Optimisation of Levofloxacin-loaded electrospun fibres for diabetic wound treatment
Bibliographic record
Abstract
Foot ulcers are a common and serious diabetes complication, significantly affecting patients' quality of life. Chronic diabetic wounds are difficult to treat and often resist conventional therapies. Recently, materials developed through emerging techniques have gained attention for use in wound care and tissue regeneration. This study explores the development of nanofibres through electrospinning to create therapeutic patches for diabetic foot ulcers. Electrospinning allows control over fibres composition, orientation, and diameter, offering advantages such as simplicity and adaptability. Key process parameters, including flow rate, applied voltage, and polymer concentration, were optimized to produce defect-free fibres. The antibiotic levofloxacin was encapsulated in the fibres to assess its controlled release profile. Additionally, blends of chitosan and polycaprolactone (PCL) in various solvent systems were studied to enhance fibre characteristics. The combination leverages the elasticity, mechanical strength, biocompatibility, and versatility of PCL. The resulting composite fibres had an optimal diameter of 400 nm. Drug release analysis showed an initial peak-crucial for antibacterial efficacy-followed by a slower, sustained release phase. This biphasic release is beneficial in preventing infection while supporting prolonged therapeutic action. The findings demonstrate that a carefully designed formulation strategy can optimize fibres performance, making electrospun nanofibers patch a promising tool in the treatment of diabetic foot ulcers.
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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".