<i>In vitro</i> differentiation of mouse brown preadipocytes is enhanced by <scp>IGFBP</scp>‐3 expression and reduced by <scp>IGFBP</scp>‐3 silencing
Bibliographic record
Abstract
OBJECTIVE: White adipocyte metabolism is regulated by insulin-like growth factor-binding protein (IGFBP)-3, but its effect on brown adipocytes is not known. This study investigated whether IGFBP-3 influences the proliferation and differentiation of brown preadipocytes in primary culture. METHODS: In vitro growth and differentiation of brown preadipocytes from wild-type mice, transgenic mice overexpressing human IGFBP-3 (PGKBP3), or its non-IGF-binding Gly56/Gly80/Gly81-mutant (PGKmutBP3), and wild-type brown preadipocytes transfected with IGFBP-3 siRNA were studied by us. In addition to IGF-I and IGFBP-3 expression, brown preadipocyte growth and differentiation were assessed by antiproliferating cell nuclear antigen, oil red O, brown fat gene expression, and phosphorylation states of Akt and ERK. RESULTS: Akt phosphorylation and IGF-I expression were paralleled by initial growth and differentiation and were slower for PGKBP3 brown preadipocytes than PGKmutBP3 and wild-type preadipocytes. Terminal adipocyte differentiation as assessed by lipid accumulation coincided with ERK inhibition and was greatest in PGKmutBP3 cells, followed by PGKBP3 cells and then wild-type cells, whereas adipocyte differentiation was poor after IGFBP-3 siRNA treatment. Thermogenic genes were increased by IGFBP-3 overexpression, but lower in differentiated PGKmutBP3 than PGKBP3 cells. CONCLUSIONS: Brown adipocyte growth and differentiation in vitro were affected by the manipulation of IGFBP-3 expression, suggesting that IGFBP-3 is a factor regulating brown adipocyte fate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".