title: Exploring serological classification tree model of active pulmonary tuberculosis by magnetic beads pretreatment and MALDI-TOF MS analysis.
abstract: Pulmonary tuberculosis (TB) is an infectious disease disturbing status of public health, and accurate diagnosis of TB would effectively help control the disturbance. Our study tried to establish a classification tree model that distinguished active TB from non-TB individuals. We used matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) combined with weak cationic exchange (WCX) magnetic beads to analyse 178 serum samples containing 75 patients with active TB and 103 non-TB individuals (43 patients with common pulmonary diseases and 60 healthy controls). Samples were randomly divided into a training set and a test set. Statistical softwares were applied to construct this model. An amount of 48 differential expressed peaks (P < 0.05) were identified by the training set, and our model was set up by three of them, m/z 7626, 8561 and 8608. This model can discriminate patients with active TB from patients with non-TB with a sensitivity of 98.3% and a specificity of 84.4%. The test set was used to verify the performance, which demonstrated good sensitivity and specificity: 85.7% and 83.3%, respectively. Differential expressed peaks between smear-positive and smear-negative active TB also have been analysed. It came out that m/z 8561 and 8608 not only acted as vital factors in the pathogenesis of active TB but also played an important role in regulating different active TB status. In conclusion, MALDI-TOF MS combined with WCX magnetic beads was a powerful technology for constructing classification tree model, and the model we built could serve as a potential diagnostic tool for active TB.
authors.affiliation: Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: C
authors.initials: C
authors.lastname: Deng
authors.forename: M
authors.initials: M
authors.lastname: Lin
authors.forename: C
authors.initials: C
authors.lastname: Hu
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.forename: Y
authors.initials: Y
authors.lastname: Gao
authors.forename: X
authors.initials: X
authors.lastname: Cheng
authors.forename: F
authors.initials: F
authors.lastname: Zhang
authors.forename: M
authors.initials: M
authors.lastname: Dong
authors.forename: Y
authors.initials: Y
authors.lastname: Li
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000097:blood*; D005260:Female; D006801:Humans; D018189:Immunomagnetic Separation / Q000379:methods*; D008168:Lung / Q000382:microbiology / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009169:Mycobacterium tuberculosis; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D014397:Tuberculosis, Pulmonary / Q000175:diagnosis* / Q000382:microbiology / Q000473:pathology; D055815:Young Adult
chemical_list: D015415:Biomarkers