Effect of Erythropoietin on Blood Pressure and on the Vascular Endothelial ET-1/ETB Receptor System
Bibliographic record
Abstract
BACKGROUND: Recombinant human erythropoietin (rhEPO) increases blood pressure (BP) and the vascular production of endothelin-1 in renal failure rats. This study was designed to investigate the effect of rhEPO on BP and on the ET-1/ET(B)R system in rats with normal renal function. To further characterize the effect of rhEPO on the ET-1/ET(B)R system, we also studied heterozygous (+/-) ET(B)R knockout (KO) mice. METHODS: The animals received either the vehicle or rhEPO (100 U/kg subcutaneously three times per week). ET(B)R(+/-) mice were compared with ET(A)R(+/-) and wild-type (WT) mice. In rats, the ET(B)R mRNA expression was assessed in blood vessels as well as the vascular ET(B)R density using immunohistochemistry. In mice, ET-1 concentration was measured in the thoracic aorta. RESULTS: RhEPO administration increased hematocrit levels in all treated animals. This therapy had no effect on BP in normal rats, but it did increase vascular and renal cortex ET(B)R mRNA expression. Immunohistochemistry confirmed that the ET(B)R density was increased in blood vessel endothelium in these normal rats. In contrast, rhEPO increased BP in ET(B)R(+/-) mice and this pressor effect was associated with higher ET-1 concentrations in the thoracic aorta. CONCLUSIONS: RhEPO exerts a pleotropic effect on the endothelial ET-1/ET(B)R system. The increase in endothelial ET(B)R expression may contribute to maintaining normal BP during rhEPO administration in normal animals. Conversely, conditions with deficient ET(B)R expression, such as in ET(B)R(+/-) mice, may lead to hypertension while receiving the same therapy.
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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 | 0.000 |
| 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".