Effect of N-Acetylcysteine on Adipose-Derived Stem Cell and Autologous Fat Graft Survival in a Mouse Model
Bibliographic record
Abstract
BACKGROUND: Autologous fat grafting is a popular reconstructive technique, but is limited by inconsistent graft retention. The authors examined whether a widely available, clinically safe antioxidant, N-acetylcysteine, could improve adipose-derived stem cell survival and graft take when added to tumescent solution during fat harvest. METHODS: Inguinal fat pads were harvested from C57BL/6 mice using tumescent solution with or without N-acetylcysteine. Flow cytometric, proliferation, and differentiation assays were performed on isolated primary adipose-derived stem cells and 3T3-L1 preadipocytes treated with or without hydrogen peroxide and/or N-acetylcysteine. N-Acetylcysteine-treated or control grafts were injected under recipient mouse scalps and assessed by serial micro-computed tomographic volumetric analysis. Explanted grafts underwent immunohistochemical analysis. RESULTS: In culture, N-acetylcysteine protected adipose-derived stem cells from oxidative stress and improved cell survival following hydrogen peroxide treatment. Combined exposure to both N-acetylcysteine and hydrogen peroxide led to a 200-fold increase in adipose-derived stem cell proliferation, significantly higher than with either agent alone. N-Acetylcysteine decreased differentiation of adipose-derived stem cells into mature adipocytes, as evidenced by decreased transcription of adipocyte differentiation markers and reduced Oil Red-O staining. In vivo, N-acetylcysteine treatment resulted in improved graft retention at 3 months compared with control (46 versus 17 percent; p = 0.027). N-Acetylcysteine-treated grafts demonstrated less fibrosis and inflammation, and a 33 percent increase in adipocyte density compared with controls (p < 0.001) that was not associated with increased vascularity. CONCLUSION: These findings provide proof of principle for the addition of N-acetylcysteine to tumescent harvest solution in the clinical setting to optimize fat graft yields.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".