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Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it