Impact of Tissue Harvesting Sites on the Cellular Behaviors of Adipose-Derived Stem Cells: Implication for Bone Tissue Engineering
Bibliographic record
Abstract
The advantages of adipose-derived stem cells (AdSCs) over bone marrow stem cells (BMSCs), such as being available as a medical waste and less discomfort during harvest, have made them a good alternative instead of BMSCs in tissue engineering. AdSCs from buccal fat pad (BFP), as an easily harvestable and accessible source, have gained interest to be used for bone regeneration in the maxillofacial region. Due to scarcity of data regarding comparative analysis of isolated AdSCs from different parts of the body, we aimed to quantitatively compare the proliferation and osteogenic capabilities of AdSCs from different harvesting sites. In this study, AdSCs were isolated from BFP (BFPdSCs), abdomen (abdomen-derived mesenchymal stem cells (AbdSCs)), and hip (hip-derived mesenchymal stem cells (HdSCs)) from one individual and were compared for surface marker expression, morphology, growth rate, and osteogenic differentiation capability. Among them, BFPdSCs demonstrated the highest proliferation rate with the shortest doubling time and also expressed vascular endothelial markers including CD34 and CD146. Moreover, the expression of osteogenic markers were significantly higher in BFPdSCs. The results of this study suggested that BFPdSCs as an encouraging source of mesenchymal stem cells are to be used for bone tissue engineering.
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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 | 0.000 |
| 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 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".