Objective:Based on UPLC-Q-TOF/MS, to screen a panel of plasma metabolite biomarkers for TB diagnosis and evaluate its diagnostic efficacy.Methods:102 active TB patients [49 males, 53 females, age 40.0(24.0, 48.5) years] from Shanghai Pulmonary Hospital and Shanghai East Hospital from January 2017 to January 2018, 100 TB-IGRA positive patients [55 males, 45 females, age 44.0(37.0, 52.0) years] and 96 healthy controls [55 males, 41 females, age 43.0(32.2, 52.8) years] from Shanghai East Hospital were randomly enrolled. UPLC-Q-TOF/MS technology was used to detect small molecule metabolites in plasma. Combined with multivariate statistical methods VIP and univariate statistic analysis Student's t-test, the main differential metabolites in the plasma of patients with active tuberculosis were filtered. The ROC curve was analyzed for the differential metabolites, and the AUC value, specificity, and sensitivity for diagnosis were used to screen metabolic biomarkers with diagnostic potential. Results:All the samples examined resulted in 10 266 variables, and 1 153 substances were identified by qualitative retrieval through the human metabolome database. After pairwise comparison of samples from the three groups, differential metabolites that simultaneously satisfied VIP > 1 and P<0.05 were plotted into a Venn diagram, and the resulting intersection set contained 38 major differential metabolites. The ROC curve analysis of 38 major metabolites showed that the area under the curve of lactic acid, dopamine, 9-pentadecenoic acid, and 12,13-dihydroxy octadecadienoic acid in the diagnosis of active tuberculosis were 0.92, 0.98, 0.94, and 0.94, respectively, the specificity was both more than 90% and the sensitivity was both more than 80%. The specificity and sensitivity of four metabolites in the combined diagnosis of active tuberculosis were both 94%. Conclusion:Lactic acid, dopamine, 9-pentadecenoic acid, and 12, 13-dihydroxy octadecadienoic acid can be used as potential metabolic biomarkers for tuberculosis diagnosis.

The accurate position of the center of rotation (COR) is a key factor to ensure the quality of computed tomography (CT) reconstructed images. The classic cross-correlation matching algorithm can not satisfy the requirements of high-quality CT imaging when the projection angle is 0 and 180°, and thus needs to be improved and innovated. In this study, considering the symmetric characteristic of the 0° and flipped 180° projection data in sinogram, a novel COR correction algorithm based on the translation and match of the 0° and 180° projection data was proposed. The OTSU method was applied to reduce noise on the background, and the minimum offset of COR was quantified using the -norm, and then a precise COR was obtained for the image correction and reconstruction. The Sheep-Logan simulation model with random gradients and Gaussian noise and the real male SD rats samples which contained the heterogenous tooth image and the homogenous liver image, were adopted to verify the performance of the new algorithm and the cross-correlation matching algorithm. The results show that the proposed algorithm has better robustness and higher accuracy of the correction (when the sampled data is from 10% to 50% of the full projection data, the COR value can still be measured accurately using the proposed algorithm) with less computational burden compared with the cross-correlation matching algorithm, and it is able to significantly improve the quality of the reconstructed images.