首页|Southeast University Reports Findings in Machine Learning (A machine learning-dr iven SERS platform for precise detection and analysis of vascular calcification)
Southeast University Reports Findings in Machine Learning (A machine learning-dr iven SERS platform for precise detection and analysis of vascular calcification)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Nanjing, Peopl e’s Republic of China, by NewsRx journalists, research stated, “Vascular calcifi cation (VC) significantly increases the incidence and mortality rates of cardiov ascular diseases, severely threatening public health as a global issue. Currentl y, there are no effective methods to prevent and treat vascular calcification.” The news reporters obtained a quote from the research from Southeast University, “This study proposes a machine learning-assisted surface-enhanced Raman scatter ing (SERS) technique for label-free, highly sensitive analysis of VC rat serum. We prepared gold nanobipyramid (GNBP) substrates using seedmediated and liquid- liquid interface self-assembly methods and measured the SERS spectra of the seru m. The collected spectral data were processed using a Principal Component Analys is (PCA)-Linear Discriminant Analysis (LDA) model to achieve effective sample di fferentiation. In this analysis model, GNBP substrates enabled rapid, sensitive, and label-free serum spectral detection, achieving classification accuracy, sen sitivity, and specificity of 96.0%, and an AUC value of 0.98, signi ficantly outperforming currently used machine learning methods. By analyzing the PCA loading plots, key spectral features that distinguished VC were successfull y captured.”
NanjingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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