Comparison of Harvest and Processing Techniques for Fat Grafting and Adipose Stem Cell Isolation
Bibliographic record
Abstract
BACKGROUND: Variability in harvest and processing technique may impact the success of fat grafting. This study compared properties of fat grafts produced by differing methods and assessed volume retention of the grafted tissue in a nude mouse model. METHODS: In phase I, fat was harvested by either suction-assisted lipoaspiration or ultrasound-assisted lipoaspiration and then filtered using two different pore sizes. Graft material was analyzed for average parcel size; relative oil, fat, and aqueous fractions; and stromal vascular fraction yield. Filtrands and filtrates were injected into athymic nude mice. In phase II, lipoaspirate harvested by suction-assisted lipoaspiration only was processed by centrifugation, cotton gauze rolling, or filtration, and then studied in a similar manner. RESULTS: Fat harvested by ultrasound- and suction-assisted lipoaspiration had comparable stromal vascular fraction counts and graft retention in vivo. Ultrasound-assisted lipoaspiration released only slightly more oil than suction-assisted lipoaspiration; filtering with either 500- or 800-µm pore size effectively removed fluid and oil. Centrifugation, cotton-gauze rolling, and filtration also effectively removed fluid and oil. In vivo graft retention and stromal vascular fraction yield was highest with the cotton gauze method. Histologic analysis of all explants showed intact adipose tissue. CONCLUSIONS: Ultrasound- and suction-assisted lipoaspiration yielded similar retention of fat grafts in a xenograft model. Processing with cotton gauze rolling may be best suited for grafting cosmetically sensitive areas of the body in which optimal retention is critical and lower total graft volumes are needed. Filtration and centrifugation both effectively removed fluid fractions and resulted in comparable graft retention, and are more feasible when larger volumes are required.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".