Adipogenic and Antiapoptotic Protein Levels in Human Adipose Stromal Cells after Weight Loss
Bibliographic record
Abstract
OBJECTIVE: Obesity is a major risk factor for type 2 diabetes and cardiovascular disease. However, current strategies to achieve sustained weight loss are often unsuccessful. Fat reaccumulation might be favored by enhanced adipose cell differentiation or survival in the postreduced state. RESEARCH METHODS AND PROCEDURES: We measured adipogenic and apoptotic protein expression in subcutaneous abdominal adipose stromal-vascular cells from 10 obese patients (7 women and 3 men) that were obtained before and after a 16% weight loss in a medically supervised weight loss program. RESULTS: After weight loss, protein expression was 2.4-fold higher (p < 0.005) for p42 C/CAAT enhancer binding protein alpha, but there was no change for peroxisome proliferator-activated receptor gamma1; both of these are adipogenic regulators. For neuronal apoptosis inhibitory protein, a protein associated with adipose cell apoptotic resistance, there was a rise of 1.7-fold (p < 0.02). DISCUSSION: Alterations in C/CAAT enhancer binding protein alpha and neuronal apoptosis inhibitory protein expression occurred in human adipose stromal-vascular cells after weight loss in a pilot study of 10 patients. It will be important for future studies to directly examine whether the adipogenic and antiapoptotic capacity of these cells is changed after weight loss.
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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".