title: [Diagnostic prediction of early silicosis patients using neural network and MALDI-TOF-MS in serum].
abstract: Serum of 79 workers exposed to silica and 25 healthy controls cases were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). 7 protein peaks were selected and used by artificial neural network (ANN) to establish a diagnostic model. A blinded test showed that accuracy, sensitivity and specificity were 91.35%, 93.69%, and 84.52%, respectively. The diagnostic pattern was also established to distinguish each stage of silica-exposed population. The diagnostic pattern worked excellently with 89.23%, 94.20% and 92.37% of accurate rate for classifying phase 0, phase 0+, and phase I of silicosis, respectively.
authors.affiliation: Key Laboratory of Medical Diagnostics of Ministry of Education, Faculty of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China.
authors.forename: Qingbo
authors.initials: Q
authors.lastname: Ma
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.forename: Shixin
authors.initials: S
authors.lastname: Wang
authors.forename: Hua
authors.initials: H
authors.lastname: Xiang
mesh_terms: D015415:Biomarkers / Q000097:blood*; D001798:Blood Proteins / Q000032:analysis; D006801:Humans; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D012829:Silicosis / Q000097:blood / Q000145:classification / Q000175:diagnosis*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
chemical_list: D015415:Biomarkers; D001798:Blood Proteins