Researchers report an accurate, high-throughput, low-cost, and noninvasive method for diagnosing oral squamous cell carcinoma (OSCC) that uses a combination of conductive polymer spray ionization mass spectrometry and machine learning to detect changes in a wide range of OSCC-associated metabolites in saliva; the method was able to analyze saliva samples from 373 volunteers in 4.5 hours with a diagnostic accuracy of 86.7%, suggesting that the method might be used as a point-of-care-test for OSCC, according to the authors.
Article #20-01395: "Oral squamous cell carcinoma diagnosed from saliva metabolic profiling," by Xiaowei Song et al.
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Proceedings of the National Academy of Sciences