One‑Step fabrication method of insulin‑loaded polycaprolactone nanofibres by blend electrospinning for diabetic wound care
Bibliographic record
Abstract
Effective management of diabetic wounds is a significant clinical challenge. Topical insulin shows therapeutic promise, but its delivery and stability require the use of advanced systems. This study aimed to develop and comprehensively characterise insulin-loaded Poly(ε-caprolactone) (PCL) nanofibres fabricated via a one-step blend electrospinning technique, as a potential platform for sustained insulin delivery in diabetic wound care. PCL nanofibres containing varying insulin concentrations were prepared using hexafluoroisopropanol (HFIP). The nanofibres were extensively characterised for their morphology, physicochemical properties (including thermal and chemical integrity), mechanical strength, surface wettability, insulin encapsulation efficiency (EE), and in vitro release kinetics. The stability of free insulin in solution was also assessed for comparison. Insulin incorporation significantly reduced nanofibre diameter (to ∼ 250 nm) and markedly enhanced tensile strength and Young's modulus without compromising elasticity, yielding mechanical properties within reported physiological ranges. An EE of approximately 78 % was achieved. In vitro studies demonstrated sustained insulin release over 14 days. Crucially, comparative analyses of release samples, contextualised by free insulin degradation studies, revealed that PCL nanofibre encapsulation conferred significant protection against insulin degradation compared to insulin in solution. The developed insulin-loaded PCL nanofibres, combining favourable physicochemical and mechanical properties with sustained release and enhanced protein stability, represent a promising approach for advanced diabetic wound dressings, potentially reducing dressing change frequency and improving therapeutic outcomes.
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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".