Berberine Improves Plasma Glucose Control and Lipid Profiles in Streptozotocin‐Induced Diabetic Rats
Bibliographic record
Abstract
The objective of the present study was to determine the effect and mechanisms of action of BBR on plasma insulin and glucose levels in streptozotocin (STZ)‐induced diabetic rats. Male Wistar rats were injected with 50 mg/kg body weight of STZ to induce diabetes. The control rats were injected with the control vehicle solution. The fasting blood glucose was measured with an ACCU‐Check on d 3 and d 14 after the STZ injection. Rats with fasting glucose over 20 mmol/L were considered diabetic and treated with 100mg/kg body weight BBR for 50 d. Results showed that BBR reduced the fasting blood glucose as compared with the diabetic control. The OGTT test demonstrated that BBR treatment tended to improve oral glucose tolerance by showing decreased blood glucose at 30‐min after the oral glucose load. BBR slightly increased plasma insulin levels but did not reach statistical significance. BBR lowered dramatically the plasma total cholesterol, LDL‐cholesterol, triacylglycerides, and free fatty acids. Real time PCR did not reveal significant effects of BBR on the expression of several genes associated with fatty acid and glucose metabolism in the liver or skeletal muscle. The results suggest that BBR has antihyperlipidaemic and antidiabetic effects in diabetic rats. This work was supported by the National Research Council of Canada.
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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) | 0.002 | 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".