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Record W4389483919 · doi:10.37349/emed.2023.00185

Involvement of protein kinases associated signal transduction mechanisms in cardiac diseases

2023· article· en· W4389483919 on OpenAlexaff
J.K. Prasad, Anureet K. Shah, Naranjan S. Dhalla

Bibliographic record

VenueExploration of Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsKinaseProtein kinase AProtein kinase CMitogen-activated protein kinaseMedicineASK1DephosphorylationProtein phosphorylationPhosphatasePhosphorylationBiologyCancer researchCell biologyCyclin-dependent kinase 2

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.276
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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