Multifunctional Nanofiber Membranes Constructed by Microfluidic Blow-Spinning to Inhibit Scar Formation at Early Intervention Stage
Bibliographic record
Abstract
Pathological scarring has been a challenge in skin injury repair since ancient times, and prophylactic treatment in the early stages of wound healing usually results in delayed wound healing. In this study, poly(ethylene oxide) (PEO) and chitosan (CTS) were used as carrier materials to construct multifunctional pirfenidone (PFD)/CTS/PEO (PCP) nanofiber membranes (NFMs) loaded with PFD by microfluidic blow-spinning (MBS). MBS is a good method for quickly, safely, and greenly constructing large-area manufacturing of inexpensive NFMs. PCP NFMs were uniform in external morphology, with diameters ranging from 200 to 500 nm. The encapsulation efficiency of the drug-loaded PCP NFMs was above 80%, which had a good slow release, visualization, water absorption, and biocompatibility. The inhibitory effect of PCP NFMs on normal human dermal fibroblasts was dose-dependent and inhibited the expression of the transforming growth factor-β1/SMAD family member 3 (TGF-β1/SMAD3) signaling pathway. PCP NFMs showed significant antibacterial effects against both Staphylococcus aureus and Escherichia coli . In the rabbit ear scar experiment, the wound healed about 70% on day 5 and almost completely on day 10 after PCP-3 NFMs treatment, with the thinnest scar tissue, skin color, tenderness close to normal tissue, and a Vancouver scar scale score of less than 5. PCP-3 NFMs had good effects on anti-inflammatory, wound healing, and collagen-I deposition reducing effects. In conclusion, PCP-3 NFMs can both promote wound healing and intervene to inhibit pathological scarring in advance, making them a potential multifunctional wound dressing for early prevention and treatment of pathological scarring.
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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".