Engineered Niclosamide Loaded Nanofiber Platforms for Enhanced Antibacterial and Anticancer Efficacy
Why this work is in the frame
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Bibliographic record
Abstract
Niclosamide, a well-known anthelmintic drug used against parasitic infections, has gained attention for its potent anticancer and antibacterial activity. However, the use of niclosamide remains limited due to its highly hydrophobic nature and low systemic bioavailability. To address these limitations, we developed a biocompatible drug delivery system to combat the challenges of niclosamide usage. We fabricated niclosamide-loaded polycaprolactone (PCL) and poly(ethylene oxide) (PEO) composite nanofibers and investigated their anticancer and antibacterial applications. Here, PEO enhances drug diffusion and bioavailability, while PCL provides biodegradability and controlled drug release properties. Four different compositions of PEO–PCL nanofibers were synthesized and infused with niclosamide. The synthesized nanofibers were thoroughly characterized by using field emission scanning electron microscopy (FE-SEM), Fourier-transform infrared spectroscopy (FTIR), and energy-dispersive X-ray (EDX) analysis, confirming successful drug incorporation. To test the functionality of fabricated nanofibers, we examined the antibacterial properties using a minimum inhibitory concentration (MIC) and minimum killing concentration (MKC). Interestingly, these nanofibers have a significant activity against Staphylococcus aureus (Gram-positive) but not against Escherichia coli (Gram-negative), suggesting selective action. Furthermore, we assessed the anticancer potential of drug-loaded nanofibers in HeLa cells using a cell cytotoxicity assay. A marked decrease in cell viability was observed when the cells were treated with niclosamide-loaded nanofibers. Collectively, our findings demonstrate that niclosamide-loaded PEO–PCL nanofibers are a promising and efficient drug delivery system with potential applications in antimicrobial and anticancer therapies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it