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.
Subject(s)
Humans , Biomarkers , Blood , Blood Proteins , Neural Networks, Computer , Sensitivity and Specificity , Silicosis , Blood , Classification , Diagnosis , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization , MethodsABSTRACT
The investigation confirms that 44.4% preoperative diagnosis was gained on 18 cases of primary small intestinal tumor, especially, a better understanding about the significances in using modern detective methods, such as B-ultrasonography, CT and X-ray, etc. in the diagnosis of small intestinal tumor was achieved. The clinical and pathological characteristics of primary small intestinal tumor were studied and the therapies were further discussed in de- tail based on modern surgery theories and techniques.