Bioactive Agent-Loaded Electrospun Nanofiber Membranes for Accelerating Healing Process: A Review
Bibliographic record
Abstract
Despite the advances that have been achieved in developing wound dressings to date, wound healing still remains a challenge in the healthcare system. None of the wound dressings currently used clinically can mimic all the properties of normal and healthy skin. Electrospinning has gained remarkable attention in wound healing applications because of its excellent ability to form nanostructures similar to natural extracellular matrix (ECM). Electrospun dressing accelerates the wound healing process by transferring drugs or active agents to the wound site sooner. This review provides a concise overview of the recent developments in bioactive electrospun dressings, which are effective in treating acute and chronic wounds and can successfully heal the wound. We also discuss bioactive agents used to incorporate electrospun wound dressings to improve their therapeutic potential in wound healing. In addition, here we present commercial dressings loaded with bioactive agents with a comparison between their features and capabilities. Furthermore, we discuss challenges and promises and offer suggestions for future research on bioactive agent-loaded nanofiber membranes to guide future researchers in designing more effective dressing for wound healing and skin regeneration.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".