Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Antianginal Drugs: Calcium Channel Blockers and Ranolazine01:25

Antianginal Drugs: Calcium Channel Blockers and Ranolazine

1.2K
Angina pectoris, a primary symptom of ischemic heart disease, requires careful pharmacological interventions. In this context, calcium channel blockers (CCBs) and ranolazine have emerged as crucial pharmacotherapeutic agents, providing deep insights into the complexities of angina management.
CCBs, a diverse class that includes dihydropyridines (nifedipine) and diphenylalkylamines (verapamil and diltiazem), exert their effect by blocking calcium channels in cardiac and smooth muscle cells. This...
1.2K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

309
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
309
Antihypertensive Drugs: Action of Calcium Channel Blockers01:18

Antihypertensive Drugs: Action of Calcium Channel Blockers

1.5K
Calcium ions are essential to contract smooth muscle cells in blood vessels. They enter these cells through voltage-dependent calcium channels, specifically L-type calcium channels in the cell membrane. These L-type calcium channels are integral to the excitation-contraction coupling process in smooth muscle. When a stimulus is received by smooth muscle cells, their membrane depolarizes. This alteration in membrane potential instigates the opening of L-type calcium channels. As a result,...
1.5K
Antiarrhythmic Drugs: Class IV Agents as Calcium Channel Blockers01:20

Antiarrhythmic Drugs: Class IV Agents as Calcium Channel Blockers

1.5K
Class IV antiarrhythmic drugs, such as verapamil and diltiazem, block calcium channels. They primarily affect the heart, slowing the conduction in calcium-dependent tissues like the SA and AV nodes. These drugs manage reentrant supraventricular tachycardia (SVT) and reduce ventricular rate in atrial flutter/fibrillation.
Verapamil, a calcium channel blocker, inhibits calcium movement across myocardial cell membranes and vascular smooth muscle. This results in the dilation of coronary and...
1.5K
Heart Failure Drugs: Inotropic Agents01:26

Heart Failure Drugs: Inotropic Agents

1.2K
Positive inotropic agents are commonly used as the first line of treatment for heart failure. One such agent is digoxin, derived from the genus Digitalis, which has been known for centuries but effectively utilized since 1785. However, these cardiac glycosides can have potentially toxic effects due to their mechanism of action, which involves inhibiting Na+/K+-ATPase and increasing contractility. Digoxin is absorbed orally and distributed in various tissues, including the CNS. It has a long...
1.2K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.8K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MYO6 and Heart: A Novel Variant in a Deaf Infant With Supraventricular Tachycardia.

Molecular genetics & genomic medicine·2026
Same author

Diagnostic accuracy of AI-Based models for pulmonary edema detection: A systematic review and Meta-Analysis.

Heart & lung : the journal of critical care·2026
Same author

"Diagnostic Performance of Artificial Intelligence in Evaluating Tricuspid Regurgitation: A Systematic Review and Meta-Analysis".

Clinical cardiology·2026
Same author

Short-term high sodium intake increases nocturnal blood pressure but not arterial stiffness in Black adults.

European journal of nutrition·2026
Same author

MicroRNA expression profiles in abdominal aortic aneurysms: A systematic review of potential diagnostic and prognostic biomarkers.

International journal of cardiology. Cardiovascular risk and prevention·2026
Same author

Association Between Dietary/Supplementary Calcium Intake and Risk of Breast Cancer: A Systematic Review and Meta-Analysis of Cohort Studies.

Nutrition and cancer·2026

Related Experiment Video

Updated: Jan 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Machine Learning-Based Prognostic Prediction Models in Calcium Channel Blockers Poisoning.

Babak Mostafazadeh1, Sayed Masoud Hosseini1, Shahin Shadnia1

  • 1Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Archives of Academic Emergency Medicine
|December 16, 2025
PubMed
Summary

Machine learning models accurately predict outcomes for calcium channel blocker (CCB) poisoning. XGBoost and CatBoost showed superior performance, aiding early risk stratification for CCB poisoning patients.

Keywords:
Calcium channel blockersMachine learningPoisoningPrognosis

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

Related Experiment Videos

Last Updated: Jan 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

Area of Science:

  • Toxicology
  • Medical Informatics
  • Cardiovascular Medicine

Background:

  • Calcium channel blocker (CCB) poisoning presents a significant toxicological emergency with severe cardiovascular complications.
  • Accurate prediction of CCB poisoning outcomes is crucial for timely and effective patient management.

Purpose of the Study:

  • To evaluate the accuracy of various machine learning (ML) models in predicting outcomes of CCB poisoning.
  • To identify key prognostic factors associated with CCB poisoning using ML techniques.

Main Methods:

  • A retrospective cross-sectional study of 274 CCB poisoning cases (2019-2024).
  • Trained ML models (XGBoost, CatBoost, Random Forest, AdaBoost) on clinical and laboratory data.
  • Utilized feature selection to identify 18 prognostic factors and assessed model performance using AUC, accuracy, precision, recall, and F1-score.

Main Results:

  • Feature selection identified 18 key prognostic factors, including temperature, GCS-eye response, ECG findings, and various lab values.
  • XGBoost and CatBoost achieved the highest predictive performance with macro-averaged AUC values of 0.9899 and 0.9983, respectively.
  • These ML models outperformed traditional statistical methods in risk stratification for CCB poisoning.

Conclusions:

  • ML models, particularly XGBoost and CatBoost, demonstrate high accuracy in predicting CCB poisoning outcomes.
  • These models offer a valuable data-driven framework for early risk stratification in clinical settings.
  • Future research should focus on multi-center validation and integration into clinical decision support systems.