Renal Protective Effect of Xiao-Chai-Hu-Tang on Diabetic Nephropathy of Type 1-Diabetic Mice
Bibliographic record
Abstract
Xiao-Chai-Hu-Tang (XCHT), a traditional Chinese medicine formula consisting of seven medicinal plants, is used in the treatment of various diseases. We show here that XCHT could protect type-1 diabetic mice against diabetic nephropathy, using streptozotocin (STZ)-induced diabetic mice and high-glucose (HG)-exposed rat mesangial cell (RMC) as models. Following 4 weeks of oral administration with XCHT, renal functions and renal hypertrophy significantly improved in the STZ-diabetic mice, while serum glucose was only moderately reduced compared to vehicle treatment. Treatment with XCHT in the STZ-diabetic mice and HG-exposed RMC resulted in a decrease in expression levels of TGF-β1, fibronectin, and collagen IV, with concomitant increase in BMP-7 expression. Data from DPPH assay, DHE stain, and CM-H(2)DCFDA analysis indicated that XCHT could scavenge free radicals and inhibit high-glucose-induced ROS in RMCs. Taken together, these results suggest that treatment with XCHT can improve renal functions in STZ-diabetic mice, an effect that is potentially mediated through decreasing oxidative stress and production of TGF-β1, fibronectin, and collagen IV in the kidney during development of diabetic nephropathy. XCHT, therefore merits further investigation for application to improve renal functions in diabetic disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".