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Related Concept Videos

Hypoglycemia and Glucagon01:15

Hypoglycemia and Glucagon

651
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...
651
Glucose Homeostasis: Pancreatic Islets and Insulin Secretion01:27

Glucose Homeostasis: Pancreatic Islets and Insulin Secretion

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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...
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Glucose Homeostasis: Regulation of Blood Glucose01:02

Glucose Homeostasis: Regulation of Blood Glucose

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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...
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Glucose Absorption Into the Small Intestine01:26

Glucose Absorption Into the Small Intestine

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Complex carbohydrates consumed cannot be absorbed into the small intestine in their original form. First, they must be hydrolyzed to a monosaccharide form such as glucose or galactose. These monosaccharides are then transported across the intestinal membrane and into the blood via transcellular transport. The intestinal epithelial cells allow the movement of these monosaccharides with a defined 'entry' through membrane transporter proteins present on their apical membrane and...
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Glucagon-like Receptor Agonists01:24

Glucagon-like Receptor Agonists

642
Incretins include glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), which stimulate insulin secretion post-meals. In type 2 diabetes, GIP's efficacy is reduced, making GLP-1 a viable drug target. GIP originates from preproGIP.
GLP-1, when administered in high doses intravenously, triggers insulin secretion, inhibits glucagon release, slows gastric emptying, reduces food intake, and restores normal insulin secretion. However, its rapid inactivation by...
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Hormones Regulating Blood Glucose01:16

Hormones Regulating Blood Glucose

5.8K
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...
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Related Experiment Video

Updated: Nov 30, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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Developing an Individual Glucose Prediction Model Using Recurrent Neural Network.

Dae-Yeon Kim1, Dong-Sik Choi2, Jaeyun Kim3

  • 1Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea.

Sensors (Basel, Switzerland)
|November 17, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a personalized deep learning model for predicting blood glucose levels in Type-2 diabetes patients. The model assists medical staff in managing insulin doses, showing promising results for hospitalized individuals.

Keywords:
continuous glucose monitoringdeep learningdiabetic inpatientglucose prediction model

Related Experiment Videos

Last Updated: Nov 30, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Endocrinology

Background:

  • Hospitalized Type-2 diabetes patients often have unstable glucose levels, necessitating precise monitoring and insulin management.
  • Existing glucose prediction models predominantly focus on Type-1 diabetes, leaving a gap for Type-2 patient-specific solutions.
  • Accurate glucose prediction can significantly aid clinical decision-making regarding insulin therapy.

Purpose of the Study:

  • To develop and evaluate a personalized deep learning model for predicting blood glucose levels in hospitalized Type-2 diabetes patients.
  • To assess the model's performance against established metrics like Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE).
  • To determine the optimal deep learning architecture and data requirements for effective personalized glucose prediction.

Main Methods:

  • Utilized a recurrent neural network (RNN) architecture, including simple RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) layers, for glucose level prediction.
  • Collected one week of continuous glucose monitoring data from 20 hospitalized Type-2 diabetes patients.
  • Trained and tested various RNN configurations to identify the most effective model for personalized glucose forecasting.

Main Results:

  • The proposed model achieved an average RMSE of 21.5 and an MAPE of 11.1% across 20 patients.
  • A GRU model with a single RNN layer and two dense layers demonstrated sufficient performance for glucose level prediction.
  • Personalized models could be trained effectively using as little as 50% of the available patient data.

Conclusions:

  • The developed deep learning model shows comparable performance to existing models for Type-1 diabetes, offering a valuable tool for Type-2 diabetes management.
  • The GRU-based model provides an efficient and effective solution for personalized glucose prediction in hospitalized patients.
  • The study highlights the feasibility of creating accurate personalized glucose prediction models with relatively small datasets, reducing data acquisition burden.