Skin Wound Healing Following Injecting Hyaluronic Acid Rejuvenating Complex, Polycaprolactone, or Combination Therapy: An Experimental Study
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the effects of hyaluronic acid rejuvenating complex, polycaprolactone, and their combination on skin wound healing, assessing their potential to accelerate tissue regeneration and optimize healing outcomes. MATERIALS AND METHODS: Forty eight Wistar Albino rats were randomly divided into four groups. Group 1 received hyaluronic acid rejuvenating complex injection, Group 2 received polycaprolactone injection, and Group 3 received a combination of both. Group 4 served as the control, undergoing incision without intervention. Skin biopsies were collected at baseline, day 7, and day 14 postincision. Wound healing was evaluated histologically using hematoxylin and eosin (H&E) and Masson's trichrome staining, focusing on epithelial thickness, collagen synthesis, and inflammatory cell infiltration. RESULTS: The combination therapy group (Group 3) exhibited the most pronounced wound healing response, demonstrating significantly accelerated re-epithelialization, enhanced collagen deposition, and well-structured granulation tissue by days 7 and 14. Additionally, inflammatory cell infiltration was markedly reduced, indicating a faster transition from the inflammatory to the proliferative phase. Compared to single-agent treatments, the combined approach resulted in superior tissue remodeling and a more efficient healing process. CONCLUSION: The dual administration of hyaluronic acid rejuvenating complex and polycaprolactone offers a synergistic effect, significantly enhancing skin wound healing compared to monotherapies. These findings highlight the potential of combination therapy as a promising strategy for improving wound repair and tissue regeneration in aesthetic and regenerative dermatology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".