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関連する概念動画

Irritable Bowel Syndrome I: Introduction01:17

Irritable Bowel Syndrome I: Introduction

360
Irritable Bowel Syndrome (IBS) is characterized by functional disturbances in the gastrointestinal system, presenting a cluster of symptoms without evident structural or biochemical abnormalities. It primarily affects the large intestine and may cause abdominal pain, bloating, excessive gas, diarrhea, constipation, or both.
IBS is a chronic condition that can persist over a long period or recur frequently.
The pathogenesis of IBS involves a complex interplay of the following factors:
Altered...
360
Irritable Bowel Syndrome II: Clinical Features and Diagnostic Evaluation01:30

Irritable Bowel Syndrome II: Clinical Features and Diagnostic Evaluation

232
Irritable Bowel Syndrome II: Clinical Features and Diagnostic Evaluation
Irritable Bowel Syndrome (IBS) is classified into subtypes based on the predominant bowel habits as determined by the Bristol Stool Form Scale (BSFS). The subtypes are:
232
Anatomy of the Intestines01:23

Anatomy of the Intestines

73.8K
Although digestion of proteins, carbohydrates, and lipids may begin in the stomach, it is completed in the intestine. The absorption of nutrients, water, and electrolytes from food and drink also occurs in the intestine. The intestines can be divided into two structurally distinct organs—the small and large intestines.
Small Intestines
The small intestine is an ~7 meter-long tube with an inner diameter of just 2.5 cm. Since most nutrients are absorbed here, the inner lining of the...
73.8K
Chronic Bowel Disorders: Introduction01:17

Chronic Bowel Disorders: Introduction

536
Chronic bowel diseases are a group of long-term conditions affecting the digestive tract, characterized by inflammation and damage to the gut lining. These conditions primarily include irritable bowel syndrome and inflammatory bowel disease.
Irritable Bowel Syndrome (IBS) is a common disorder affecting the gastrointestinal tract. The distinctive feature is recurrent abdominal pain associated with altered bowel movements, manifesting as constipation, diarrhea, or fluctuating between both. The...
536
Bacterial Flora of the Large Intestine01:29

Bacterial Flora of the Large Intestine

640
The gut microbiome is formed by a vast and diverse community of bacteria that colonizes our large intestine. These bacteria start residing in the gut from birth and continue diversifying throughout life, influenced by factors such as diet, lifestyle, and stress. The gut bacterial community also includes bacteria from food and those that enter the colon through the anus.
The normal gut flora of the colon plays a critical role in generating essential vitamins such as vitamins K, B5, and B7.
640
Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

140
This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
140

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Updated: Sep 9, 2025

Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform
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ディープラーニングを用いた刺激性腸症候群における腸微生物のダイナミクスを解読する

Faisal1, S R Mani Sekhar1, D S Anurag1

  • 1Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India.

Neurogastroenterology and motility
|September 5, 2025
PubMed
まとめ

ディープ・マシン・ラーニング・モデルは ヒトの腸内微生物群データを用いて 病気を正確に分類します ディープニューラルネットワークは92.79%の精度を達成し,診断と健康状態の改善の可能性を強調しました.

キーワード:
分類するディープラーニング刺激性腸症候群機械学習微生物群

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Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform
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科学分野:

  • 微生物群の研究
  • 計算生物学
  • 医学における機械学習

背景:

  • 人間の腸内微生物群は健康と病気において重要な役割を果たし,生理学的プロセスや代謝/免疫機能に影響を与えます.
  • 複雑で高次元な微生物群データは,複雑な微生物の相互作用と個体間の多様性により,重要な分析課題を提示しています.
  • 刺激性腸症候群 (IBS) のような疾患は 腸内微生物の変化と関連しており 研究の重要な分野となっています

研究 の 目的:

  • 複雑なヒト腸内微生物群データを分析するための高度な機械学習技術の適用を調査する.
  • 微生物群のプロファイルに基づいた正確な疾患分類のための効果的なモデルを特定する.
  • 微生物の洞察力を活用した新しい診断と治療戦略を探求する.

主な方法:

  • 複数の機械学習モデル (XGBoost,RandomForest,Logistic Regression,LightGBM,DNN) の実装と評価について
  • 微生物群のデータを精密に処理して 意味のあるパターンを抽出します
  • 疾病分類におけるモデルの強度と信頼性を確保するために,包括的なデータセットで厳格なクロス検証を行います.

主要な成果:

  • 精度,感度,特異性という点でのモデルの性能の比較分析
  • ディープニューラルネットワーク (DNN) は,高度なパターン認識能力により優れたパフォーマンスを示しました.
  • DNNモデルを使用して 92.79%の高い分類精度を達成しました.

結論:

  • この研究は,ヒトの健康や病気に対する 微生物群の影響の理解を深めています
  • 先進的な深層機械学習モデルは 複雑な微生物群データを分析するための強力なツールを提供します
  • この研究により 診断方法の改善や 世界的な健康状態の改善が期待できます