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: Pancreatic Islets and Insulin Secretion

Glucose Homeostasis: Pancreatic Islets and Insulin Secretion

The pancreatic islets comprising only 1%-2% of the volume are highly vascularized and innervated mini-organs. They contain five endocrine cell types, including β cells that secrete insulin, which is synthesized as a single polypeptide chain, preproinsulin, processed to proinsulin, and finally to insulin and C-peptide. This process is complex and regulated, involving the Golgi complex, the endoplasmic reticulum, and the secretory granules of the β cell.
Insulin and C-peptide are co-secreted in...

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...

Hypoglycemia and Glucagon

Hypoglycemia and Glucagon

Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...

Hormones Regulating Blood Glucose

Hormones Regulating Blood Glucose

Insulin is released by beta cells of the pancreas when blood glucose levels are high. It facilitates glucose absorption and utilization in insulin-dependent cells with insulin receptors on their plasma membranes. Insulin promotes glucose uptake by increasing the number of glucose transport proteins in the cell membrane, allowing glucose to enter the cell. As a result, glucose utilization and ATP production are enhanced.
In addition to accelerating glucose uptake and utilization, insulin has...

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).

SBAR II: Application of SBAR

SBAR II: Application of SBAR

SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...

You might also read

Related Articles

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

Sort by

Same author

Catalytic Ambient Temperature Dinitrogen Conversion to a Bis(silyl)amine by Mononuclear Group 4 Aryloxide Complexes.

Inorganic chemistry·2026

Same author

Training for the Digital Clinical Environment: Preliminary Findings in Implementation of Electronic Health Record Platform in Medical Education.

Journal of medical systems·2026

Same author

Uranyl Tris(benzoate) Photocatalysts for Site-Selective Hydrocarbon Functionalization.

Journal of the American Chemical Society·2026

Same author

Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.

Frontiers in bioinformatics·2025

Same author

Loss of ADAM17 in smooth muscle cells enhances their transformation to macrophage-like cells leading to more severe atherosclerosis in mice.

Atherosclerosis·2025

Same author

Prognostic Impact of Lymph Node Yield in a Phase III Clinical Trial (NRG/RTOG 9601) of Prostate Cancer Patients Treated With Salvage Radiation Following Prostatectomy.

The Prostate·2025

Search research articles

Related Experiment Video

Updated: Jun 4, 2026

Improving IV Insulin Administration in a Community Hospital

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

Modeling Glucose Homeostasis and Insulin Dosing in an Intensive Care Unit using Dynamic Bayesian Networks.

Senthil K Nachimuthu¹, Anthony Wong, Peter J Haug

¹Department of Biomedical Informatics, University of Utah;

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 25, 2011

Summary

This study introduces a Dynamic Bayesian Network (DBN) model for managing serum glucose in critically ill patients. The DBN model demonstrates comparable or superior performance to existing rule-based protocols in predicting insulin doses.

More Related Videos

Studying the Hypothalamic Insulin Signal to Peripheral Glucose Intolerance with a Continuous Drug Infusion System into the Mouse Brain

Studying the Hypothalamic Insulin Signal to Peripheral Glucose Intolerance with a Continuous Drug Infusion System into the Mouse Brain

Published on: January 4, 2018

A Model of Chronic Nutrient Infusion in the Rat

A Model of Chronic Nutrient Infusion in the Rat

Published on: August 14, 2013

Related Experiment Videos

Last Updated: Jun 4, 2026

Improving IV Insulin Administration in a Community Hospital

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

Studying the Hypothalamic Insulin Signal to Peripheral Glucose Intolerance with a Continuous Drug Infusion System into the Mouse Brain

Studying the Hypothalamic Insulin Signal to Peripheral Glucose Intolerance with a Continuous Drug Infusion System into the Mouse Brain

Published on: January 4, 2018

A Model of Chronic Nutrient Infusion in the Rat

A Model of Chronic Nutrient Infusion in the Rat

Published on: August 14, 2013

Area of Science:

Critical Care Medicine
Biomedical Informatics
Computational Biology

Background:

Controlling serum glucose in critically ill patients is challenging and impacts clinical outcomes.
Understanding temporal relationships in disease processes aids treatment optimization.
Current methods often rely on rule-based protocols for insulin and glucose management.

Purpose of the Study:

To develop and evaluate a Dynamic Bayesian Network (DBN) model for representing and predicting insulin and glucose homeostasis.
To compare the DBN model's performance against a current rule-based protocol (eProtocol-insulin) in an intensive care unit (ICU) setting.

Main Methods:

Utilized the Projeny toolkit to construct a DBN model incorporating clinical variables.
Modeled temporal and atemporal relationships governing insulin and glucose homeostasis.
Evaluated the DBN model by comparing its insulin dose predictions with those from the eProtocol-insulin system.

Main Results:

The DBN model's insulin dose predictions were found to be as effective as, or better than, the rule-based eProtocol-insulin.
The model provides a novel approach to understanding complex glucose regulation in critical care.

Conclusions:

Dynamic Bayesian Networks offer a promising tool for optimizing glucose control in critically ill patients.
The developed DBN model shows potential for improving clinical decision-making in intensive care settings.