Objective To develop a diagnosis model for active pulmonary tuberculosis. Methods The proteomic fingerprinting of 264 sera from active tuberculosis patients and controls were analyzed using the surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) and protein-chip technology. The peaks were detected and filtrated by Ciphergen PrnteinChip(R) Software (version 3.1.1). Using the Biomarker Pattern 5.0 software, a diagnostic model was developed for diagnosis of active tuberculosis. Re-sults Fifty protein peaks were significantly different between the patients with active pulmonary tuberculosis and the controls with overlapping clinical features (P<0.01). Five protein peaks at 4360, 3311, 8160, 5723, 15173 m/z were chosen for the system classifier and the development of diagnosis model 1. The model differenti-ated the patients with active pulmonary tuberculosis from the controls with a sensitivity of 83.0%, and a speci-ficity of 89.6%. The diagnostic accuracy was up to 86.4%. Three protein peaks at 5643, 4486, 4360 m/z were chosen for the system classifier and the development of diagnosis model 2. The model differentiated the pa-tients with active pulmonary tuberculosis from the controls with a sensitivity of 96.9%, and a specificity of 97.8%. The diagnostic accuracy was up to 97.3%. Conclusion It might be a new diagnostic test for the de-tection of sera from the patients with active pulmonary tuberculosis using SELDI-TOF-MS and protein chip.

Objective:To explore eye movement characteristics in newly diagnosed, drug-naive Parkinson′s disease (PD) patients and their correlation with motor and non-motor symptoms.Methods:Seventy-five newly diagnosed, drug-naive PD patients and 46 healthy controls (HCs) were included in this cross-sectional study. Patients were recruited from the Department of Neurology, Shanghai Ninth People′s Hospital, Shanghai Jiao Tong University School of Medicine from November 2017 to December 2021, while HCs were recruited from the local community during the same period. For PD patients, motor severity was measured with the modified Hoehn and Yahr stage, Movement Disorder Society Unified Parkinson′s Disease Rating Scale part Ⅲ and the Freezing of Gait questionnaire. Non-motor symptoms were evaluated by serial scales such as Non-Motor Symptoms Questionnaire, 16-item odor identification test from Sniffin Sticks, 17-item Hamilton Rating Scale for Depression, Chinese version of Mini-Mental State Examination, Montreal Cognitive Assessment Basic and REM Behavior Disorder Screening Questionnaire. All subjects underwent oculomotor test including pro-saccade task and smooth pursuit eye movement (SPEM) task in the horizontal direction via videonystagmography. Visually guided saccade latency, saccadic accuracy and gain in SPEM at three frequencies (0.1, 0.2, 0.4 Hz) of the horizontal axis were compared between the 2 groups. The association between key oculomotor parameters and clinical phenotypes was explored in PD patients. The receiver operating characteristic (ROC) analyses of eye movement parameters as independent factors were also performed for detecting PD from HCs, then combining the saccadic latency, saccadic accuracy and the most significant SPEM gain (0.4 Hz) as the model to distinguish PD from HCs.Results:Relative to HCs, newly diagnosed, drug-naive PD patients showed prolonged saccadic latency [(210.4±41.3) ms vs (191.3±18.9) ms, t=-3.445, P=0.001] and decreased saccadic accuracy (88.4%±6.8% vs 92.2%±6.1%, t=3.064, P=0.003). SPEM gain in PD was uniformly reduced at each frequency(0.1 Hz: 0.68±0.15 vs 0.74±0.14, t=2.261, P=0.026; 0.2 Hz: 0.72±0.16 vs 0.79±0.16, t=2.704, P=0.008; 0.4 Hz: 0.67±0.19 vs 0.78±0.19, t=2.937, P=0.004). The ROC analyses of saccade latency, saccadic accuracy and gain in SPEM at 0.1, 0.2, 0.4 Hz as independent factors for detecting PD from HCs showed that the area under the curve (AUC) of each parameter was lower than 0.7: the AUC of saccade latency was 0.641 ( P=0.010), the AUC of saccadic accuracy was 0.681 ( P=0.001), the AUC of gain in SPEM at 0.1 Hz was 0.616 ( P=0.032), at 0.2 Hz was 0.652 ( P=0.005), at 0.4 Hz was 0.660 ( P=0.003). Combining the saccadic latency, saccadic accuracy and the most significant SPEM gain (0.4 Hz) revealed that the model could significantly distinguish PD from HCs with an 80.4% sensitivity and a 73.3% specificity (AUC=0.780, P<0.001). Prolonged saccadic latency was correlated with long disease duration ( β=0.334, 95% CI 0.014-0.654, P=0.041), whereas decreased SPEM gain was associated with severe motor symptoms in newly diagnosed drug-naive PD patients (0.1 Hz: β=-0.004, 95% CI -0.008--0.001, P=0.036; 0.4 Hz: β=-0.006, 95% CI -0.011--0.001, P=0.012). Conclusions:Ocular movements are impaired in newly diagnosed, drug-naive PD patients. These changes could be indicators for disease progression in PD.