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Diabetes Mellitus: Type 2 and Gestational01:22

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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Pathophysiology of Diabetes01:20

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Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...
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Diabetes: Management and Pharmacotherapy01:15

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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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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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Biguanides, particularly metformin (Glucophage), are insulin sensitizers that enhance glucose uptake, thereby reducing insulin resistance. Unlike sulfonylureas, metformin doesn't prompt insulin secretion, which helps to curb hypoglycemia risk. Metformin is beneficial in treating conditions like polycystic ovary syndrome due to its insulin-resistance reduction capability. The drug's primary action involves curtailing hepatic gluconeogenesis, a significant contributor to high blood...
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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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妊娠中の糖尿病の治療における人工知能:体系的なレビュー

Rawan AlSaad1, Ali Elhenidy2, Aliya Tabassum3

  • 1AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.

Journal of diabetes science and technology
|August 26, 2025
PubMed
まとめ

人工知能 (AI) は,妊娠糖尿病 (GDM) の予測とケアを改善することを約束しています. これらのAIツールの臨床統合には,さらなる検証と前向きな研究が不可欠です.

キーワード:
人工知能糖尿病妊娠糖尿病妊娠女性の健康

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科学分野:

  • 医療情報学
  • コンピューター医学
  • 医療における人工知能

背景:

  • 人工知能 (AI) は,妊娠糖尿病 (GDM) の治療に先端的なデータベースのアプローチを提供します.
  • AIはGDMの早期発見,パーソナライズされた管理,介入戦略を容易にする.

研究 の 目的:

  • GDMケアにおけるAIモデルの適用を体系的に検討し,統合する.
  • GDMのスクリーニング,診断,管理,結果予測におけるAIの役割を検討する.
  • 研究設計,データ型,AIモデル,検証,パフォーマンスメトリックを分析する.

主な方法:

  • 2025年2月までの6つの電子データベースのシステム検索
  • 対象となる研究126件を対象にデータ抽出と品質評価を行った.
  • バイアスのリスクの評価に使用される修正された QUADAS-2 ツール.

主要な成果:

  • ほとんどの研究 (75%) は遡及的設計を使用し,85%はGDMの予測に焦点を当てた.
  • クラシックな機械学習,特にロジスティック回帰とランダムフォレストが一般的であった (84%).
  • 内部検証は一般的であった (68%),外部検証は稀であった (6%);報告の欠陥がある中等から良好な品質.

結論:

  • AIはGDMの予測,スクリーニング,管理を向上させる大きな可能性を秘めています.
  • より広範な検証,モデル解釈の改善,将来性研究が不可欠です.
  • 人工知能技術を臨床実用化するには 現在の限界を克服する必要があります