以人工智能为基础的无标签的等离子体外基因组SERS分析策略,用于早期肺癌检测
Dechan Lu1, Zhikun Shangguan1, Zhehao Su1
1School of Mechanical, Electrical & Information Engineering, PuTian University, PuTian, Fujian, 351100, China.
Analytical and bioanalytical chemistry
|July 17, 2024
概括
这项研究引入了一种人工智能驱动的SERS方法,用于使用外体的早期肺癌检测. 这种方法可以准确地从等离子体外基因组中识别早期的肺癌,提供了一个有前途的非侵入性诊断工具.
科学领域:
- 生物标志物 生物标志物
- 纳米技术 纳米技术
- 人工智能的人工智能
背景情况:
- 外体显示出作为肺癌生物标志物在体外诊断的前景,解决当前成像和活检方法的局限性.
- 精确检测微妙的外体组合差异是具有挑战性的,因为内在的外体异质性.
- 开发可靠的外体分析方法对于早期肺癌诊断至关重要.
研究的目的:
- 开发一种人工智能辅助的表面增强拉曼光谱 (SERS) 策略,用于无标签的等离子体外体的分析.
- 准确诊断早期的肺癌使用外体谱数据.
- 为肺癌建立一种新的非侵入性预查技术.
主要方法:
- 使用肺癌和正常细胞系的外体谱数据构建了一个深度学习模型.
- 卷积神经网络 (CNN) 和支持矢量机器 (SVM) 模型被用来提取和分析外体的光谱特征.
- 人工智能辅助的SERS策略应用于血外体样本,以区分现场腺癌 (AIS) 和健康对照 (HC).
主要成果:
- 人工智能辅助的SERS方法成功地分析了无标签分析的等离子体外体.
- 综合的光谱特征有效地区分了AIS和HC样本.
- 该方法实现了0.84的曲线下面积 (AUC),在区分AIS和HC时具有83.3%的灵敏度和83.3%的特异性.
结论:
- 外体细胞被验证为早期肺癌诊断的有效生物标志物.
- 人工智能辅助的SERS战略为早期肺癌检测提供了一种新且准确的方法.
- 这种技术为肺癌提供了一种有前途的非侵入性预选方法,提高了诊断能力.
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