Recombinant globular adiponectin inhibits lipid deposition by p38 MAPK/ATF-2 and TOR/p70 S6 kinase pathways in chicken adipocytes
Bibliographic record
Abstract
Adiponectin plays a fundamental role in lipid and carbohydrate metabolism. However, its role in adipocyte differentiation remains controversial. To investigate the effect of gAd on lipid deposition in chicken adipocytes and its related signaling pathways, 200 μg/mL recombinant globular adiponectin, isoproterenol, SB253580, leucine, and rapamycin were used to treat chicken adipocytes. Results demonstrated that gAd increased the expression of endogenous adiponectin and AdipoR1 (P < 0.01); gAd inhibited triglyceride (TG) accumulation in chicken adipocyte and increased the release of free fatty acids (FFA) in medium; gAd decreased the expression of adipogenic marker genes CCAAT/enhancer binding protein alpha (C/EBPα) and fatty acid synthase (FAS), while activating the expression of lipolytic marker gene adipose triglyceride lipase (ATGL) (P < 0.01). Meanwhile, gAd activated the phosphorylation levels of p38 mitogen-activated protein kinase (p38 MAPK) and activating transcription factor 2 (ATF-2), and suppressed the phosphorylation levels of rapamycin (TOR) and p70 S6 Kinase (P < 0.01). In conclusion, the results demonstrate that gAd has an ability to inhibit lipids deposition in chicken adipocyte, which depends on the p38 MAPK/ATF-2 and TOR/p70 S6 Kinase pathways.
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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".