In-Situ-Generated Vasoactive Intestinal Peptide Loaded Microspheres in Mussel-Inspired Polycaprolactone Nanosheets Creating Spatiotemporal Releasing Microenvironment to Promote Wound Healing and Angiogenesis
Bibliographic record
Abstract
Vasoactive intestinal peptide (VIP) was reported to promote angiogenesis. Electrospun nanofibers lead to idea wound dressing substrates. Here we report a convenient and novel method to produce VIP loaded microspheres in polycaprolactone (PCL) nanofibrous membrane without complicated processes. We first coated mussel-inspired dopamine (DA) to nanofibers, then used strong adhesive DA to absorb the functional peptide. PCL membrane was then immersed into acetone to generate microspheres with VIP loading. We employed high pressure liquid chromatography to record encapsulation efficiency of (31.8 ± 2.2)% and loading capacity of (1.71 ± 0.16)%. The release profile of VIP from nanosheets showed a prolonged release. The results of laser scanning confocal microscope, scanning electron microscope and cell counting kit-8 proliferation assays showed that cell adhesion and proliferation were promoted. In order to verify the efficacy on wound healing, in vivo implantation was applied in the full-thickness defect wounds of BALB/c mice. Results showed that the wound healing was significantly promoted via favoring the growth of granulation tissue and angiogenesis. However, we found wound re-epithelialization was not significantly improved. The resulting VIP-DA-coated PCL (PCL-DA-VIP) nanosheets with spatiotemporal delivery of VIP could be a potential application in wound treatment and vascular tissue engineering.
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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) | 0.000 | 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".