公に利用可能なデータを用いた口腔微生物群の多様性とランダムな森林モデル
Alba Regueira-Iglesias1, Berta Suárez-Rodríguez1, Triana Blanco-Pintos1
1Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical-Surgical Specialties, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Instituto de Investigación Sanitaria de Santiago (IDIS), Santiago de Compostela, Spain.
Journal of periodontology
|August 21, 2025
まとめ
機械学習モデルは健康な個体における 口腔微生物群のニッチを正確に区別しました 唾液と歯のプラークに特定の細菌のサインが特定され,歯周病の潜在的バイオマーカーを強調しました.
科学分野:
- 口腔微生物群の研究
- 微生物生態学
- 計算生物学
背景:
- 歯周病における上歯,下歯,唾液微生物群の16Sメタバーコーディングに関する証拠は限られている.
- これらの微生物のコミュニティを理解することは 診断と治療に不可欠です
研究 の 目的:
- 歯周が健康な被験者の上歯,下歯,唾液の微生物群の多様性を分析する.
- 口腔微生物のニッチを分類する機械学習モデルの可能性を評価する.
主な方法:
- 491人の健康な被験者からの848個のサンプル (上歯,下歯,唾液) の分析.
- 16S rRNA遺伝子の配列化と処理は,口腔特有のデータベースを用いて行われます.
- ランダムフォレスト (RF) モデルを構築し,分類の正確性をテストしました.
主要な成果:
- プラークの種類と唾液の間でバクテリアの豊富さの有意な違いが見つかりました.
- RFモデルは精度によってサンプルを分類し,唾液とプラークを区別する優れた性能 (AUC > 0.98) を示した.
- 5つのアンプリカン配列変異 (ASV) は,上下歯関節プラークの分類において重要であった (AUC = 0. 908).
結論:
- 上および下細菌のプロファイルはわずかな差異を示したが,唾液とは異なる.
- 機械学習は 利口に特化した微生物のサインを 効果的に特定しました
- 特定のASVは,口腔内ニッチの潜在的バイオマーカーを示し,下膜斑と唾液と関連していました.
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