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相关概念视频

Biofilms01:29

Biofilms

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Biofilms are complex communities of microorganisms encased in a self-produced extracellular polysaccharide matrix attached to surfaces. These microbial consortia can include single or multiple species, providing enhanced survival benefits by forming organized, multilayered structures.The formation of biofilms occurs through four key stages: attachment, colonization, development, and dispersal.During attachment, free-swimming planktonic cells adhere to a surface, often facilitated by...
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相关实验视频

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Organotypic Tissue Model Systems for Investigating Host-Pathogen Interactions In Vitro
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在口腔生物膜中预测微生物间宿主特异性,使用轻量级关系意识知识图模型.

Prabhu Manickam Natarajan1,2, Sudhir Rama Varma1,2, Jayaraj Kodangattil Narayanan2,3

  • 1Department of Clinical sciences, College of Dentistry, Ajman University, Ajman, United Arab Emirates.

Frontiers in cellular and infection microbiology
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概括

这项研究引入了一种基于图形的新型模型,用于预测口腔中的微生物相互作用,改善与疾病相关的病毒的检测和了解口腔微生物组动态.

关键词:
细菌菌体是一种细菌体.主机的特殊性 主机的特殊性知识图表知识图表通过口服使用的生物膜.口腔微生物组是口腔的微生物组.牙周病是一种牙周病.菌体宿主相互作用病毒组病毒组

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科学领域:

  • 微生物学和生物信息学
  • 计算生物学 计算生物学
  • 系统生物学 系统生物学

背景情况:

  • 人口腔内有复杂的微生物生态系统,包括细菌和病毒,形成生物膜.
  • 菌细菌的特异性对于微生物社区的稳定性和预防失生症至关重要,但对其进行测绘在实验上具有挑战性.
  • 传统的方法很难捕捉口腔微生物群相互作用的生态复杂性.

研究的目的:

  • 开发基于图形的模型,用于预测口腔生物膜中的微生物间宿主特异性.
  • 将各种数据 (分类学,生态学,感染) 整合到关系式学习的知识图中.
  • 改善菌-细菌相互作用的预测,并确定与牙周病相关的微生物中心.

主要方法:

  • 构建了口腔微生物组的异质,关系意识的知识图,包括分类,利基和感染关系.
  • 集成微生物特征与图形嵌入,并开发了一个关系感知图形神经网络 (IK-BRNet).
  • 雇员分层交叉验证与阶级不平衡校正用于与传统图表注意力网络 (GAT) 相比的模型评估.

主要成果:

  • 与GAT相比,IK-BRNet表现出更快的收和更高的歧视,实现了较高的AUC-ROC (0.929与0.904) 相比.
  • IK-BRNet显著提高了与疾病相关的病毒类型的敏感性 (93.8%与56.3%相比),减少了虚假阴性.
  • 特定地点的预测与生物有效性保持一致,识别了牙斑相关病毒的较高疾病得分.

结论:

  • 关系意识图形学习为口腔生物膜中模拟微生物间宿主特异性提供了有效的框架.
  • 开发的模型增强了口腔微生物组网络推断,有助于疾病查和生态分析.
  • 这种方法支持了基于微生物的牙科和理解口腔健康的进步.