Editorial: Reviews in cardiovascular pharmacology: 2023
Bibliographic record
Abstract
Cardiovascular diseases remains a leading cause of death globally, accounting for approximately 17.9 million fatalities in 2019 (WHO, 2021). Cardiovascular diseases include hypertension, atherosclerosis, ischemic heart disease, stroke and heart failure, and imposes a significant socioeconomic burden. The Research Topic "Reviews in Cardiovascular Pharmacology: 2023" provides an overview of both the pathogenesis and pharmacological advances in cardiovascular diseases. This collection features twelve articles, each offering in-depth discussions on recent findings on the mechanisms underlying cardiovascular diseases, as well as the development and application of novel cardiovascular therapies. The articles cover a wide range of topics, including recent advancements in clinical trials, emerging concepts on drug mechanisms, therapeutic strategies, and challenges related to pharmacokinetics.Nitric oxide (NO) is a highly reactive gaseous molecule released by endothelial cells in blood vessels, playing a crucial role in mediating protective cardiovascular effects, including vasodilation (Siti et al., 2019). Impaired NO function often occurs before the clinical onset of cardiovascular disease (). This endogenous vasodilator binds to soluble guanylate cyclase (sGC), stimulating the synthesis of cyclic guanosine monophosphate (cGMP). Elevated cGMP activates protein kinase G (PKG), which lowers intracellular calcium levels in vascular smooth muscle cells, inducing vasodilation (Mishra et al., 2025). Yin et al. review the progress of guanylate cyclase activators to stimulate the NO-sGC-cGMP signaling pathway in patients with cardiovascular disease. These include riociguat,vericiguat,praliciguat,olinciguat,cinaciguat,ataciguat,runcaciguat,mosliciguat,and BI 685509,the The authors declare that the editorial was written in the absence of any commercial or financial 117 relationships that could be construed as a potential conflict of interest. 118
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.049 |
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".