AIMC Topic: Cardiotoxicity

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Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.

Circulation. Cardiovascular quality and outcomes Sep 2, 2024
BACKGROUND: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to...
Middle Aged Risk Assessment Humans Antineoplastic Agents Time Factors Artificial Intelligence Male Risk Factors Ventricular Function, Left Aged Stroke Volume Retrospective Studies United States Female Electrocardiography Predictive Value of Tests Ventricular Dysfunction, Left Cardiotoxicity Trastuzumab Anthracyclines
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A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers.

CPT: pharmacometrics & systems pharmacology Aug 26, 2024
This study addresses the critical issue of drug-induced torsades de pointes (TdP) risk assessment, a vital aspect of new drug development due to its association with arrhythmia and sudden cardiac death. Existing methodologies, particularly those reli...
Humans Neural Networks, Computer Torsades de Pointes Risk Assessment Support Vector Machine Cardiotoxicity Machine Learning Biomarkers Computer Simulation
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Artificial intelligence-derived left ventricular strain in echocardiography in patients treated with chemotherapy.

The international journal of cardiovascular imaging Jul 23, 2024
Global longitudinal strain (GLS) is an echocardiographic measure to detect chemotherapy-related cardiovascular dysfunction. However, its limited availability and the needed expertise may restrict its generalization. Artificial intelligence (AI)-based...
Image Interpretation, Computer-Assisted Observer Variation Cardiotoxicity Middle Aged Neoplasms Ventricular Function, Left Antineoplastic Agents Stroke Volume Ventricular Remodeling Echocardiography Ventricular Dysfunction, Left Aged Predictive Value of Tests Humans Aged, 80 and over Heart Ventricles Female Male Reproducibility of Results Artificial Intelligence Retrospective Studies
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Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study.

Expert opinion on drug metabolism & toxicology Jul 10, 2024
BACKGROUND: Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identify...
Humans Machine Learning Algorithms Deep Learning Ether-A-Go-Go Potassium Channels Potassium Channel Blockers Drug Development Quantitative Structure-Activity Relationship Cardiotoxicity
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Two-Dimensional Deep Learning Frameworks for Drug-Induced Cardiotoxicity Detection.

ACS sensors Jun 6, 2024
The identification of drug-induced cardiotoxicity remains a pressing challenge with far-reaching clinical and economic ramifications, often leading to patient harm and resource-intensive drug recalls. Current methodologies, including in vivo and in v...
Cardiotoxicity Induced Pluripotent Stem Cells Neural Networks, Computer Humans Myocytes, Cardiac Deep Learning Fourier Analysis
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CardioDPi: An explainable deep-learning model for identifying cardiotoxic chemicals targeting hERG, Cav1.2, and Nav1.5 channels.

Journal of hazardous materials May 24, 2024
The cardiotoxic effects of various pollutants have been a growing concern in environmental and material science. These effects encompass arrhythmias, myocardial injury, cardiac insufficiency, and pericardial inflammation. Compounds such as organic so...
Cardiotoxins ERG1 Potassium Channel NAV1.5 Voltage-Gated Sodium Channel Deep Learning Calcium Channels, L-Type Cardiotoxicity Humans
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A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Methods (San Diego, Calif.) May 1, 2024
Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate predic...
Drug Discovery Deep Learning Humans Animals Drug-Related Side Effects and Adverse Reactions Cardiotoxicity
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Deep learning-assisted high-content screening identifies isoliquiritigenin as an inhibitor of DNA double-strand breaks for preventing doxorubicin-induced cardiotoxicity.

Biology direct Oct 9, 2023
BACKGROUND: Anthracyclines including doxorubicin are essential components of many cancer chemotherapy regimens, but their cardiotoxicity severely limits their use. New strategies for treating anthracycline-induced cardiotoxicity (AIC) are still neede...
Deep Learning Cardiotoxicity Doxorubicin Artificial Intelligence DNA Anthracyclines Antibiotics, Antineoplastic Animals
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Use of Deep-Learning Assisted Assessment of Cardiac Parameters in Zebrafish to Discover Cyanidin Chloride as a Novel Keap1 Inhibitor Against Doxorubicin-Induced Cardiotoxicity.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Sep 7, 2023
Doxorubicin-induced cardiomyopathy (DIC) brings tough clinical challenges as well as continued demand in developing agents for adjuvant cardioprotective therapies. Here, a zebrafish phenotypic screening with deep-learning assisted multiplex cardiac f...
Doxorubicin NF-E2-Related Factor 2 Cardiotoxicity Deep Learning Kelch-Like ECH-Associated Protein 1 Animals Artificial Intelligence Oxidative Stress Molecular Docking Simulation Zebrafish
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Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing.

Journal of tissue engineering and regenerative medicine May 27, 2022
Utilizing recent advances in human induced pluripotent stem cell (hiPSC) technology, nonlinear analysis and machine learning we can create novel tools to evaluate drug-induced cardiotoxicity on human cardiomyocytes. With cardiovascular disease remain...
Inhibitory Concentration 50 Myocytes, Cardiac Cardiotoxicity Algorithms Humans Induced Pluripotent Stem Cells Artificial Intelligence Pharmaceutical Preparations Machine Learning Nonlinear Dynamics
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