Systemic and renal haemodynamic changes in renal schemia/reperfusion injury: impact of erythropoietin
Bibliographic record
Abstract
The objective of this study was to investigate the effects of erythropoietin (EPO) on systemic and renal hemodynamics in a rat model of renal ischemic/reperfusion (I/R) injury. We used 30 male Sprague-Dawley rats distributed among the following 3 groups (10 rats per group): (i) the sham-operated group, (ii) the control group (I/R injury only), and (iii) the EPO-treated group (I/R injury with 1500 U EPO·(kg body mass)⁻¹ on day 0, and 500 U·kg⁻¹ on days 2 and 4 after ischemia). Renal function, arterial blood pressure (ABP), renal plasma flow (RPF), renal blood flow (RBF), and renal vascular resistance (RVR) were measured on days 1, 2, and 7 after ischemia. The expression of endothelial NO synthase (eNOS) and histopathology of kidney were evaluated on day 7. The contractility of aortic strips was recorded from the different groups. The results show that renal function and histopathology were significantly improved after treatment with EPO. Compared with the control group, the EPO-treated group showed a significant increase in RPF, RBF, haematocrite, ABP, eNOS expression, and a decrease in RVR (p < 0.05).The response of aortic strips to the relaxant effect of acetylcholine was improved in the EPO-treated group. In conclusion, treatment with EPO improves renal function and renal haemodynamics in renal I/R injury, and causes significant rise of ABP and haematocrite value.
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 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) | 0.001 | 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".