Improving Detection Accuracy of XXXX Serum Proteomic Profiling via Two-Stage Training Process.

@entity1 @entity32 @entity5 @entity2 @entity5475
BACKGROUND: Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for @entity5 biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant @entity32 . This study aimed to increase detection accuracy using a two-stage analysis process. METHODS: Sera from 118 @entity2 @entity1 , 72 healthy individuals, and 31 @entity1 with @entity32 were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish @entity2 @entity1 from sera was used. The two-stage analysis of distinguishing the healthy @entity1 and non-healthy @entity1 (1st-stage) and then differentiating @entity5 @entity1 from @entity32 @entity1 (2nd-stage) to minimize the influence of @entity32 was validated in the test group. RESULTS: In the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be @entity5475 ( @entity5475 ) due to the similar m/z countered around this area. This hypothesis was further tested using an @entity5475 ELISA assay. CONCLUSIONS: Inflammatory disease can severely interfere with the detection accuracy of SELDI profiles for @entity2 . Using a two-stage training process will improve the specificity and accuracy of detecting @entity2 .
