Involvement of protein kinases associated signal transduction mechanisms in cardiac diseases
Bibliographic record
Abstract
Protein kinases, a family of enzymes responsible for regulating various cellular processes, have been implicated in the development and progression of various heart diseases, making them attractive therapeutic targets. This review focuses on the role of protein kinases induced phosphorylation and protein phosphatase-induced dephosphorylation in cardiovascular disorders, including heart failure, ischemic heart disease, arrhythmias, hypertension, and diabetic cardiomyopathy. This paper explores the potential of novel kinase-targeted therapies and emerging technologies for the prevention and treatment of these conditions. It also discusses the involvement of protein kinase A (PKA), protein kinase C (PKC), phosphoinositide 3-kinases (PI3Ks), mitogen-activated protein kinases (MAPKs), and Ca2+/calmodulin-dependent protein kinase II (CaMKII) in heart dysfunction and alterations in their function that contribute to their respective cardiac disorders. Furthermore, this article presents a comprehensive overview of protein kinases in cardiac disorders and the potential of innovative kinase-targeted therapies, advanced technologies, and multidisciplinary approaches for the effective prevention and treatment of cardiovascular diseases, ultimately aiming to improve patient outcomes and quality of life.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".