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Related Concept Videos

Hypoglycemia and Glucagon01:15

Hypoglycemia and Glucagon

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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...
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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.
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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.
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Diabetes: Symptoms, Diagnosis, and Complications01:15

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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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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.
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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach.

Hoda Nemat, Heydar Khadem, Mohammad R Eissa

    IEEE Journal of Biomedical and Health Informatics
    |January 25, 2022
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    Summary

    Advanced machine learning models improve blood glucose level (BGL) prediction for type-1 diabetes management. Deep-ensemble models with meta-learning show superior performance over traditional methods.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence
    • Endocrinology

    Background:

    • Type-1 diabetes management requires precise blood glucose level (BGL) control.
    • Automated BGL prediction using machine learning (ML) offers a promising approach to support diabetes management.

    Purpose of the Study:

    • To develop and evaluate advanced ML architectures for BGL prediction.
    • To investigate novel meta-learning approaches within deep-ensemble models.
    • To assess the impact of changing time series dimensions on forecasting accuracy.

    Main Methods:

    • Development of deep-ensemble ML models incorporating novel meta-learning.
    • Investigation of dimension changes in univariate time series forecasting.
    • Evaluation of models using regression and clinical metrics.
    • Comparison against benchmark non-ensemble ML models.

    Main Results:

    • The proposed deep-ensemble models demonstrated superior performance compared to non-ensemble benchmarks.
    • The novel meta-learning approaches proved effective in enhancing prediction accuracy.
    • The study confirmed the efficacy of the developed ML architectures for BGL prediction.

    Conclusions:

    • Advanced deep-ensemble ML models, particularly with meta-learning, significantly improve BGL prediction.
    • These models hold potential for enhancing type-1 diabetes management and patient outcomes.
    • The findings support the integration of sophisticated ML techniques in clinical decision support systems.