Angiogenic Capacity of Human Adipose-Derived Stromal Cells During Adipogenic Differentiation: An <i>In Vitro</i> Study
Bibliographic record
Abstract
BACKGROUND: Improving vascularization of engineered adipose tissue constructs is a major challenge in the field of plastic surgery. Although human adipose-derived stromal cells (hASCs) are known to release factors that stimulate new blood vessel formation, detailed information about the effects of adipogenic differentiation on the angiogenic potential of hASCs remains largely unknown. In the present study, we studied the expression and secretion of a large panel of angiogenic factors during hASC differentiation and evaluated the effects of hASC-conditioned medium (hASC-CM) on endothelial cells. METHODS: hASCs were cultured on adipogenic medium or basal medium. Conditioned medium was collected, and cells were harvested following 0, 3, 7, 14, and 22 days of culture. The stage of adipogenic differentiation of hASC was assessed using Oil Red O staining, fatty acid binding protein-4 gene expression, and glycerol-3-phosphate dehydrogenase activity. RESULTS: Gene expression of vascular endothelial growth factor (VEGF), placental growth factor, angiopoietin-1 (ANGPT1), angiopoietin-2 (ANGPT2), and protein secretion of VEGF significantly increased during short-term adipogenic differentiation of hASCs. Moreover, conditioned medium from differentiated hASCs strongly enhanced endothelial cell numbers compared to conditioned medium from undifferentiated hASCs. CONCLUSION: In vitro adipogenic differentiation of hASCs improves their ability to support endothelial viable cell numbers and suggests that hASCs differentiated for a short period potentially improve angiogenic responses for in vivo implantation.
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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 | 0.001 | 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) | 0.001 | 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".