Hypertension Suppression, Not a Cumulative Thrust of Quantitative Trait Loci, Predisposes Blood Pressure Homeostasis to Normotension
Bibliographic record
Abstract
BACKGROUND: Genetics of high blood pressure (BP) has revealed causes of hypertension. The cause of normotension, however, is poorly understood. Inbred Lewis rats sustain normotension despite a genetic push in altering BP. It was unknown whether this rigid resistance to BP changes is because of an insufficient hypertensive impact from limited alleles of quantitative trait loci (QTLs) or because of an existence of a master control superseding the combined strength of hypertensive QTL alleles. METHODS AND RESULTS: Currently, BP-elevating QTL alleles from hypertensive Dahl salt-sensitive rats (DSS) replaced those of Lewis on chromosomes 7, 8, 10, and 17 on the Lewis background. These hypertensive QTL alleles were then merged to systematically achieve multiple combinations. Results showed that there was no quantitative correlation between BP variations and the number of hypertensive QTL alleles, and that BP was only slightly elevated from a combined force of normotensive alleles from 7 QTLs. Thus, a genetic factor aside from the known QTLs seemed to be at play in preserving normotension and act as a hypertension suppressor. A follow-up study using consecutive backcrosses from Dahl salt-sensitive rats and Lewis identified a chromosome segment where a hypertension suppressor might reside. CONCLUSIONS: Our results provide the first evidence that normotension is not enacted via a numeric advantage of BP-lowering QTL alleles, and instead can be achieved by a particular genetic component actively suppressing hypertensive QTL alleles. The identification of this hypertension suppressor could result in formulating unique diagnostic and therapeutic targets, and above all, preventive measures against essential 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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".