Enhanced Targeted Drug Delivery for Scar Prevention: Clathrin‐Coated Solid Lipid Nanoparticles for Model Drug Encapsulation
Bibliographic record
Abstract
Abstract Excessive scar formation is a major complication of wound healing. Premature release of anti‐scarring drugs can negatively impact healing. This study aims to develop a targeted delivery system for the controlled release of anti‐scarring drugs during the scar formation stage. Solid lipid nanoparticles (SLNs) coated with Clathrin, a cage‐like protein, to prevent premature drug release is developed. Insulin‐like growth factor (IGF) is conjugated to the SLNs for targeted delivery via its affinity for connective tissue growth factor (CTGF), a protein overexpressed during scar formation. The IGF‐Clathrin‐SLNs exhibited a size of 300 ± 20 nm and a zeta potential of 9.23 ± 0.4 mV. In vitro studies demonstrated sustained release of the encapsulated drug‐ kynurenic acid; less than 10% of kynurenic acid is released within three days, while over 50% is released within 10 h upon Clathrin removal using a surfactant at pH 8. Cellular uptake studies confirmed targeting efficacy. Fibroblasts with low CTGF expression displayed low uptake (<10%), whereas MCF7 cells with high CTGF expression showed significantly higher uptake (80%). This work demonstrates a promising targeted delivery platform for the controlled release of anti‐scarring drugs during scar formation.
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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".