分析物
生物标志物
化学
人工智能
荧光
人工神经网络
模式识别(心理学)
代谢物
计算机科学
RGB颜色模型
图像处理
反向传播
人类健康
目标检测
计算机视觉
标识
DOI:10.1021/acs.analchem.5c05732
摘要
Developing a sensitive analytical platform for monitoring tiopronin (MPG), its metabolite 2-mercaptopropionic acid (MPA), and the key liver biomarker glutathione (GSH) is crucial for liver health assessment. Here, an artificial intelligence-assisted bionic vision platform based on a dual-emission Tb3+-functionalized hydrogen-bonded organic framework (Tb@HOF-dobpdc) is constructed. The ratiometric fluorescent sensor exhibits a distinct ″turn-on″ response, enabling highly sensitive detection of MPG, MPA, and GSH with low limits of detection (0.20, 0.68, and 0.31 μM, respectively). For practical application, Tb@HOF-dobpdc can achieve rapid detection of target analytes in real serum and urine samples by combining with the hydrogel. To overcome the human eye's limitation in discerning subtle color changes, RGB channel processing is used to generate visually distinguishable pseudocolor signals. Furthermore, a backpropagation neural network (BPNN) is applied for accurately distinguishing analyte concentrations by identifying fluorescence images. Leveraging the strong correlation between these analytes and liver health, an AND logic gate diagnostic system is established, outputting ″healthy″ only when all biomarker levels are within safe ranges. This intelligent platform combines advanced material design and artificial intelligence, providing a powerful tool for point-of-care liver health diagnosis.
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