摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-中枢神经系统疾病和状况的新研究-癫痫是一篇报道的主题。据《新闻周刊》编辑在上海报道，Rese Arch称：“癫痫是一种慢性神经系统疾病，对世界范围内的公众健康和疾病负担都是很大的威胁。需要开发高性能的癫痫诊断工具，以提高诊断的准确性和效率，但仍然缺乏。”我们的新闻记者引用了上海交通大学的研究，“在此，我们利用纳米粒子增强激光解吸电离质谱(NELDI MS)从癫痫患者和健康人中获得血浆代谢指纹图谱(PMFs)，以及时、准确地筛选EPIL EPSY。NELDI MS使检测速度高(每个样品30s)，PUT高(每次运行384个样品)，通过对PMFs的机器学习，建立了癫痫诊断模型，验证集曲线下面积(AUC)V值为0.941，并鉴定出4种代谢产物为癫痫诊断标志物组，AUC值为0.812-0.860.
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions - Epilepsy is the subject of a report. According to new s reporting out of Shanghai, People's Republic of China, by NewsRx editors, rese arch stated, "Epilepsy is a chronic neurological disorder that causes a major th reat to public health and the burden of disease worldwide. High-performance diag nostic tools for epilepsy need to be developed to improve diagnostic accuracy an d efficiency while still missing." Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, "Herein, we utilized nanoparticle-enhanced laser desorption/ionizati on mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epil epsy. The NELDI MS enabled high detection speed ( 30 s per sample), high through put (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMF s. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) v alue of 0.941 for the validation set. Furthermore, four metabolites were identif ied as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.8 60."
NETL
NSTL