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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".