Nanotechnology-based approaches for scar minimization in plastic surgery: Systematic overview and future perspectives
Bibliographic record
Abstract
Scarring is a significant problem in plastic surgery, which affects both aesthetic and functional results and leads to psychological discomfort. Classical methods of scar treatment (surgery, laser treatment, silicone sheeting) usually have very variable outcomes with high recurrence rates. They are not good options to treat pathological scarring (hypertrophic and keloid scars). Nanotechnology has provided far-fetched solutions to problems using unique characteristics of the nanomaterial, like improved drug delivery, biocompatibility, and tissue regenerative properties. This systematic review provides an overview of nanotechnology applications for scar minimization, with particular emphasis on approaches such as nanoparticle-mediated drug delivery, nanomicroneedles, nanoscaffolds, and biomimetic nanomaterials. Based on a review of literature from 2015 to 2025, these studies demonstrate potential usefulness in regulating wound healing phases, reducing inflammation, and promoting scarless tissue regeneration. Clinical applications of the review include the use of deliverable anti-fibrotic agents and combination with stem cell therapy to give superior outcomes compared to standard management, as revealed by higher mean Vancouver Scar Scale scores and low rates of recurrence. Irrespective of the advantages, challenges like regulations, cost of production, and safety issues in the long run do exist. This article aims to close the loop in nanotechnology development before its application in clinical practice, forecasting the future, and assessing the potential of 3D-printed nanodressings using AI for personalized scar treatment. Differently, nanotechnology has the potential to change and have a significant positive effect on patient cases in plastic surgery through the environment-friendly alliance between nanoscientists and plastic surgeons.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".