Membrane-targeting antimicrobial compounds have differential effects on living and artificial yeast membrane models
Bibliographic record
Abstract
The stability of the plasma membrane is crucial for cell viability and disruptions in membrane stability can significantly impact cell function. Antimicrobial compounds targeting fungal membranes are required as novel alternatives to current resistance-prone fungicides. Six antimicrobials were assessed using the yeast Saccharomyces cerevisiae in living and artificial membrane models to gain insight into their efficacy and mechanistic activity. Antimicrobial-treated yeast cultures were monitored for growth inhibition and cell membrane permeability. Liposomes prepared from yeast polar lipids were used to examine the impact of the antimicrobials on size, polydispersity, and ζ-potential. Iturin and nystatin were the most effective compounds in reducing growth and increasing membrane permeability. ζ-Potential measurements indicated that iturin caused reduced stability, whereas there were no changes in stability with nystatin. Daptomycin and fengycin did not affect growth or permeability, but reduced stability. Nisin inhibited growth but did not affect stability. Surfactin was the only tested compound to increase stability. Results indicate that antimicrobials known to target biomembranes had variable effects, with lipid membrane components playing a role in antifungal outcome and mechanistic activity.
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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) | 0.000 | 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 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".