Signal-averaged resting sympathetic transduction of blood pressure: is it time to account for prevailing muscle sympathetic burst frequency?
Bibliographic record
Abstract
Calculating the blood pressure (BP) response to a burst of muscle sympathetic nerve activity (MSNA), termed sympathetic transduction, may be influenced by an individual’s resting burst frequency. We examined the relationships between sympathetic transduction and MSNA in 107 healthy males and females and developed a normalized sympathetic transduction metric to incorporate resting MSNA. Burst-triggered signal averaging was used to calculate the peak diastolic BP response following each MSNA burst (sympathetic transduction of BP) and following incorporation of MSNA burst cluster patterns and amplitudes (sympathetic transduction slope). MSNA burst frequency was negatively correlated with sympathetic transduction of BP ( r = −0.42; P < 0.01) and the sympathetic transduction slope ( r = −0.66; P < 0.01), independent of sex. MSNA burst amplitude was unrelated to sympathetic transduction of BP in males ( r = 0.04; P = 0.78), but positively correlated in females ( r = 0.44; P < 0.01) and with the sympathetic transduction slope in all participants ( r = 0.42; P < 0.01). To control for MSNA, the linear regression slope of the log-log relationship between sympathetic transduction and MSNA burst frequency was used as a correction exponent. In subanalysis of males (38 ± 10 vs. 14 ± 4 bursts/min) and females (28 ± 5 vs. 12 ± 4 bursts/min) with high versus low MSNA, sympathetic transduction of BP and sympathetic transduction slope were lower in participants with high MSNA (all P < 0.05). In contrast, normalized sympathetic transduction of BP and normalized sympathetic transduction slope were similar in males and females with high versus low MSNA (all P > 0.22). We propose that incorporating MSNA burst frequency into the calculation of sympathetic transduction will allow comparisons between participants with varying levels of resting MSNA.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".