Sympathetic Nervous System, Genes and Human Essential Hypertension
Bibliographic record
Abstract
The sympathetic nervous system (SNS) is the first line of defense in the response to environmental stress through its regulation of second-to-second changes in blood pressure (BP). Both the activity of the SNS and the therapeutic responses to SNS agonists and antagonists are known to be highly variable in the population. "Small" changes caused by single nucleotide polymorphisms (SNPs) of SNS genes may have considerable impact on SNS function and individualized hypertension treatment. In this review, we first describe the physiology of the SNS and its influence on cardiovascular and renal mechanisms of BP regulation. A thorough review of the role of genetic variability of various SNS genes in relation to the development of BP and essential hypertension (EH) follows. Given the vast number of SNS components, evaluations of multiple SNPs from multiple SNS genes are necessary for future association studies of BP and EH. One way to surpass the limitations and inconsistencies of previous association studies is to use a gene-based approach also referred to as indirect association, which takes all common variation within a candidate gene into account. In order to determine how SNS genes are differentially expressed or silenced, activated or inactivated against various environmental backgrounds, it is important to assess not only environmental and lifestyle risk factors such as diet, climate, chronic stress, but also personality characteristics such as hostility and coping styles. Uncovering relevant gene-gene and gene-environment interactions within the SNS cascade will not only enable early detection of EH risk but will also aid in the treatment of hypertensives through both non-pharmacological and pharmacological means.
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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 | 0.001 |
| 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".