Controlling Parasympathetic Regulation of Heart Rate: A Gatekeeper Role for RGS Proteins in the Sinoatrial Node
Bibliographic record
Abstract
Neurotransmitters released from sympathetic and parasympathetic nerve terminals in the sinoatrial node (SAN) exert their effects via G-protein-coupled receptors. Integration of these different G-protein signals within pacemaker cells of the SAN is critical for proper regulation of heart rate and function. For example, excessive parasympathetic signaling can be associated with sinus node dysfunction (SND) and supraventricular arrhythmias. Our previous work has shown that one member of the regulator of G-protein signaling (RGS) protein family, RGS4, is highly and selectively expressed in pacemaker cells of the SAN. Consistent with its role as an inhibitor of parasympathetic signaling, RGS4-knockout mice have reduced basal heart rates and enhanced negative chronotropic responses to parasympathetic agonists. Moreover, RGS4 appears to be an important part of SA nodal myocyte signaling pathways that mediate G-protein-coupled inwardly rectifying potassium channel (GIRK) channel activation/deactivation and desensitization. Since RGS4 acts immediately downstream of M2 muscarinic receptors, it is tempting to speculate that RGS4 functions as a master regulator of parasympathetic signaling upstream of GIRKs, HCNs, and L-type Ca(2+) channels in the SAN. Thus, loss of RGS4 function may lead to increased susceptibility to conditions associated with increased parasympathetic signaling, including bradyarrhythmia, SND, and atrial fibrillation.
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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".