Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Glucose Homeostasis: Regulation of Blood Glucose

Glucose Homeostasis: Regulation of Blood Glucose

Carbohydrates consumed through foods are converted into glucose, a crucial energy source for the body. In the prandial state, high blood glucose levels stimulate the secretion of insulin from the pancreas. Insulin inhibits hepatic glucose production and stimulates glucose uptake and metabolism by muscle and adipose tissue. The excess glucose is converted into glycogen and stored in the liver and muscles.
During fasting, when blood glucose levels are low, the pancreas secretes glucagon. it...

You might also read

Related Articles

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

Sort by

Same author

Accuracy of Continuous Glucose Monitoring in Adults With Postbariatric Hypoglycemia After Roux-en-Y Gastric Bypass Surgery Under Stable and Dynamic Glucose Conditions.

Diabetes care·2026

Same author

Clinical Efficacy Evaluation of a Continuous Glucose Monitoring-Guided Forecasting Algorithm to Mitigate Postbariatric Hypoglycemia: A Randomized Controlled Trial.

Diabetes technology & therapeutics·2026

Same author

Research Code Sharing in Support of Gold Standard Science.

Journal of diabetes science and technology·2026

Same author

Systematic Review on Deep Learning Algorithms for Blood Glucose Forecasting in Type 1 Diabetes.

IEEE journal of biomedical and health informatics·2026

Same author

Development of Insulin Bolus Calculators in Type 1 Diabetes using A Framework Based on Real-world Data, Digital Twins and Machine Learning.

IEEE transactions on bio-medical engineering·2025

Same author

Evaluating the Effect of Input Features on Deep Learning Models for Blood Glucose Forecasting.

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

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

IEEE transactions on bio-medical engineering·2026

Same journal

Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

IEEE transactions on bio-medical engineering·2026

Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026

Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026

Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026

Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 7, 2026

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Published on: August 3, 2021

Modeling the glucose sensor error.

Andrea Facchinetti, Simone Del Favero, Giovanni Sparacino

IEEE Transactions on Bio-Medical Engineering

|October 11, 2013

Summary

This summary is machine-generated.

This study models continuous glucose monitoring (CGM) sensor errors by analyzing multiple simultaneous CGM recordings. The approach effectively separates physiological and technological factors contributing to CGM inaccuracies.

More Related Videos

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

Published on: June 1, 2012

Quantitative and Temporal Control of Oxygen Microenvironment at the Single Islet Level

Quantitative and Temporal Control of Oxygen Microenvironment at the Single Islet Level

Published on: November 17, 2013

Related Experiment Videos

Last Updated: May 7, 2026

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Published on: August 3, 2021

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

Published on: June 1, 2012

Quantitative and Temporal Control of Oxygen Microenvironment at the Single Islet Level

Quantitative and Temporal Control of Oxygen Microenvironment at the Single Islet Level

Published on: November 17, 2013

Area of Science:

Biomedical Engineering
Medical Device Technology
Diabetes Technology

Background:

Continuous glucose monitoring (CGM) offers near real-time glucose data for diabetes management.
CGM accuracy is limited by diffusion, calibration drifts, and measurement noise compared to blood glucose (BG) meters.
Accurate modeling of CGM sensor error is crucial for improving data processing, prediction, and artificial pancreas systems.

Purpose of the Study:

To develop and validate a model for describing continuous glucose monitoring (CGM) sensor error using multiple simultaneous sensor recordings.
To dissect CGM sensor error into its constituent components: diffusion, time-varying calibration/drift, and measurement noise.

Main Methods:

Utilized n multiple simultaneous CGM recordings and frequent blood glucose (BG) references.
Developed a sensor error model incorporating blood-to-interstitial glucose diffusion, a linear time-varying model for drift, and an autoregressive model for noise.
Identified model orders and parameters from simultaneous CGM and BG data.

Main Results:

The proposed model successfully describes CGM sensor error by integrating diffusion, time-varying drift, and noise components.
Analysis of Dexcom SEVEN Plus data demonstrated that multiple sensors and robust modeling can differentiate physiological from technological error sources.
The approach allows for a more nuanced understanding of CGM inaccuracies.

Conclusions:

Multiple simultaneous CGM recordings, when analyzed with a comprehensive model, enable a detailed understanding of sensor error.
This method distinguishes between physiological influences and technological limitations affecting CGM performance.
The findings support the development of improved algorithms for CGM data interpretation and diabetes management systems.