深度学习辅助的质谱成像用于精神活性物质的初步查和预分类
Yingjie Lu1, Yuqi Cao2, Xiaohang Tang3
1Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China; Department of Pharmacognosy, School of Pharmacy, Naval Medical University, Shanghai, 200433, China.
Talanta
|February 18, 2024
概括
这项研究引入了一种使用质谱成像和卷积神经网络 (CNN) 快速选新精神活性物质的新方法. 该方法通过分析神经递质变异来有效地分类物质,有助于法医识别.
科学领域:
- 法医科学 法医科学 法医科学
- 神经科学是一个神经科学.
- 计算化学计算化学
背景情况:
- 新的精神活性物质的出现需要快速的预分类和选方法.
- 基于化学结构和药理学的传统分类对于新物质是不够的.
- 现有的方法很难解决新兴精神活性物质的复杂性.
研究的目的:
- 开发可疑精神活性物质的快速预分类和选方法.
- 为此目的利用质谱成像和卷积神经网络 (CNN).
- 提高识别新型精神活性物质的准确性和效率.
主要方法:
- 质谱成像是在空白组和物质诱导组的大脑切片上进行的.
- 产生了神经递质变异质谱图像 (Nv-MSI),以突出差异.
- 一个CNN模型被开发和训练来分类Nv-MSI.
主要成果:
- 与传统分类方法相比,CNN模型实现了更高的估计准确性.
- 对于CNN的方法,所需的数据预处理是最小的.
- 美国有线电视新闻网的分析确定了影响分类的关键大脑区域和神经递质.
结论:
- 质谱成像和CNN的联合使用为预先分类和选疑似精神活性物质提供了有效的工具.
- 这种方法成功地帮助识别了被扣押的样品作为一种大麻素.
- 开发的技术显著加快了新的精神活性物质的选过程.
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