Topical vs. Intralesional Drug Delivery Systems in Hypertrophic Scar Management: A Comparative Study Using Nanocarrier-Based Formulations
Bibliographic record
Abstract
BackgroundHypertrophic scars pose considerable aesthetic and functional difficulties, frequently arising from trauma, burns, or surgical procedures. Current treatment modalities encompass both topical and intralesional therapies; however, emerging drug delivery platforms, such as nanocarrier-based systems, present innovative strategies to improve therapeutic efficacy. This study evaluates the efficacy and safety of topical versus intralesional administration of nanocarrier-based corticosteroid formulations, concentrating on treatment response, scar regression, and patient adherence. Objective: To assess and contrast the therapeutic efficacy of topical versus intralesional nanocarrier-mediated corticosteroid therapies in individuals with hypertrophic scars. MethodsThis prospective, comparative interventional study was performed at Patna Medical College and Hospital, involving 60 patients diagnosed with hypertrophic scars. Participants were randomly assigned to two groups: Group A received a topical liposomal corticosteroid gel, while Group B received an intralesional corticosteroid suspension encapsulated in ethosomal nanocarriers. The treatment response was evaluated using the Vancouver Scar Scale (VSS) at baseline and during periodic follow-ups over a one-year period. Adverse events, patient-reported outcomes, and satisfaction were documented as well. ResultsBoth groups exhibited a statistically significant decrease in VSS scores following treatment (p < 0.05). Nevertheless, the intralesional group exhibited a more rapid onset of improvement, especially regarding scar height and vascularity. The topical nanocarrier group was preferred due to its pain tolerance, compliance, and lack of injection-related complications. No systemic adverse effects were noted in either cohort. ConclusionBoth topical and intralesional nanocarrier-based drug delivery systems are efficacious in the management of hypertrophic scars. Intralesional therapy facilitates rapid and significant scar regression, whereas topical nanocarrier systems present a non-invasive, patient-friendly option with satisfactory therapeutic effectiveness. Customizing therapy according to scar attributes and patient preferences may improve clinical results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".