Novel Approach for Simultaneous Recording of Renal Sympathetic Nerve Activity and Blood Pressure with Intravenous Infusion in Conscious, Unrestrained Mice.
Bibliographic record
Abstract
Renal sympathetic nerves contribute significantly to both physiological and pathophysiological phenomena. Evaluating renal sympathetic nerve activity (RSNA) is of great interest in many areas of research such as chronic kidney disease, hypertension, heart failure, diabetes and obesity. Unequivocal assessment of the role of the sympathetic nervous system is thus imperative for proper interpretation of experimental results and understanding of disease processes. RSNA has been traditionally measured in anesthetized rodents, including mice. However, mice usually exhibit very low systemic blood pressure and hemodynamic instability for several hours during anesthesia and surgery. Meaningful interpretation of RSNA is confounded by this non-physiological state, given the intimate relationship between sympathetic nervous tone and cardiovascular status. To address this limitation of traditional approaches, we developed a new method for measuring RSNA in conscious, freely-moving mice. Mice were chronically instrumented with radio-telemeters for continuous monitoring of blood pressure as well as a jugular venous infusion catheter and custom-designed bipolar electrode for direct recording of RSNA. Following a 48-72 hour recovery period, survival rate was 100% and all mice behaved normally. At this time-point, RSNA was successfully recorded in 80% of mice, with viable signals acquired up to 4 and 5 days post-surgery in 70% and 50% of mice, respectively. Physiological blood pressures were recorded in all mice (116±2 mmHg; n=10). Recorded RSNA increased with eating and grooming, as well-established in the literature. Furthermore, RSNA was validated by ganglionic blockade and modulation of blood pressure with pharmacological agents. Herein, an effective and manageable method for clear recording of RSNA in conscious, freely-moving mice is described.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".