Bioengineered Skin Substitutes in Aesthetic Reconstruction: Patient-Centered Applications
Bibliographic record
Abstract
Background: Restoring skin integrity and aesthetic appearance after trauma, disease, or congenital defects remains a complex challenge in reconstructive surgery. Traditional approaches, such as autologous skin grafting, often face hurdles including donor site complications, inconsistent cosmetic results, and limited tissue availability for large defects. Bioengineered skin substitutes have emerged as innovative solutions, closely replicating the structure and function of native skin to improve aesthetic outcomes. This review explores recent advancements in these technologies.Methods: A systematic literature search was conducted across PubMed, Scopus, Web of Science, and Embase, guided by PRISMA principles where applicable. Search terms included “bioengineered skin,” “skin substitutes,” “aesthetic reconstruction,” and “patient-reported outcomes.” Peer-reviewed studies from January 2015 to May 2025 addressing bioengineered skin substitutes, aesthetic outcomes, and patient-centered metrics were included. Data on substitute types, clinical efficacy, and patient-reported outcomes (PROs) were extracted and synthesized qualitatively.Results: Bioengineered skin substitutes, from acellular dermal matrices (e.g., Integra, AlloDerm) to cellular constructs and 3D bioprinted tissues, show enhanced scar quality, reduced contractures, and greater patient satisfaction in facial, burn, and breast reconstruction. PROs, measured via tools like the Vancouver Scar Scale and FACE-Q, reflect improvements in cosmetic appearance, pain reduction, and quality of life. Challenges include vascularization, adnexal regeneration, and cost, with ethical considerations and long-term stability as ongoing concerns.Conclusions: Bioengineered skin substitutes are transforming aesthetic reconstruction by enhancing both cosmetic and functional outcomes while prioritizing patient needs. Future efforts should focus on improving vascularization, adnexal regeneration, and standardized PROs to support broader clinical use. This review offers a valuable resource for researchers and clinicians aiming to refine reconstructive approaches and elevate patient well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".