Web based educational systems-governance, accountability, and assessment
Bibliographic record
Abstract
There are multiple methods to prepare lipoaspirate for autologous fat transfer; however, graft retention remains unpredictable. The purpose of this study was to compare the cellular and protein composition of adipose grafts and the stromal vascular fraction (SVF) resulting from three common techniques to prepare adipose grafts. Adipose grafts were harvested from healthy donors and processed via three techniques: centrifugation (C), a single-filter (SF) device, and a double-filtration (DF) system. Part of each graft was analyzed or further processed to isolate the SVF. Cell viability, surface markers, cytokine, and growth factors were compared between the graft and SVF as well as adipose-derived stem cells (ASCs). Overall, we found variations across the three processing techniques and among the graft components (ASCs, SVF, and fat). Cell viability within the grafts was similar (94.6%, 92.3%, and 93.6%; <i>P</i> = 0.93). The trend was a greater percentage of ASCs from SF versus DF or centrifugation (6.95%, 4.63%, and 1.93%, respectively, <i>P</i> = 0.06). Adipogenic markers (adiponectin and leptin) were similar among all three grafts (<i>P</i> = 0.45). Markers of tissue remodeling were greatest in the SVF compared with fat and ASCs, regardless of processing technique. There was higher relative expression of MMP-9 (2×), Extracellular matrix metalloproteinase inducer (EMMPRIN) (2.5×), endoglin (5×), and IL-8 (1.5×) in the SVF (<i>P</i> < 0.005). Our study identified differences in cytokine expression in adipose grafts and the SVF, particularly in cytokines important in inflammation and wound healing. These secretomes may impact graft retention and fat necrosis and have the potential implications in cell-assisted lipotransfer. There were no significant differences between the final products of any of the processing techniques.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".