standardized clinical trials to establish optimal treatment parameters.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".