Jove
Visualize
Contact Us

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

236
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
236
Substance Use Disorders Affecting Sleep01:24

Substance Use Disorders Affecting Sleep

369
Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
Understanding the concepts of physical dependence,...
369
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

297
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
297

You might also read

Related Articles

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

Sort by
Same author

Host and Environmental Drivers of Gut Microbiome Variation in Wild Anolis Lizards.

Molecular ecology·2026
Same author

Early Screening of Sleep-Disordered Breathing Using a Smartphone-Based Portable System in Stroke Patients and Its Relevance for Rehabilitation: A Prospective Observational Study.

Sensors (Basel, Switzerland)·2026
Same author

Postmortem Associations Between Alzheimer Disease Pathology and Plasma pTau217, GFAP, and NfL in AD and AD-Related Dementias.

Neurology·2026
Same author

Comparison meta-analysis of two swept-source optical coherence biometers.

Indian journal of ophthalmology·2025
Same author

Virtual Reality-Based Neurorehabilitation Support Tool for People With Cognitive Impairments Resulting From an Acquired Brain Injury: Usability and Feasibility Study.

JMIR neurotechnology·2025
Same author

In-lab Evaluation of New Tattoo Skin Electrodes for Measuring Masticatory Electromyographic Activity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles
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 Experiment Video

Updated: Jan 9, 2026

Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

8.1K

Multivariate Model-based Tool for Predicting Cardiovascular Outcomes Using Multimodal Physiological Sleep Data.

Daniel Romero, Raimon Jane

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Obstructive sleep apnea (OSA) patients may have better cardiovascular disease (CVD) risk prediction by using multiple sleep metrics beyond the Apnea-Hypopnea Index (AHI). Novel biomarkers from polysomnography improve CVD risk stratification.

    More Related Videos

    Author Spotlight: IntelliSleepScorer &#8212; A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
    04:54

    Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

    Published on: November 8, 2024

    948
    Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
    05:51

    Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

    Published on: February 21, 2025

    1.2K

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    Multi-Modal Home Sleep Monitoring in Older Adults
    07:40

    Multi-Modal Home Sleep Monitoring in Older Adults

    Published on: January 26, 2019

    8.1K
    Author Spotlight: IntelliSleepScorer &#8212; A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
    04:54

    Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

    Published on: November 8, 2024

    948
    Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
    05:51

    Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

    Published on: February 21, 2025

    1.2K

    Area of Science:

    • Sleep Medicine
    • Cardiology
    • Biomarker Discovery

    Background:

    • Obstructive sleep apnea (OSA) is linked to cardiovascular disease (CVD), but the Apnea-Hypopnea Index (AHI) may not fully reflect long-term CVD risk.
    • Current risk assessment for OSA patients may benefit from incorporating more detailed physiological sleep data.

    Purpose of the Study:

    • To identify novel sleep-related physiological predictors of cardiovascular disease (CVD) outcomes in obstructive sleep apnea (OSA) patients.
    • To develop a more accurate CVD risk stratification model beyond the standard Apnea-Hypopnea Index (AHI).

    Main Methods:

    • Utilized a multimodal database including polysomnography (PSG) metrics, clinical data, and risk factors with an average 10-year follow-up.
    • Employed a two-step feature selection process: initial irrelevant feature removal followed by LASSO for informative feature selection.
    • A wrapper-based method selected the final model, validated using repeated holdout, achieving 75.3% accuracy and 78.0% AUC with 10 features.

    Main Results:

    • Identified key predictors beyond traditional risk factors (sex, age, BMI, diabetes).
    • Significant novel biomarkers include nocturnal average SpO2 (total and NREM), heart rate during apneic episodes with SpO2 drops, blood pressure, and central apnea frequency during REM/NREM sleep.
    • The model demonstrated robust performance in predicting CVD outcomes.

    Conclusions:

    • Integrating multimodal sleep physiological metrics enhances cardiovascular risk stratification in OSA patients compared to AHI alone.
    • These findings enable more precise clinical interventions for OSA patients at risk of CVD.
    • Novel biomarkers identified offer potential for improved diagnostic and prognostic tools.