Activity of novel non-amphipathic cationic antimicrobial peptides against Candida species
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Candida species are problematic opportunistic pathogens in the hospital setting, where they are frequently associated with opportunistic infections of indwelling medical devices. There are only a few effective classes of antifungal agents currently available, and some species, such as Candida lusitaniae, Candida glabrata and Candida krusei, are intrinsically resistant to some of these drugs, further reducing existing therapeutic options. We have recently developed synthetic, non-amphipathic cationic antimicrobial peptides (CAPs) based on the structure of native hydrophobic membrane-spanning domains of integral membrane proteins. In this article, we report on the activity of these CAPs and new variants thereof against eight Candida species. METHODS AND RESULTS: Using a combination of MIC, haemolysis, time-kill and biofilm killing assays, we demonstrate activity of CAPs in the micromolar range against eight Candida species, with little toxicity to mammalian cells. The synthetic peptides killed both the fluconazole-susceptible and fluconazole-resistant strains of Candida albicans, Candida tropicalis and C. glabrata by 4 logs or more within 3 h, and also killed pre-formed yeast biofilms on plastic surfaces. CONCLUSIONS: These peptides show promise as a basis for development of novel, broad-spectrum antimicrobial agents.
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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".