Effects of curcumin and captopril on the functions of kidney and nerve in streptozotocin-induced diabetic rats: role of angiotensin converting enzyme 1
Bibliographic record
Abstract
Oxidative stress and inflammation are involved in the development and progression of diabetes and its complications. The renin-angiotensin system also plays an important role in the pathogenesis of diabetes and its complications. We hypothesized that curcumin and captopril would restore the kidney and nerve functions of diabetic rats through their angiotensin converting enzyme 1 (ACE1) inhibiting activity as well as their antioxidant and anti-inflammatory effects. Diabetes was induced by a single intraperitoneal injection of streptozotocin (100 mg·kg(-1) body weight). One week after induction of diabetes, rats were treated with 100 mg·kg(-1)·day(-1) curcumin or 50 mg·kg(-1)·day(-1) captopril orally for 6 weeks. Compared with diabetic control rats, curcumin- or captopril-treated diabetic rats had significantly improved blood glucose, lipid profile, kidney/body weight ratio, serum creatinine, blood urea nitrogen (BUN), and pain thresholds assessed by Von Frey filaments, hot plate test, and tail-flick test. Diabetic control rats showed increased levels of total peroxide, renal and neural tumor necrosis factor-α and interleukin-10, and renal ACE1 compared with nondiabetic rats. Although treatment with either curcumin or captopril restored the altered variables, captopril was more effective in reducing these variables. ACE1 was positively correlated with BUN and creatinine and negatively correlated with paw withdrawal threshold, hot plate reaction time, and tail-flick latency, suggesting a possible causal relationship. We conclude that curcumin and captopril protect against diabetic nephropathy and neuropathy by inhibiting ACE1 as well as oxidation and inflammation. These findings suggest that curcumin and captopril may have a role in the treatment of diabetic nephropathy and neuropathy.
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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 | 0.001 |
| 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 | 0.001 | 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".