Research on PPARγ Expression in Transgenic Type 2 Diabetic MKR Mice Treated by Zuogui Recipe
Bibliographic record
Abstract
Objective To observe the effect of of Zuogui Recipe(ZR) on glucose metabolism and PPARγ expression in the liver,skeletal muscle and fat tissue of MKR mice.Methods Forty qualified MKR mice were equally randomized into 4 groups:model group(MG),low-dose ZP group(LZP),high-dose-ZP group(HZP) and positive control group(PCG).LZP,HZP and PCG were treated by corresponding drugs for 30 days.Using C57 mice as the controls,the fasting blood glucose(FBG) level and the serum insulin level were determined;the expression of PPARγ mRNA in the liver and skeletal muscle was determined by RT-PCR and the expression of PPARγ protein in the liver,skeletal muscle and fat tissue was determined by Western Blotting and SABC immunohistochemistry.Results After treatment,hyperglycemia in HZP was significantly ameliorated and fasting blood glucose was decreased(P 0.05 or P 0.01).Comparing with C57 mice,the expression of PPARγ mRNA were decreased in the liver and skeletal muscle of model group(P 0.01),and the expression of PPARγ protein were also decreased in the skeletal muscle and fat tissue(P 0.05).After treatment,the expression of PPARγ mRNA in the liver and skeletal muscle of HZP group and that of PPARγ mRNA in the liver,skeletal muscle and fatty tissue were significantly increased(P 0.05 or P 0.01 compared with the model group).The up-regulation of PPARγ mRNA and protein expression of ZP was similar to that of rosiglitazone maleate.Conclusion ZP has obvious effect on improving glucose metabolism in MKR mice,and its mechanism may be related to the increase of PPARγ expression in liver,skeletal muscle and fat tissue,the increase of sensitivity of peripheral tissue to insulin,and the improvement of insulin resistance in the mice.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".