Splenic baroreceptors control splenic afferent nerve activity
Bibliographic record
Abstract
Stenosis of either the portal or splenic vein increases splenic afferent nerve activity (SANA), which, through the splenorenal reflex, reduces renal blood flow. Because these maneuvers not only raise splenic venous pressure but also reduce splenic venous outflow, the question remained as to whether it is increased intrasplenic postcapillary pressure and/or reduced intrasplenic blood flow, which stimulates SANA. In anesthetized rats, we measured the changes in SANA in response to partial occlusion of either the splenic artery or vein. Splenic venous and arterial pressures and flows were simultaneously monitored. Splenic vein occlusion increased splenic venous pressure (9.5 +/- 0.5 to 22.9 +/- 0.8 mmHg, n = 6), reduced splenic arterial blood flow (1.7 +/- 0.1 to 0.9 +/- 0.1 ml/min, n = 6) and splenic venous blood flow (1.3 +/- 0.1 to 0.6 +/- 0.1 ml/min, n = 6), and increased SANA (1.7 +/- 0.4 to 2.2 +/- 0.5 spikes/s, n = 6). During splenic artery occlusion, we matched the reduction in either splenic arterial blood flow (1.7 +/- 0.1 to 0.7 +/- 0.05, n = 6) or splenic venous blood flow (1.2 +/- 0.1 to 0.5 +/- 0.04, n = 5) with that seen during splenic vein occlusion. In neither case was there any change in either splenic venous pressure (-0.4 +/- 0.9 mmHg, n = 6 and +0.1 +/- 0.3 mmHg, n = 5) or SANA (-0.11 +/- 0.15 spikes/s, n = 6 and -0.05 +/- 0.08 spikes/s, n = 5), respectively. Furthermore, there was a linear relationship between SANA and splenic venous pressure (r = 0.619, P = 0.008, n = 17). There was no such relationship with splenic venous (r = 0.371, P = 0.236, n = 12) or arterial (r = 0.275, P = 0.413, n = 11) blood flow. We conclude that it is splenic venous pressure, not flow, which stimulates splenic afferent nerve activity and activates the splenorenal reflex in portal and splenic venous 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 | 0.001 |
| 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".