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Related Concept Videos

Diabetes: Management and Pharmacotherapy01:15

Diabetes: Management and Pharmacotherapy

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The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...
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Insulin: Dosing Regimen and Adverse Effects01:16

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Insulin-replacement therapy usually includes both long-acting insulin (basal) and short-acting insulin (to cater to postprandial needs). In a diverse group of type 1 diabetes patients, the average daily insulin dose is typically 0.5-0.7 units/kg body weight. However, obese patients and pubertal adolescents may need more due to insulin resistance.
The basal dose constitutes about 40%-50% of the total daily dose, with the rest as premeal insulin. The mealtime insulin dose should mirror...
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Diabetes Mellitus: Overview and Type I Subtype01:22

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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Hypoglycemia and Glucagon01:15

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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: Biosynthesis, Chemistry, and Preparation01:25

Insulin: Biosynthesis, Chemistry, and Preparation

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The endoplasmic reticulum (ER) of pancreatic β-cells synthesizes preproinsulin, which consists of a signal peptide, A and B chains, and a C-peptide. Preproinsulin is then cleaved and folded into proinsulin, which translocates to the Golgi apparatus for sorting and packaging into secretory granules. In these granules, enzymatic clipping generates insulin and C-peptide.
Damage or functional impairment of β-cells inhibits insulin production, leading to diabetes. Diabetes treatment...
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Towards Personalized AI-Based Diabetes Therapy: A Review.

Sara Campanella, Giovanni Paragliola, Valentino Cherubini

    IEEE Journal of Biomedical and Health Informatics
    |August 13, 2024
    PubMed
    Summary

    Artificial intelligence (AI) enhances diabetes treatment through personalized therapy and optimized algorithms. Future advancements in AI and data analysis will address current limitations, enabling wider clinical adoption for better patient outcomes.

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    Area of Science:

    • Medical Artificial Intelligence (AI)
    • Diabetes Management Technologies

    Background:

    • Diabetes affects millions globally, necessitating advanced treatment solutions.
    • Smart devices and AI are revolutionizing medical diagnostics and therapeutic strategies.
    • AI methods aid physicians in diagnosis, treatment selection, and outcome prediction.

    Purpose of the Study:

    • To analyze the application of AI in enhancing and personalizing diabetes treatment.
    • To review learning models, data types, deployment stages, and application domains of AI in diabetes care.
    • To identify key trends and limitations in AI-driven diabetes management.

    Main Methods:

    • Systematic review of 77 original research papers on AI in diabetes treatment.
    • Analysis of AI learning models, data typology, deployment stage, and application domains.
    • Identification of trends in patient-based therapy personalization and therapeutic algorithm optimization.

    Main Results:

    • AI enables personalized diabetes treatment tailored to individual patients.
    • AI optimizes therapeutic algorithms for more effective diabetes management.
    • Key trends include patient-based personalization and algorithm optimization.

    Conclusions:

    • AI significantly enhances diabetes treatment personalization and therapeutic algorithm optimization.
    • Current limitations include a lack of multimodal data analysis and interpretability.
    • Future AI improvements and increased data availability promise wider clinical deployment.