Monitoring Retinoblastoma by Machine Learning of Aqueous Humor Metabolic Fingerprinting (Small Methods 1/2022). Issue 1 (18th January 2022)

• Record Type: Journal Article
• Authors: Liu, Wanshan; Luo, Yingxiu; Dai, Jingjing; Yang, Ludi; Huang, Lin; Wang, Ruimin; Chen, Wei; Huang, Yida; Sun, Shiyu; Cao, Jing; Wu, Jiao; Han, Minglei; Fan, Jiayan; He, Mengjia; Qian, Kun; Fan, Xianqun; Jia, Renbing
• Is Part Of: Small methods. Volume 6:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-18
• Subjects: aqueous humor -- biomarkers -- mass spectrometry -- metabolic fingerprinting -- retinoblastoma; Nanotechnology -- Methodology -- Periodicals; Nanotechnology -- Periodicals; Periodicals; 620.5028
• DOI: 10.1002/smtd.202270005
• Languages: English
• ISSNs: 2366-9608

Abstract

Abstract : Advanced Metabolic Analysis In article number 2101220, Qian, Fan, Jia, and co‐workers have developed a nanoparticle enhanced laser desorption/ionization mass spectrometry platform to record aqueous humor metabolic fingerprinting (AH‐MF) of retinoblastoma (RB) with high performance. Early and advanced RB patients are differentiated by machine learning of AH‐MF and identified biomarkers with area‐under‐the‐curve over 0.9 and accuracy over 80%. This work will contribute to advanced metabolic analysis of eye diseases.