Effects of adipokine zinc-α2-glycoprotein on adipose tissue metabolism after dexamethasone treatment
Bibliographic record
Abstract
Zinc-α2-glycoprotein (ZAG) has been demonstrated to play a role in stimulating lipid mobilization under normal conditions. However, further studies are required to determine whether ZAG overexpression can alleviate the reduction in plasma lipid levels under stress conditions. In the present study, we investigated the effects of ZAG on lipometabolism in white adipose tissue (WAT) after dexamethasone (DEX) stimulation using C57BL/6 male mice as the experimental models. Transcript and protein levels of genes associated with the β-adrenoreceptor (β-AR)/cyclic adenosine monophosphate/protein kinase a (PKA) pathway, lipid mobilization, and energy metabolism were determined by quantitative real-time polymerase chain reaction and Western blotting. Plasma levels of nonesterified fatty acid (NEFA) were measured using an automatic biochemical analyzer. Results indicated that plasma NEFA levels were decreased in the DEX group, but NEFA levels were rescued by ZAG overexpression. ZAG overexpression resulted in the upregulation of β3-AR and phosphorylated PKA protein relative to those of the DEX group. Analysis of lipometabolism showed that protein levels of phosphorylated hormone-sensitive lipase was reduced upon DEX treatment but were restored by ZAG overexpression. For energy metabolism, ZAG significantly upregulated the protein expression of carnitine palmitoyltransferase1a and cytochrome c oxidase subunit 1 relative to those of the DEX group. In conclusion, ZAG could alleviate DEX-induced decrease in plasma NEFA levels and this could be associated with the promoting lipid mobilization in WAT.
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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) | 0.001 | 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".