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

Lipid-Lowering Drugs: Statins and Miscellaneous Agents01:20

Lipid-Lowering Drugs: Statins and Miscellaneous Agents

659
Hyperlipidemia, a medical condition often referred to as high cholesterol, is characterized by abnormally elevated levels of lipids in the bloodstream. When present in excess, these lipids, specifically cholesterol and triglycerides, can lead to serious health complications, often involving cardiovascular diseases. Illnesses like atherosclerosis, heart attacks, and pancreatitis have all been linked to untreated hyperlipidemia. This means controlling and regulating cholesterol and triglyceride...
659
Antihypertensive Drugs: Angiotensin-Converting Enzyme Inhibitors01:30

Antihypertensive Drugs: Angiotensin-Converting Enzyme Inhibitors

627
Angiotensin-converting enzyme (ACE), a vital component of the renin-angiotensin-aldosterone system, is abundant in lung endothelial cells. ACE converts the inactive decapeptide, angiotensin I, into the active octapeptide, angiotensin II. This potent vasoconstrictor narrows blood vessels, increasing resistance to blood flow and elevating blood pressure. Angiotensin II also stimulates aldosterone production, encouraging kidney cells to reabsorb more sodium and water from urine, thereby increasing...
627
Heart Failure Drugs: β-Blockers01:22

Heart Failure Drugs: β-Blockers

337
β-adrenergic antagonists, commonly known as β-blockers, block the effects of sympathetic neurotransmitters such as noradrenaline (NA) and adrenaline (ADR). They have several beneficial effects in heart failure treatment. They reduce heart rate, the force of contraction, and cardiac muscle relaxation. They also slow the atrial-ventricular conduction rate and raise the threshold for arrhythmias. The concentration of β-blockers determines their effects on bronchodilation,...
337
Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

2.0K
When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
2.0K
Antihypertensive Drugs: Angiotensin II Receptor Blockers01:30

Antihypertensive Drugs: Angiotensin II Receptor Blockers

720
In the renin-angiotensin-aldosterone system, a hormone called angiotensin II plays a crucial role. It binds to the AT1 receptors in vascular smooth muscles coupled with Gq proteins. The activation of these receptors activates an enzyme called phospholipase C, which releases two molecules: inositol trisphosphate and diacylglycerol. These molecules cause a chain reaction that leads to the phosphorylation of myosin light chains and promotes interaction between actin and myosin, leading to smooth...
720
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

126
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
126

You might also read

Related Articles

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

Sort by
Same author

Lipid Profile Testing and Interpretation.

Circulation·2026
Same author

Low-Density Lipoprotein Cholesterol Lowering With Inclisiran Plus Usual Care in Recent Acute Coronary Syndrome: VICTORION-INCEPTION, a Randomized, Controlled, Open-Label Trial.

Journal of the American Heart Association·2026
Same author

Association Between Discrimination in Health Care and ASCVD Among Middle-Aged and Older Adults in the United States.

Circulation. Population health and outcomes·2026
Same author

Targeted use of large language models for EHR-based computable phenotyping.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Stimulant Toxicity as Acute-on-Chronic Cardiovascular Death: How Cardiovascular Care Needs to Adapt.

Circulation. Population health and outcomes·2026
Same author

Variation in Lipid Management for Atherosclerotic Cardiovascular Disease Across US Health Systems.

Circulation. Population health and outcomes·2026

Related Experiment Video

Updated: Jun 29, 2025

Differential Effects of Lipid-lowering Drugs in Modulating Morphology of Cholesterol Particles
09:15

Differential Effects of Lipid-lowering Drugs in Modulating Morphology of Cholesterol Particles

Published on: November 10, 2017

14.6K

Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022.

Samuel D Slavin1,2, Adam N Berman1, Andrew L Beam2

  • 1Brigham and Women's Hospital Boston MA USA.

Journal of the American Heart Association
|March 27, 2024
PubMed
Summary

Human-generated statin content on Twitter has increased, surpassing bots. Both human and bot accounts show rising negative sentiment and statin skepticism, highlighting Twitter

Keywords:
cardiovascular preventionmisinformationsocial mediastatins

More Related Videos

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.2K
LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring
08:45

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

Published on: November 17, 2018

13.3K

Related Experiment Videos

Last Updated: Jun 29, 2025

Differential Effects of Lipid-lowering Drugs in Modulating Morphology of Cholesterol Particles
09:15

Differential Effects of Lipid-lowering Drugs in Modulating Morphology of Cholesterol Particles

Published on: November 10, 2017

14.6K
Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.2K
LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring
08:45

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

Published on: November 17, 2018

13.3K

Area of Science:

  • Cardiovascular Health
  • Social Media Analysis
  • Medical Misinformation

Background:

  • Many eligible patients decline statin therapy due to adverse effect fears and misinformation.
  • The prevalence of statin misinformation on social media platforms like Twitter (now X) is largely unknown.
  • The role of social media bots in spreading medical misinformation, specifically regarding statins, requires investigation.

Purpose of the Study:

  • To examine temporal trends in the volume of statin-related Twitter posts (tweets).
  • To differentiate between bot-generated and human-generated statin-related content.
  • To analyze the sentiment and prevalence of statin skepticism over time.

Main Methods:

  • Analysis of 1,155,735 original statin-related tweets from 2010-2022.
  • Utilized a machine learning classifier to determine bot probability for each account.
  • Employed natural language processing for sentiment analysis and manual coding for statin skepticism.

Main Results:

  • Human-generated tweets increased from 43.6% to 79.8%, while bot-generated tweets decreased from 47.8% to 11.3%.
  • Negative sentiment rose in both bot (27.8% to 43.4%) and human (30.9% to 38.4%) tweets.
  • Statin skepticism increased significantly in human tweets (26.0% to 40.0%) and bot tweets (8.0% to 19.0%).

Conclusions:

  • Humans now dominate statin-related content creation on Twitter, surpassing bots.
  • There is a concerning increase in negative sentiment and statin skepticism across all user types.
  • Twitter presents a critical platform for addressing and combating statin misinformation.