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预测性分析牙损伤风险通过快速尿液活动评估在唾液使用ZIF-8纳米孔膜.

Bao-Yi Zhou1, Xiao-Yan Shi2, Zhao-Ying Luo1

  • 1Institute for Applied Research in Public Health, School of Public Health, Nantong University, Nantong, Jiangsu 226019, China.

ACS sensors
|May 21, 2025
PubMed
概括
此摘要是机器生成的。

研究人员确定了Streptococcus salivarius的尿酶活性,作为预测牙的新型代谢标志物. 这种方法与机器学习相结合,在识别疾病时实现了81%的准确性.

关键词:
ZIF-8修饰的纳米多孔性牙腐烂是指牙的腐烂.离子运输 离子运输 离子运输逻辑回归的逻辑回归机器学习是机器学习.尿酶活性活动的尿酶活性.

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科学领域:

  • 生物医学工程 生物医学工程
  • 微生物学 微生物学
  • 公共卫生 公共卫生

背景情况:

  • 尽管发病率下降,但牙仍然是一个重要的公共卫生问题.
  • 牙腐烂的复杂原因阻碍了有效的预防和早期干预.
  • 需要快速的预测方法来解决多因素牙损伤.

研究的目的:

  • 为了确定Streptococcus salivarius的尿酶活性作为牙损伤的代谢标志物.
  • 开发一种使用ZIF-8纳米孔膜量化尿酶活性的新方法.
  • 为了研究尿酶迈凯利斯常数 (Km) 与牙损伤发育之间的相关性.

主要方法:

  • 通过测量氧化离子在ZIF-8膜上的扩散来量化尿酶活性.
  • 收集了287个唾液样本以确定尿酶Km.
  • 使用后勤回归和机器学习来预测.

主要成果:

  • 在低至1CFU/mL的度下检测到尿酶活性.
  • 鉴定了尿酶Km和糖摄入频率作为脏病发展的重要因素.
  • 开发了一种具有81%准确度的机器学习模型,用于识别牙虫.

结论:

  • 尿酶活性是牙损的可行的代谢标志物.
  • 开发的方法为早期检测提供了一种敏感的方法.
  • 机器学习集成显示了与更大的样本大小增强预测准确性的承诺.