Role of Oxidative Stress in Erythropoietin-Induced Hypertension in Uremic Rats
Bibliographic record
Abstract
BACKGROUND: Erythropoietin (EPO) administration in uremic rats leads to an increase in blood pressure (BP). Because chronic renal failure has been associated with oxidative stress, we hypothesize that EPO treatment could accentuate this condition and contribute to hypertension. The present study was designed to investigate the role of reactive oxygen species in EPO-induced hypertension and the effect of tempol, a superoxide dismutase-mimetic. METHODS: Renal failure was induced by a two-stage 5/6 nephrectomy followed by a 3-week stabilization period. Uremic rats were divided into four groups and received for 4 weeks: vehicle; EPO (100 U/kg, subcutaneously, three times per week); vehicle + tempol (1 mmol/l in drinking water); and EPO + tempol. Systolic BP and biochemical parameters were assessed before and at the end of the treatment. Renal histology, creatinine clearance rate, endothelin-1 (ET-1) concentrations and superoxide anion production were assessed at the end of the study. RESULTS: The uremic rats developed anemia and hypertension. ET-1 concentrations and superoxide anion production were increased. EPO administration corrected anemia, but accentuated hypertension and renal injuries such as glomerulosclerosis, interstitial fibrosis, and inflammation. EPO therapy further increased tissue levels of ET-1 and superoxide anion production. Tempol treatment improved hypertension and renal injury, and reduced ET-1 concentrations and superoxide anion production. CONCLUSION: Oxidative stress contributes to the development of hypertension and to the progression of renal injuries in uremic rats. EPO administration further increases oxidative stress, which might partly account for the accentuation of hypertension and renal injury.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".