Guanylyl cyclase / atrial natriuretic peptide receptor-A: role in the pathophysiology of cardiovascular regulation
Bibliographic record
Abstract
Atrial natriuretic factor (ANF), also known as atrial natriuretic peptide (ANP), is an endogenous and potent hypotensive hormone that elicits natriuretic, diuretic, vasorelaxant, and anti-proliferative effects, which are important in the control of blood pressure and cardiovascular events. One principal locus involved in the regulatory action of ANP and brain natriuretic peptide (BNP) is guanylyl cyclase / natriuretic peptide receptor-A (GC-A/NPRA). Studies on ANP, BNP, and their receptor, GC-A/NPRA, have greatly increased our knowledge of the control of hypertension and cardiovascular disorders. Cellular, biochemical, and molecular studies have helped to delineate the receptor function and signaling mechanisms of NPRA. Gene-targeted and transgenic mouse models have advanced our understanding of the importance of ANP, BNP, and GC-A/NPRA in disease states at the molecular level. Importantly, ANP and BNP are used as critical markers of cardiac events; however, their therapeutic potentials for the diagnosis and treatment of hypertension, heart failure, and stroke have just begun to be realized. We are now just at the initial stage of molecular therapeutics and pharmacogenomic advancement of the natriuretic peptides. More investigations should be undertaken and ongoing ones be extended in this important field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.003 |
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".