Attenuation of Diabetic Nephropathy in Otsuka Long‐Evans Tokushima Fatty (OLETF) Rats with a Combination of Chinese Herbs (Tangshen Formula)
Bibliographic record
Abstract
Diabetic nephropathy is one of the most significant microvascular complications in patients with type 2 diabetics. The concise mechanism of diabetic nephropathy is unknown and there is no successful treatment. The objective of study was to investigate effects of Chinese herbs (Tangshen Formula) on diabetic nephropathy in Otsuka Long-Evans Tokushima Fatty (OLETF) rats. OLETF rats and LETO rats were divided into four groups: LETO control, OLETF diabetics, OLETF diabetics treated with Tangshen Formula, and OLETF diabetics treated with Monopril. Body weight, blood glucose, and 24 h urinary proteins were measured once every four weeks. Blood samples and kidney tissues were obtained for analyses of total cholesterol, triglyceride, whole blood viscosity, plasma viscosity, and pathohistological examination at 36 and 56 weeksrespectively. Untreated OLETF rats displayed diabetic nephropathy over the study period. Treatment of OLETF rats with Tangshen Formula attenuated the increases in blood glucose, body weight, 24 h urinary protein content, serum total cholesterol, whole blood viscosity and plasma viscosity at certain time. Treatment with Tangshen Formula also reduced glomerulosclerotic index and interstitial fibrotic index seen in OLETF rats. In conclusion, Tangshen Formula could attenuate the development of diabetic nephropathy in OLETF rat diabetic model.
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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".