基于机器学习的生物标志物发现从血清微量元素和生物化学参数在患有鼻的患者
Berrin Aydin1, Omer Faruk Kocak2, Saime Ozbek Sebin3
1Department of Otorhinolaryngology, Erzurum City Hospital, Erzurum, Turkey.
Biological trace element research
|June 23, 2025
概括
鼻息肉 (NP) 与炎症和改变的微量元素水平有关. 血清中的,铜和葡萄糖显示出作为诊断NP的非侵入性生物标志物的潜力,有助于早期检测和管理.
科学领域:
- 生物化学 生物化学
- 免疫学 免疫学 免疫学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 鼻息肉 (NP) 是与慢性炎症相关的良性生长,影响患者的生活质量.
- 了解NP中的生化变化对于识别诊断标志物至关重要.
研究的目的:
- 评估NP患者的炎症,氧化应激和微量元素稳态.
- 为了确定潜在的非侵入性诊断生物标志物用于鼻息肉.
主要方法:
- 测量了血清微量元素 (,铜,) 和生化参数 (白血球,葡萄糖).
- 使用了包括OPLS-DA和机器学习 (逻辑回归,SHAP) 在内的高级统计分析.
- 使用接收器操作特征 (ROC) 分析来评估生物标志物的区分能力.
主要成果:
- 与对照组相比,NP患者的白细胞计数 (WBC),铜 (Cu) 和葡萄糖水平较高.
- 在NP患者中观察到血清 (Zn) 含量和 Zn/Cu比率明显降低.
- 优化后勤回归实现了100%的分类准确性,其中Zn,Cu和葡萄糖是关键特征.
结论:
- 在NP患者中可以检测到炎症,微量元素失衡 (Zn,Cu) 和代谢变化.
- 血清Zn,Cu,Zn/Cu比,葡萄糖和炎症指数显示为NP的非侵入性诊断生物标志物具有前途.
- 为了临床应用,需要在更大的队列中进一步验证.
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