Effect of Zuogui Recipe on glucose metabolism and antioxidative stress in MKR mice of Type 2 diabetes
Bibliographic record
Abstract
Objective To observe the effect of Zuogui Recipe on glucose metabolism and antioxidative stress in MKR mice. Methods Fifty MKR mice were identified and randomly grouped. They were model group (MG),low Zougui Recipe group (LZG),middle Zuogui Recipe group (MZG),high Zuogui Recipe group (HZG),and positive control group (Rosiglitazone Maleate Tablets,PCG),ten mice each group. LZG,MZG,HZG,and PCG were treated by ig corresponding drug for 30 d. Using C57 mice as controls,the fasting blood glucose (FBG) level was determined before administration,after administration day 15,and day 30,respectively. And the serum insulin level was determined by radioimmunoassay. Total antioxidative capacity (T-AOC),malondiadehyde (MDA) levels,superoxide diamutase (SOD),and glutathioneperoxidase (GSH-Px) activities in heart,kidney,and liver tissue were determined by spectrophotometric method in 0.5 h of last administration. Results After treating MKR mice with Zuogui Recipe,the hyperglycemia was significantly ameliorated and the serum insulin in HZG was decreased (P0.05 and 0.01) in a dose dependent manner. The levels of MDA were significantly higher (P0.05 and 0.01),while capacity of T-AOC and activities of SOD and GSH-Px were significantly lower (P0.01) in heart,kidney,and liver tissue of MKR mice than those of C57 mice. After treating with Zuogui Recipe,the levels of MDA were significantly decreased (P0.05 and 0.01) and capacity of T-AOC,activities of SOD and GSH-Px in heart,kidney,liver tissue of MKR mice was significantly increased (P0.05 and 0.01) in a dose dependent manner. Conclusion The mechanism of protective effect of Zuogui Recipe on heart,kidney,and liver may be partly correlated with increasing the antioxidative capacity in MKR mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".