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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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Hypoglycemia01:26

Hypoglycemia

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Hypoglycemia is a blood glucose level below 70 mg/dL. It commonly occurs in individuals using insulin or insulin-secreting drugs, but may also arise in non-diabetic conditions. People with type 1 diabetes are at the highest risk because they depend on exogenous insulin. People with type 2 diabetes are also at risk, especially when treated with insulin or medications such as sulfonylureas, which increase insulin release regardless of blood glucose levels. It develops when insulin levels exceed...
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Hyperglycemia01:29

Hyperglycemia

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Hyperglycemia is an abnormally high blood glucose level. It is diagnosed by fasting glucose ≥126 mg/dL, 2-hour oral glucose tolerance test (or OGTT) ≥200 mg/dL, random glucose ≥200 mg/dL with symptoms, or HbA1c ≥6.5%. However, HbA1c results may be unreliable in certain conditions, such as anemia or hemoglobinopathies, and the diagnosis should be confirmed unless classic symptoms are present. Postprandial hyperglycemia is typically considered significant when glucose...
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Hyperosmolar Hyperglycemic State01:21

Hyperosmolar Hyperglycemic State

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Hyperosmolar Hyperglycemic State, or HHS, is a serious and life-threatening complication of type 2 diabetes mellitus. It is characterized by three main features: severe hyperglycemia, profound dehydration, and elevated serum osmolality, all occurring without significant ketoacidosis.HHS typically develops in older adults or individuals with limited access to fluids. This may result from illness, cognitive impairment, or medications such as diuretics or corticosteroids. These factors reduce...
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Diabetic Ketoacidosis l: Introduction01:25

Diabetic Ketoacidosis l: Introduction

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DefinitionDiabetic ketoacidosis (DKA) is an acute, life-threatening complication of diabetes mellitus, characterized by a triad of hyperglycemia (blood glucose >250 mg/dL), ketonemia or ketonuria, and metabolic acidosis (arterial pH <7.30 and serum bicarbonate <18 mEq/L). It results from insulin deficiency combined with elevated levels of counterregulatory hormones—glucagon, catecholamines, cortisol, and growth hormone—leading to increased lipolysis, hepatic...
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Hormones Regulating Blood Glucose01:16

Hormones Regulating Blood Glucose

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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.
In addition to accelerating glucose uptake and utilization, insulin has...
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Related Experiment Video

Updated: Apr 18, 2026

Author Spotlight: Investigating the Blood Glucose Homeostasis in Murine Brain Using a Cost-Effective Hyperglycemic And Hypoglycemic Clamp Technique
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A novel extreme learning machine for hypoglycemia detection.

Phyo Phyo San, Sai Ho Ling, Ni Ni Soe

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary
    This summary is machine-generated.

    Hypoglycemia detection in type 1 diabetes mellitus (T1DM) is crucial. An extreme learning machine (ELM) system shows promise for early detection, identifying episodes by analyzing physiological changes.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Medicine
    • Endocrinology

    Background:

    • Hypoglycemia is a frequent and limiting side-effect of insulin therapy for type 1 diabetes mellitus (T1DM).
    • Severe hypoglycemia poses significant risks, including seizures, loss of consciousness, and potential permanent brain dysfunction.
    • Accurate and early detection of hypoglycemic episodes is a critical research area for patient well-being.

    Purpose of the Study:

    • To develop and evaluate an extreme learning machine (ELM) based system for the early detection of hypoglycemic episodes in patients with T1DM.
    • To investigate physiological markers associated with nocturnal hypoglycemia in children with T1DM.

    Main Methods:

    • A clinical study involving 16 children with T1DM was conducted to observe natural hypoglycemic episodes.
    • Physiological data, including heart rate and corrected QT intervals, were collected during hypoglycemic events.
    • An extreme learning machine-trained feed-forward neural network (ELM-FFNN) was utilized for hypoglycemia detection, with data split into training and testing sets.

    Main Results:

    • Nocturnal hypoglycemic episodes were associated with statistically significant increases in heart rate (p < 0.06) and corrected QT intervals (p < 0.001).
    • The ELM-FFNN system achieved a testing sensitivity of 78% for detecting hypoglycemia.
    • The system demonstrated a testing specificity of 60% in identifying non-hypoglycemic periods.

    Conclusions:

    • The study highlights the association between hypoglycemia and specific physiological changes in T1DM patients.
    • An ELM-based system demonstrates potential for early hypoglycemia detection, although further refinement is needed to improve accuracy.
    • This technology offers a promising avenue for enhancing glycemic control and patient safety in type 1 diabetes management.