Overview of Tissue Engineering and Drug Delivery Applications of Reactive Electrospinning and Crosslinking Techniques of Polymeric Nanofibers with Highlights on Their Biocompatibility Testing and Regulatory Aspects
Bibliographic record
Abstract
Traditional electrospinning is a promising technique for fabricating nanofibers for tissue engineering and drug delivery applications. The method is highly efficient in producing nanofibers with morphology and porosity similar to the extracellular matrix. Nonetheless, and in many instances, the process has faced several limitations, including weak mechanical strength, large diameter distributions, and scaling-up difficulties of its fabricated electrospun nanofibers. The constraints of the polymer solution's intrinsic properties are primarily responsible for these limitations. Reactive electrospinning constitutes a novel and modified electrospinning techniques developed to overcome those challenges and improve the properties of the fabricated fibers intended for various biomedical applications. This review mainly addresses reactive electrospinning techniques, a relatively new approach for making in situ or post-crosslinked nanofibers. It provides an overview of and discusses the recent literature about chemical and photoreactive electrospinning, their various techniques, their biomedical applications, and FDA regulatory aspects related to their approval and marketing. Another aspect highlighted in this review is the use of crosslinking and reactive electrospinning techniques to enhance the fabricated nanofibers' physicochemical and mechanical properties and make them more biocompatible and tailored for advanced intelligent drug delivery and tissue engineering 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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".