High-Fat Diets-Induced Metabolic Alterations Alter the Differentiation Potential of Adipose Tissue-Derived Stem Cells
Bibliographic record
Abstract
Background: Adipose tissue-derived stem cells (ASC) possess the ability to differentiate into adipocytes or endothelial cells to help in the adipogenesis, vasculogenesis and vascular repair. This study aims at determining the impact of high-fat diets (HFD)-induced type 2 diabetes (T2D) on the differentiation potential of ASC. Results: C57BL/6J male mice were fed a vegetal (VD) or an animal (AD) HFD. Isolation of ACS from mice showing different levels of metabolic alterations reveals that advanced T2D did not affect the number of cells per gram of tissue. Rather, a higher proportion of inflammatory CD36+ cells was identified in HFD fed mice. Despite a marked decreased expression of adipogenic genes (aP2, C/EBPα and PPARγ2), ASC from HFD groups had a higher adipogenic potential and a lower endothelial differentiation potential in vitro compared to control. ASC from the VD group had enhanced cyclin B1 expression and had more adipogenic potential compared to AD group. Conclusion: Our results demonstrate that the metabolic modifications, linked to the nature of fatty acids in diets, modulate the differentiation potential of ASC with increased adipogenesis to the detriment of the endothelial pathway. Results highlight the importance of evaluating the ASC differentiation behavior in a context of autologous cell-based therapy for the repair of vascular tissues in diabetic patients.
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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) | 0.001 | 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".