Alternatively Activated M2 Macrophages Improve Autologous Fat Graft Survival in a Mouse Model through Induction of Angiogenesis
Bibliographic record
Abstract
BACKGROUND: Variability in graft retention with subsequent undercorrection remains a significant limitation of autologous fat grafting. The authors evaluated whether graft retention in a mouse model could be improved via graft supplementation with alternatively activated M2 macrophages, cells known to play a critical role in tissue repair. METHODS: Grafts from C57BL/6 mouse inguinal fat pads were supplemented with M2 macrophages generated by intraperitoneal Brewer's thioglycollate injection and in vitro culture. Grafts with saline or M2 macrophages were injected under recipient mouse scalps and assessed by serial micro-computed tomographic analysis. Explanted grafts underwent immunohistochemical and flow cytometric analyses. M2 culture supernatants were added to stromal vascular fraction adipose-derived stem cells to assess adipogenic gene expression induction. RESULTS: One month after graft injection, no significant difference was noted between M2 macrophage-supplemented (105 ± 7.0 mm) and control graft volumes (72 ± 22 mm). By 3 months after injection, M2 macrophage-supplemented grafts remained stable, whereas controls experienced further volume loss (103 ± 8 mm versus 39.4 ± 15 mm; p = 0.015). Presence of macrophages in supplemented grafts was confirmed by flow cytometry. M2 macrophage-supplemented grafts exhibited a 157 percent increase in vascular density compared with controls (p < 0.05). Induction of adipogenic C/EBPα gene expression was observed with M2 supernatants addition to stromal vascular fraction adipose-derived stem cells. CONCLUSIONS: M2 macrophages improve autologous fat graft volume retention by stimulating angiogenesis. These findings provide proof-of-principle for development of fat grafting techniques that harness reparative properties of M2 macrophages.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".