The Genetic Blueprint of Cardiovascular Therapy: Pharmacogenomics for Improved Efficacy and Safety DOI Creative Commons
Nikhilesh Andhi,

Bhuvana Darawadi

Journal of Indian College of Cardiology, Год журнала: 2024, Номер 14(3), С. 79 - 87

Опубликована: Июль 1, 2024

Globally, cardio vascular diseases (CVD) remain the primary cause of morbidity and mortality. Pharmacogenomics (PGxs) has profoundly changed how various drug classes are managed in CVDs. For example, genetic polymorphisms genes such as SLCO1B1 impact a person responds to statins rosuvastatin atorvastatin, where interindividual variability reaction (Fluvastatin)used lipid-lowering therapy can be partly explained by variations encoding drug-metabolizing enzymes cytochrome P450 transporters like OATP1B1. Similarly, antiplatelet therapy, CYP2C19 affect clopidogrel metabolism, influencing its efficacy preventing thrombotic events. Genes CYP2C9 VKORC1 crucial for metabolism response acenocoumarol warfarin during anticoagulant monitoring bleeding risk. Genetic CYP2D6 effectiveness propafenone metoprolol. Understanding PGx presumptions these cardiovascular drugs may help develop personalized treatment strategies that lower possibility adverse reactions, obtain desired therapeutic outcomes, improve patient compliance safety with respect each patient’s unique makeup.

Язык: Английский

Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes DOI
Hirak Mazumdar, Kamil Reza Khondakar, Suparna Das

и другие.

Expert Opinion on Drug Delivery, Год журнала: 2024, Номер unknown

Опубликована: Дек. 8, 2024

Applying artificial intelligence (AI) to nanomedicine has greatly increased the production of specially engineered nanoscale materials for tailored medicine, marking a significant advancement in healthcare. With use AI, researchers can search through massive databases and find nano-properties that support range therapeutic objectives, eventually producing safer, customized nanomaterials. AI analyzes patient data, including clinical genetic information, predict results individualized care makes recommendations therapy improvement. Furthermore, logically creates nanocarriers give precise controlled drug release patterns optimize advantages minimize undesirable side effects. Even though lot potential nanomedicine, there are still issues data integration techniques, moral dilemmas, requirement governmental backing. Future developments tools multidisciplinary cooperation between scientists with expertise biological sciences nanoengineering essential nanomedicine. Together, these disciplines propel advancements precision contributing ultimate objective—a future which combine provide really The authors this editorial encourage call on scientists, physicians, legislators acknowledge its transform treatment.

Язык: Английский

Процитировано

9
Machine learning-based myocardial infarction bibliometric analysis DOI Creative Commons
Ying Fang, Yang Wu, Lijuan Gao

и другие.

Frontiers in Medicine, Год журнала: 2025, Номер 12

Опубликована: Фев. 6, 2025

This study analyzed the research trends in machine learning (ML) pertaining to myocardial infarction (MI) from 2008 2024, aiming identify emerging and hotspots field, providing insights into future directions of development ML for MI. Additionally, it compared contributions various countries, authors, agencies field focused on A total 1,036 publications were collected Web Science Core Collection database. CiteSpace 6.3.R1, Bibliometrix, VOSviewer utilized analyze bibliometric characteristics, determining number publications, institutions, keywords, cited documents, journals popular scientific fields. was used temporal trend analysis, Bibliometrix quantitative country institutional visualization collaboration networks. Since emergence literature medical imaging 2008, interest this has grown rapidly, particularly since pivotal moment 2016. The MI domains, represented by China United States, have experienced swift after 2015, albeit with States significantly outperforming quality (as evidenced higher impact factors citation counts States). Institutional collaborations formed, notably between Harvard Medical School Capital University China, highlighting need enhanced cooperation among domestic international institutions. In realm research, cooperative teams led figures such as Dey, Damini, Berman, Daniel S. emerged, indicating that Chinese scholars should strengthen their focus both qualitative development. overall direction toward Medicine, Sciences, Molecular Biology, Genetics. particular, "Circulation" "Computers Biology Medicine" hold prominent positions study. paper presents a comprehensive exploration hotspots, trends, over past two decades. analysis reveals deep is an MI, neural networks playing crucial role early diagnosis, risk assessment, rehabilitation therapy.

Язык: Английский

Процитировано

1
Can artificial intelligence lower the global sudden cardiac death rate? A narrative review DOI

Raja Savanth Reddy Chityala,

Sandhya Bishwakarma,

Kanval Shah

и другие.

Journal of Electrocardiology, Год журнала: 2025, Номер 89, С. 153882 - 153882

Опубликована: Янв. 22, 2025

Язык: Английский

Процитировано

0
Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS) DOI Creative Commons
Qing Huang,

Zi-Hao Jiang,

Bo Shi

и другие.

