Splenorenal reflex modulates renal blood flow in the rat
Bibliographic record
Abstract
We have previously shown that the splenorenal reflex controls renin release through splenic afferent and renal sympathetic nerves. We proposed that this reflex would also affect renal blood flow (RBF). RBF was measured in male Long Evans rats using transit-time flow probes. There were no significant differences between any of the experimental groups with respect to baseline values of RBF (8.9 +/- 0.4 ml min(-1), n= 25) or mean arterial pressure (MAP, 98.7 +/- 2.5 mmHg, n= 25). Splenic venous pressure was selectively raised (from 7.9 +/- 0.6 to 21.6 +/- 0.3 mmHg, n= 25) in anaesthetized rats by partial ligation of the splenic vein. This caused an immediate fall in RBF (-2.1 +/- 0.2 ml min(-1), n= 7) and in MAP (-12.4 +/- 2.8 mmHg, n= 7). The fall in RBF, but not the fall in blood pressure, was attenuated by renal denervation (DeltaRBF: - 0.7 +/- 0.1 ml min(-1), n= 6), splenic denervation (DeltaRBF: -0.8 +/- 0.1 ml min(-1), n= 6) and close renal arterial injection of the alpha1-adrenergic blocker phenoxybenzamine (12.5 microg; DeltaRBF: -0.8 +/- 0.1 ml min(-1), n= 6). Renal conductance fell only in the intact control group, i.e. the residual fall in RBF in the denervated and phenoxybenzamine-treated animals could be attributed to the fall in MAP. We also showed that splenic vein occlusion increased both splenic afferent (from 3.0 +/- 0.3 to 6.6 +/- 0.6 spikes s(-1), n= 5) and renal efferent (from 24.8 +/- 2.0 to 50.2 +/- 4.9 spikes s(-1), n= 9) nerve activity. We conclude that obstruction to splenic venous outflow, such as would occur in portal hypertension, initiates increased splenic afferent nerve activity and renal vasoconstriction through the splenorenal reflex, as well as a fall in blood pressure. We propose that this contributes to the renal and cardiovascular dysfunction observed in portal hypertension.
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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".