Cell子刊：我国学者利用牙菌斑微生物时空变化，实现儿童蛀牙的精准诊断和预测

Core Viewpoint - The article discusses the significance of early childhood caries (ECC) and highlights a recent study that developed a high-resolution microbial map to understand the microbial causes of caries at the single-tooth level, which can inform targeted prevention strategies [2][4][7]. Group 1 - ECC is a common issue that can lead to various health consequences, including pain, chewing difficulties, infections, and impacts on the development of permanent teeth and overall health [2]. - The study published in Cell Host & Microbe utilized machine learning to create a spatial microbial indicator (Spatial-MiC) that achieved 98% accuracy in diagnosing early caries and 93% accuracy in predicting future caries in seemingly healthy teeth [3][4]. Group 2 - The research analyzed 2,504 plaque microbiota samples from 89 preschool children over 11 months, revealing that healthy children's oral microbiota exhibited a gradient and symmetry, which was disrupted in children with caries [4]. - The findings indicate that specific microbial features related to individual teeth can guide targeted prevention strategies for ECC [4][7].