Electrospun chitosan nanofibers for regenerative wound healing: from molecular design to functional scaffolds
Bibliographic record
Abstract
The management of acute and chronic wounds remains a clinical challenge due to infection, delayed re-epithelialization, and impaired angiogenesis. Electrospun nanofibrous scaffolds have emerged as promising biomaterials, offering high surface area-to-volume ratios, tunable porosity, and ECM-like architectures. Chitosan, derived from chitin, is a biocompatible, biodegradable, and antimicrobial natural polymer ideally suited for wound healing. Electrospun chitosan nanofibres support cellular proliferation, modulate inflammation, and promote tissue regeneration. This review examines recent advances in the fabrication and biomedical applications of electrospun chitosan-based nanofibres for wound healing. Key electrospinning parameters, such as polymer concentration, molecular weight, solution viscosity, and applied voltage, are discussed. Various electrospinning strategies, including blend, coaxial, emulsion, and multilayer methods, are explored for encapsulating therapeutic agents, controlling drug release, and enhancing scaffold performance. The influence of polymer blends, crosslinking methods, and solvent systems on nanofibre morphology and mechanical integrity is also examined. Significantly, this work bridges materials design with clinical functionality, offering a roadmap for translating molecular-level chitosan modifications and nanostructure control into precision medicine. Beyond wound healing, the fabrication strategies and design principles discussed herein hold broad relevance for the fields of materials science and biomedical engineering, particularly in developing next-generation bioresponsive materials, tissue scaffolds, and drug delivery systems. As the field evolves, electrospun chitosan nanofibres are poised to play a pivotal role in advancing smart, adaptive, and regenerative biomaterials for diverse therapeutic applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".