Powering up antifungal treatment: using small molecules to unlock the potential of existing therapies
Bibliographic record
Abstract
ABSTRACT Fungal pathogens are increasingly appreciated as a significant infectious disease challenge. Compared to bacteria, fungal cells are more closely related to human cells, and few classes of antifungal drugs are available. Combination therapy offers a potential solution to reduce the likelihood of resistance acquisition and extend the lifespan of existing antifungals. There has been recent interest in combining first-line drugs with small-molecule adjuvants. In a recent article, Alabi et al. identified 1,4-benzodiazepines as promising molecules to enhance azole activity in pathogenic Candida spp. (P. E. Alabi, C. Gautier, T. P. Murphy, X. Gu, M. Lepas, V. Aimanianda, J. K. Sello, I. V. Ene, 2023, mBio https://doi.org/10.1128/mbio.00479-23 ). These molecules have no antifungal activity on their own but exhibited significant potentiation of fluconazole in azole-susceptible and -resistant isolates. Additionally, the 1,4-benzodiazepines increased the fungicidal activity of azoles that are typically fungistatic to Candida spp., inhibited filamentation (a virulence-associated trait), and accordingly increased host survival in Galleria mellonella . This research thus provides another encouraging step on the critical pathway toward reducing mortality due to antimicrobial resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".