24 The CD34+ Adipose-derived Stem Cells from Fat Grafts Accelerates Keratinocyte Wound Closure
Bibliographic record
Abstract
Abstract Introduction Adipose derived stem cells (ADSCs) possess high regenerative potential and therefore are postulated to enhance re-epithelialization via recruitment of keratinocytes into the wound bed, and thusly improve healing of severe burns. Although ADSC’s are enriched in the stromal vascular fraction (SVF) of fat grafts, the ADSC frequency in these samples is very low (1%) which has hindered their further characterization. So far, the CD34+ subset of SVF cells has shown to contain up to 75% of all the ADSCs present in SVF samples but their ADSC frequency remains low (2.5%). Unfortunately, while the ADSC frequency increases in passaged SVF cells, the expression of CD34 is lost in these cells. Therefore, there is a pressing need to identify new ADSC biomarkers to study ADSC mechanisms that accelerate wound closure. In this study, we aim to identify new ADSC biomarkers and to examine the impact of ADSC-enriched cells on skin wound closure. Methods Single cells from SVFs were obtained from disassociated fat tissue, collected from excess lipoaspirates used in patients undergoing mastectomies. Native cells (P0), and passaged cells (P3) were placed in cell cultures and the ADSC numbers were obtained using the colony forming unit-fibroblast assays. The expression of 244 cell surface receptors was examined in P0 (low ADSCs) and P3 (high ADSCs) SVF cells to obtain new ADSC biomarkers. In another experiment, the impact of ADSC-enriched CD34+ SVF cells on wound closure was assessed. Using transwell inserts containing adult human epidermal keratinocyte (HEKa) cells in the bottom and the bulk SVF or CD34+ SVF cells in the top chamber, a scratch was induced in the HEKa cell layer and the scratch area was examined at 0h and 12h. Results Our cell surface antibody array experiments revealed 10 potentially new ADSC biomarkers that will be used to examine their ability to enrich for ADSCs. Both the bulk SVF and the CD34+ SVF cells showed a 2-fold increase in would closure. It is noteworthy that ADSCs frequency in the CD34+ SVF cells is still very low, and these experiments need to be conducted using the new SVF subsets with potentially higher ADSC frequencies based on our cell surface array. Conclusions The ADSC-enriched CD34+ SVF enhance keratinocyte wound closure, a function that is important to healing of severe burns. Further studies are needed to validate our findings using more purified SVF subsets to identify ADSC-regulated keratinocyte wound closure mechanisms.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".