Bibliographic record
Abstract
Abstract Essential hypertension is one of the most damaging health risk factors; however, its underlying mechanisms of pathogeneses continue to be undeciphered. To assist this endeavour, investigations utilising human populations and rodent models have revealed numerous genetic building blocks known as quantitative trait loci (QTLs) for blood pressure (BP). Although BP is a quantitatively measured trait manifesting in a continuous variation in heterogeneous populations, each QTL governing it appears to functionally behave as an independent and ‘monogenic’ Mendelian determinant. Mechanistically, multiple QTLs are functionally modularised by epistasis that implies a common pathway or cascade among them, whereas others belong to parallel epistatic modules/pathways. These insights suggest that similar genetic mechanisms probably shepherd the genetic architectures in physiological functions for essential hypertension. Translations of gene discovery to therapeutic targets and diagnostic tools will require biology‐based function validations of specific genes constituting BP QTLs in appropriate animal models. Key Concepts There is a regulatory hierarchy in the genetic architecture governing the functional biology of BP determination. When a master control is removed, modularity and epistatic hierarchy, not a cumulation of minuscule effects, determine the functionality of multiple QTLs on BP. Mechanistically, separate epistatic modules integrating BP QTLs are best explained by independent pathways, each consisting of multiple components that act in sequential cascades in a pathway. As there are fewer pathways than the components comprising them, a defective protein product per se encoded by a mutated QTL, either qualitatively or quantitatively, can lead to a deficient BP‐regulating pathway of pathogenesis and does not have to directly act on BP as a physiological agent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".