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Glucose Homeostasis: Regulation of Blood Glucose01:02

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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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Hormones Regulating Blood Glucose01:16

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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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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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The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
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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...
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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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A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli
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Neural Physiological Model: A Simple Module for Blood Glucose Prediction.

Kang Gu, Ruoqi Dang, Temiloluwa Prioleau

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
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    Summary

    This study introduces the Neural Physiological Encoder (NPE) for predicting blood glucose levels using continuous glucose monitoring (CGM) data. The NPE model effectively captures temporal patterns, improving diabetes management and preventing glycemic events.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Healthcare
    • Diabetes Technology

    Background:

    • Continuous glucose monitors (CGM) and insulin pumps are vital for diabetes management.
    • Data from these devices offer potential for accurate blood glucose prediction.
    • Preventing adverse glycemic events is a key challenge in diabetes care.

    Purpose of the Study:

    • To introduce the Neural Physiological Encoder (NPE), a novel module for blood glucose prediction.
    • To investigate the efficacy of decomposed convolutional filters for feature generation in the diabetes domain.
    • To enhance the accuracy of predicting future blood glucose levels.

    Main Methods:

    • Development of the Neural Physiological Encoder (NPE) module.
    • Leveraging decomposed convolutional filters for automatic feature generation.
    • Integration of NPE with a Long Short-Term Memory (LSTM) network for prediction.

    Main Results:

    • The NPE model effectively captures temporal patterns and associations with daily activities.
    • NPE+LSTM achieved an RMSE of 9.18 mg/dL for 30-minute blood glucose prediction on an in-house dataset (34 subjects).
    • State-of-the-art RMSE of 17.80 mg/dL was achieved on the public OhioT1DM dataset (6 subjects).

    Conclusions:

    • The proposed NPE model demonstrates significant potential for accurate blood glucose prediction.
    • This approach offers a novel method for utilizing CGM data in diabetes management.
    • The findings suggest improved strategies for preventing adverse glycemic events.