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

Insulin: Dosing Regimen and Adverse Effects

Insulin: Dosing Regimen and Adverse Effects

Insulin-replacement therapy usually includes both long-acting insulin (basal) and short-acting insulin (to cater to postprandial needs). In a diverse group of type 1 diabetes patients, the average daily insulin dose is typically 0.5-0.7 units/kg body weight. However, obese patients and pubertal adolescents may need more due to insulin resistance.
The basal dose constitutes about 40%-50% of the total daily dose, with the rest as premeal insulin. The mealtime insulin dose should mirror...

Diabetes: Management and Pharmacotherapy

Diabetes: Management and Pharmacotherapy

The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...

Model Approaches for Pharmacokinetic Data: Physiological Models

Model Approaches for Pharmacokinetic Data: Physiological Models

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...

Type I Diabetes I: Introduction

Type I Diabetes I: Introduction

Type 1 diabetes mellitus is a chronic metabolic disorder characterized by an absolute deficiency of insulin resulting from the autoimmune destruction of pancreatic β-cells. Although it can occur at any age, it is most commonly diagnosed in childhood, adolescence, or early adulthood. The loss of insulin production impairs cellular glucose uptake, resulting in persistent hyperglycemia and necessitating lifelong insulin therapy.Autoimmune Destruction of β-CellsThe hallmark of type 1 diabetes is an...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

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 assumptions,...

You might also read

Related Articles

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

Sort by

Same author

Survey of Trichogramma (Hymenoptera: Trichogrammatidae) Natural Populations in Quebec Cranberry Bogs and Identification Using rDNA ITS-2 Sequence Analysis.

Journal of economic entomology·2022

Same author

Beyond HbA<sub>1c</sub> : using continuous glucose monitoring metrics to enhance interpretation of treatment effect and improve clinical decision-making.

Diabetic medicine : a journal of the British Diabetic Association·2019

Same author

Black-box Model Identification of Physical Activity in Type-l Diabetes Patients.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2018

Same author

Radiotherapy of relapse-refractory follicular lymphoma.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique·2018

Same author

Factors associated with multiple barriers to access to primary care: an international analysis.

International journal for equity in health·2018

Same author

Quality control and biophysical characterisation data of <i>VanS</i><sub></sub>.

Data in brief·2017

Search research articles

Related Experiment Video

Updated: Jun 10, 2026

Control of Eating Behavior Using a Novel Feedback System

Control of Eating Behavior Using a Novel Feedback System

Published on: May 8, 2018

Anticipating the next meal using meal behavioral profiles: a hybrid model-based stochastic predictive control

C S Hughes¹, S D Patek, M Breton

¹Department of Systems and Information Engineering, University of Virginia, USA.

Computer Methods and Programs in Biomedicine

|July 22, 2010

Summary

This study introduces a novel control law for Type 1 Diabetes Mellitus (T1DM) management, using a probabilistic meal profile to anticipate glucose fluctuations. The strategy enhances safety and controller performance in artificial pancreas systems despite mealtime uncertainty.

More Related Videos

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

Published on: November 29, 2024

Static Strength Training Method for Type 2 Diabetic Mice

Static Strength Training Method for Type 2 Diabetic Mice

Published on: March 29, 2024

Related Experiment Videos

Last Updated: Jun 10, 2026

Control of Eating Behavior Using a Novel Feedback System

Control of Eating Behavior Using a Novel Feedback System

Published on: May 8, 2018

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

Published on: November 29, 2024

Static Strength Training Method for Type 2 Diabetic Mice

Static Strength Training Method for Type 2 Diabetic Mice

Published on: March 29, 2024

Area of Science:

Biomedical Engineering
Control Systems Engineering
Endocrinology

Background:

Type 1 Diabetes Mellitus (T1DM) management relies on balancing glucose levels through insulin therapy.
Subcutaneous (SC) glucose sensing and insulin infusion present challenges due to inherent delays, impacting control system stability and responsiveness.
Model Predictive Control (MPC) offers a way to anticipate and manage glucose fluctuations, but its effectiveness is limited by uncertainty in meal timing.

Purpose of the Study:

To develop and evaluate a novel control law for T1DM management that accounts for uncertain meal timing.
To mitigate the destabilizing effects of sensing and actuation delays in SC-SC artificial pancreas systems.
To improve the safety and aggressiveness of glucose control by incorporating probabilistic meal behavior.

Main Methods:

Development of a control law utilizing a probabilistic description of patient eating behavior (random meal profile).
Preclinical in silico trials employing the Dalla Man et al. oral glucose meal model.
Simulation of the SC-SC control strategy under conditions of uncertain meal arrival.

Main Results:

The proposed control strategy effectively anticipates meals based on probabilistic behavioral profiles.
The system demonstrated safe and effective glucose control, even when anticipated meals were skipped.
The control law successfully accounted for uncertain prior knowledge of meals without compromising patient safety.

Conclusions:

A novel control law using probabilistic meal profiles can significantly improve artificial pancreas performance in T1DM.
This approach addresses the critical challenge of mealtime uncertainty in SC-SC diabetes control systems.
The strategy offers a safe and convenient method for managing glucose levels in Type 1 Diabetes Mellitus.