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Record W4391955364 · doi:10.1177/20595131241230739

Autologous nanofat harvested from donor site of full-thickness skin or skin flap grafting for the treatment of early postburn scarring: a case series

2024· article· en· W4391955364 on OpenAlexaboutno aff
H. Wang, Yang Liu, Yan Li, Fu Han, Qiaohua Chen, Juntao Han, Dahai Hu

Bibliographic record

VenueScars Burns & Healing · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicineSurgeryContractureSkin graftingDeformityScar tissue

Abstract

fetched live from OpenAlex

Introduction: Postburn scarring often presents a specific reconstructive challenge from both functional and cosmetic perspectives. The purpose of this study was to investigate whether autologous nanofat harvested from the donor site of full skin or a skin flap can be reused for the treatment of early postburn scaring. Methods: From July 2018 to April 2022, patients with early postburn scarring underwent scar reconstruction surgery with full-thickness skin or a skin flap for a contour deformity and/or scar contracture, and autologous nanofat grafting was performed during the same operation. The Vancouver Scar Score (VSS) and the itch and pain scores were evaluated at the preoperation time point as well as at 2-3 weeks and 3-months postoperation. A comparison was made among the same patients at different time points. Results: A total of 17 patients, aged from 18 months to 62 years old were included in this analysis. The VSS was reduced from 10.00 ± 2.12 to 7.41 ± 1.277 at the 2-3-week postoperation time point, and to 5.53 ± 1.37 at the 3-month postoperation time point. The pain and itch score were reduced from 4.65 ± 1.37 and 6.35 ± 1.27, to 3.70 ± 1.10 and 4.94 ± 1.30 at the 2-3-week postoperation time point, and to 3.00 ± 1.28 and 3.94 ± 0.97 at the 3-month postoperation time point respectively. The VSS and pain and itch scores showed a statistically significant reduction (P < 0.05) at the 2-3-week and 3-month postoperative follow-ups compared with the preoperation time point. Conclusion: Autologous nanofat grafting from donor sites of full thickness skin or skin flap may be a promising treatment for an early postburn scaring as it promotes scar softening, improves itching and pain within the scar. However, this is a small case series with only 17 patients. Further conclusions need to be drawn through expanded samples for randomized controlled clinical trials. Lay Summary: Hypertrophic scarring is the most common complication after partial thickness burn injury, and the complex pathogenesis and prolonged dynamic process render treatments only marginally effective. In the past few decades, with the technological advances of liposuction and fat grafting, nanofat grafting has been used in a variety of surgical fields, including wound healing, scleroderma, facial rejuvenation, and neuralgia. However, the role of nanofat grafting is not well documented in the prevention and treatment of early postburn scarring. Full-thickness skin grafting or skin flap transplantation is the most common method for the reconstruction of a hypertrophic scaring until now. In the current study, we harvested subcutaneous fat during the preparation of the full-thickness skin or skin flap, prepared nanofat and injected it in the scar located at a nonsurgical site. Comparison of the pre- and postoperation scores for scar color, scar thickness, scar stiffness, and scar regularity showed that the postoperation scores were decreased significantly and that there was a significant improvement in scar pigmentation and thickness as well astheaesthetic outcome after treatment. Most importantly, reductions in the scores for pain and itching could be assessed objectively. It seems that the nanofat grafting is a potential method for prevention and treatment for early postburn scaring.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.345
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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