Exploring the Potential of Antisense Technologies to Enhance Traditional Antifungal Treatments for Candida albicans Biofilms
Bibliographic record
Abstract
Biofilm-related infections present critical challenges in the context of medical implantable devices, often necessitating the removal of these life-saving devices. Among these infections, Candida albicans biofilms are particularly prevalent and are often addressed with Caspofungin, a fungicidal echinocandin. However, prevailing antifungal treatments exhibit limited efficacy against biofilms due to their intricate structure and the emergence of antifungal resistance. The recent advancement of antisense therapy offers a promising avenue to augment the effectiveness of anti-biofilm treatments. Targeting EFG1, a key transcription regulator in C. albicans, anti-EFG1 2′-OMethylRNA can increase membrane permeability while reducing antifungal properties. As such, there could be therapeutic benefits in leveraging anti-EFG1 2′-OMethylRNA to amplify the fungicidal properties of echinocandins. Our study aims to assess the viability of cells subjected to individual and combined treatment modalities. Employing an in vivo benchtop biofilm model, we aim to investigate the efficacy of Caspofungin and anti-EFG1 2′-OMethylRNA, both independently and in combination. By evaluating their impact on biofilm growth, cellular viability, and gene and protein expressions (transcriptome), we seek to ascertain and explore the potential synergistic effects of this combinatorial therapeutic approach. Investigating the combination of antisense and Caspofungin therapy presents a promising direction toward the more effective treatment of biofilm-related infections on medical implantables. Furthermore, the results of this study may present promising opportunities for the future use of antisense technology in more efficacious treatment and novel therapeutic approaches to other illnesses.
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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 | 0.001 |
| 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".