β-Adrenoceptors as Molecular Targets in the Treatment of Hypertension
Bibliographic record
Abstract
Regulation of sympathoadrenal activity has been a long-time target in the management of hypertension. Regulation of β-adrenoceptor (βAR) function has been the most therapeutically important of these targets. The development of effective antihypertensive treatments based on βAR antagonism paralleled the elucidation of the molecular basis of β-adrenergic effects by the family of βARs, which are members of the G-protein-coupled receptor (GPCR) superfamily. βARs serve as the extracellular face of the transmembrane signalling pathway that results in the consequent activation of heterotrimeric G-proteins and the activation of several other newly appreciated signalling molecules that include β-arrestins and GPCR kinases (GRKs). The aggregate effect of the activation of these signalling pathways mediates the response to βAR activation. Paradoxically, the hypertensive state is characterized by impaired βAR responsiveness. This defect is common to many other receptor systems linked to the stimulator G protein (Gs) and adenylyl cyclase activation. This impairment is principally mediated by receptor-G-protein uncoupling, which has been linked to increased expression and activity of GRK2.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".