<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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".