Combinatory Use of hLF(1-11), a Synthetic Peptide Derived from Human Lactoferrin, and Fluconazole/Amphotericin B against Malassezia furfur Reveals a Synergistic/Additive Antifungal Effect
Bibliographic record
Abstract
Objective: The increasing resistance of Malassezia yeasts against commonly used antifungal drugs dictates the need for novel antifungal compounds. Human lactoferrin-based peptides show a broad spectrum of antimicrobial activities. Various assays were performed to find the optimal growth conditions of the yeasts and to assess cell viability, using media with low lipid content to avoid peptide binding to medium components. Methods: In the current study, we tested the antimicrobial susceptibility of 30 strains of M. furfur that cover the known IGS1 genotypic variation. Results: hLF(1-11) inhibited the growth of all species tested, resulting in minimum inhibitory concentrations (MIC) values ranging from 12.5 to 100 μg/mL. In the combinatory tests, the majority of fractional inhibitory concentration indexes (FIC) for the tested strains of M. furfur were up to 1.0, showing that there is a synergistic or additive effect on the efficacy of the antifungal drugs when used in combination with hLF(1-11). Conclusion: Results showed that hLF(1-11) could be combined with fluconazole or amphotericin for the antimicrobial treatment of resistant strains, enhancing the potency of these antifungal drugs, resulting in an improved outcome for the patient.
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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) | 0.001 | 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) | 0.001 | 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".