Integration of resol/block-copolymer carbonization and machine learning: A convenient approach for precise monitoring of glycan-associated disorders
Chinese Chemical Letters(2024)
摘要
Ensuring the timely and precise monitoring of severe liver diseases is crucial for guiding effective therapies and significantly extending overall quality of life. However, this remains a worldwide challenge, given the high incidence rate and the presence of strong confounding clinical symptoms. Herein, we applied a convenient and high-yield method to prepare the magnetic mesoporous carbon (MMC-Fe), guided by a composite of resol and triblock copolymer. With the combination of MMC-Fe, high-throughput mass spectrometry, and a simple machine learning algorithm, we extracted N-glycan profiles from various serum samples, including healthy controls, liver cirrhosis, and liver cancer, and from which we screened specific N-glycans. Specifically, the selected N-glycans demonstrate exceptional performance with area under the curve (AUC) values ranging from 0.948 to 0.993 for the detection of liver diseases, including alpha fetoprotein (AFP) -negative liver cancer. Among them, five N-glycans holds potential in monitoring distinctions between liver cirrhosis and AFP-negative liver cancer (AUC values of 0.827-0.842). This study is expected to promote the glycan-based precise monitoring of diseases, not limited to liver disease.
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关键词
N-Glycan,Mesoporous Carbon,MALDI-TOF MS,Machine learning model,Liver disease
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