BMC Public Health, Год журнала: 2025, Номер 25(1)

Опубликована: Фев. 7, 2025

Due to the ageing population and evolving lifestyles occurring in China, middle-aged elderly populations have become high-risk groups for cardiovascular disease (CVD). The aim of this study was analyse incidence characteristics CVD these develop a prediction model by using data from China Health Retirement Longitudinal Study (CHARLS). We used follow-up CHARLS Chinese over time span 9 years. Five machine learning (ML) algorithms were employed risk prediction. Data preprocessing included missing value imputation via random forest. Feature selection performed Least Absolute Shrinkage Selection Operator (Lasso CV) method with cross-validation prior training. application synthetic minority over-sampling technique (SMOTE) address class imbalance. Model performance evaluated analyses including area under ROC curve (AUC), precision, recall, F1 score, SHAP plots interpretability. In accordance exclusion criteria, 12,580, 12,061, 11,545, 11,619 participants enrolled four rounds. cumulative (CI) at 2, 4, 7, years 2.846%, 8.971%, 17.869% 20.518%,, respectively. Significant differences observed across gender, age, ethnicity, region, higher rates females northeast region. Ultimately, 8,080 24 features analysed ML models built based on features. Although LGB achieves an AUC 0.818, indicating strong overall performance, its score recall rate are relatively low, 0.509 43.1%, Shapley additive explanations (SHAP) revealed importance key features, such as night sleep duration, TG levels, waist circumference, predicting outcomes, highlighted nonlinear relationships between risk. Gender, region significant factors influencing incidence. demonstrates good low reveal limitations identifying patients.

Язык: Английский

Процитировано

0
The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease DOI Creative Commons
Mohammed Andaleeb Chowdhury, Rodrigue Rizk, J. Christine Chiu

и другие.

Biomedicines, Год журнала: 2025, Номер 13(2), С. 427 - 427

Опубликована: Фев. 10, 2025

The application of artificial intelligence (AI) and machine learning (ML) in medicine healthcare has been extensively explored across various areas. AI ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, prediction, workflow optimization, resource utilization. This review summarizes current advancements concerning disease, including their clinical investigation use primary cardiac imaging techniques, common categories, research, patient care, outcome prediction. We analyze discuss commonly used models, algorithms, methodologies, highlighting roles improving outcomes while addressing limitations future applications. Furthermore, this emphasizes the transformative potential practice decision making, reducing human error, monitoring support, creating more efficient workflows for complex conditions.

Язык: Английский

Процитировано

0
From algorithms to clinical outcomes: how artificial intelligence shapes metaclinical medicine DOI Creative Commons
Panos Vardas, Charalambos Vlachopoulos

Hellenic Journal of Cardiology, Год журнала: 2025, Номер 81, С. 1 - 3

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study DOI Open Access

Koushik Rao Gadhachanda,

Mohammed Dheyaa Marsool Marsool, Ali Bozorgi

и другие.

Annals of Medicine and Surgery, Год журнала: 2025, Номер 87(4), С. 2187 - 2203

Опубликована: Фев. 27, 2025

Background: The integration of artificial intelligence (AI) into cardiovascular procedures has significantly advanced diagnostic accuracy, outcome prediction, and robotic-assisted surgeries. However, a comprehensive bibliometric analysis AI’s impact in this field is lacking. This study examines research trends, key contributors, emerging themes AI-driven interventions. Methods: We retrieved relevant publications from the Web Science Core Collection analyzed them using VOSviewer, CiteSpace, Biblioshiny to map trends collaborations. Results: AI-related grown substantially 1993 2024, with sharp increase 2020 2023, peaking at 93 2023. USA (127 papers), China (79), England (31) were top Harvard University leading institutional output (17 papers). Frontiers Cardiovascular Medicine was most prolific journal. included “machine learning,” “mortality,” “cardiac surgery,” “association,” “implantation,” “aortic stenosis,” underscoring expanding role predictive modeling surgical outcomes. Conclusion: AI demonstrates transformative potential procedures, particularly imaging, modeling, patient management. highlights growing interest applications provides framework for integrating clinical workflows enhance treatment strategies,

Язык: Английский

Процитировано

0
Combating cardiovascular disease disparities: the potential role of artificial intelligence DOI Creative Commons

Chisom J Orakwue,

Farbod Zahedi Tajrishi,

Constance M Gistand

и другие.

American Journal of Preventive Cardiology, Год журнала: 2025, Номер 22, С. 100954 - 100954

Опубликована: Март 9, 2025

Процитировано

0
Integration of Bioinformatic Tools in Functional Analysis of Genes and Their Application in Disease Diagnosis DOI
Jaspreet Kaur, Simran Jit, Mansi Verma

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Diabetes-induced edothelial dysfuction: Molecular pathways and clinical implication DOI
Mohd Basheeruddin,

Sana Qausain,

Arvind Kumar Kushwaha

и другие.

Multidisciplinary Reviews, Год журнала: 2025, Номер 8(7), С. 2025232 - 2025232

Опубликована: Фев. 14, 2025

Endothelial dysfunction caused by diabetic conditions is one of the most pivotal factors in formation various CAD. This review will explain cellular changes endothelial cells diabetes mellitus especially hyperglycemia induced damage oxidative stress inflammation and defects eNOS enzyme. High glucose stimulates biomechanisms such as ROS formation, polyol PKC activation, AGE increased hexosamine that are all instrumental damage. These mechanisms acting concert with another disrupt normally balanced function contributing to reduction bioavailability nitric oxide (NO), permeability endothelium pro-inflammatory pro-thrombotic states. Chronic inflammations exacerbate because sustained release production apoptotic signals cells. Furthermore, also consider’s roles microRNAs epigenomics managing nations. a clinical perspective leading factor atherosclerosis, hypertension well other vascular complications affect patients. Therapeutic approaches regard dysfunction: non-pharmacological interventions, drug interventions (statins; ACE inhibitors; SGLT2 GLP-1 receptor agonists, etc.). From this review, it can be concluded screening for more particularly tackling crucial during early stages minimize cardiovascular risks translate into better patient outcomes. It have comprehension these molecular cascades advance novel treatment consistent preservation integrity its comparatively worse complications.

Язык: Английский

Процитировано

0