Objective To study the comparability of total prostate specific antigen ( tPSA) meas-urement by four domestic chemiluminescence immunoassays ( DCI) and electrochemiluminescence immuno-assay ( ECI) . Methods A total of 45 serum samples that requested tPSA tests were selected. Four DCIs ( Snibe MAGLUMI 4000, Mindray CL-2000i, Autobio A2000, HYBIOME AE180) and ECI ( Roche Cobas e601) were used to measure tPSA. The precisions of the methods were evaluated. The four DCIs were com-pared with Roche ECI respectively, and the comparability of the test results was analyzed. Wilcoxon signed rank test and Spearman correlation analysis were used to analyze the data. Results The precisions of five methods were good. The tPSA levels measured by Roche Cobas e601, Snibe MAGLUMI 4000, Mindray CL-2000i, Autobio A2000, and HYBIOME AE180 were 14.11(9.92, 36.09), 12.00(8.56, 27.23), 12.10 (8. 60, 29.87), 13.35(9.51, 32.85) and 14.50(9.88, 40.06) μg/L, respectively. The correlation coeffi-cients of Roche with Snibe MAGLUMI 4000, Mindray CL-2000i, and Autobio A2000 were 0.992, 0.989, 0. 957 and 0.983, respectively (all P<0.001). Assuming the tPSA medical decision point for regression equation was 4.0μg/L, the proportional biases of Snibe MAGLUMI 4000, Mindray CL-2000i, Autobio A2000, and HYBIOME AE180 compared with Roche were -10. 88％, -18. 07％, 0. 23％ and 22. 31％, respectively. Conclusion The comparability of tPSA test results is different between 4 DCIs and Roche ECI, which pro-vides some references for clinical application and standardization of the DCI test results.

In order to diagnose and evaluate the human spinal lesions through the paravertebral muscles, a paravertebral muscle monitoring system based on surface EMG signals was designed. The system used surface mount electrodes to obtain the surface myoelectric signal (sEMG) of paravertebral muscle. The signal was filtered and amplified by the conditioning circuit. The signal was collected by the microcontroller NRF52832 and was sent to the mobile APP. After the signal was preprocessed by the wavelet threshold denoising algorithm in APP, the time and frequency characteristics of the sEMG signal reflecting the functional state of the muscle were extracted. The calculated characteristic parameters was displayed in real time in the application interface. The experimental results show that the system meets the design requirements in analog signal acquisition, digital processing of signals and calculation of characteristic parameters. The system has certain application value.
Algorithms ; Computers ; Electrodes ; Electromyography ; instrumentation ; Humans ; Monitoring, Physiologic ; Muscle, Skeletal ; Signal Processing, Computer-Assisted

OBJECTIVE: To design a rapid scoliosis detection system for general survey. METHODS: The camera was used to take the upright image of human back, and then the region of interest was extracted. After image preprocessing, the feature points of human back spine were extracted. The feature points were fitted into the spine contour curve. Finally, the Cobb angle of scoliosis was calculated according to the contour curve, and the scoliosis degree was judged. RESULTS: The outline curve of the spine can be obtained by this method, and the Cobb angle of scoliosis can be calculated. It can detect scoliosis quickly, effectively and accurately. CONCLUSIONS Compared with the traditional methods, the digital image processing method can achieve rapid and non-destructive detection of scoliosis, save a lot of manpower and material resources, and is of great significance to the national survey of adolescent scoliosis.
Adolescent ; Humans ; Image Processing, Computer-Assisted ; Reproducibility of Results ; Scoliosis ; Spine ; diagnostic imaging ; Surveys and Questionnaires

Objective : To evaluate the prognostic value of a radiomics model based on the peritumoral region of gli- oma． Methods : 138 patients with glioma were retrospectively analyzed ，medical imaging interaction toolkit ( MITK) software was used to obtain the magnetic resonance imaging (MRI) images of peritumoral area 5 mm，10 mm and 20 mm from the tumor edge and extract texture features．The texture features were screened the radiomics model was established and the radiomic score was calculated．A clinical prediction model and a combined predic- tion model along with Rad-score and clinical risk factors were established．The combined prediction model was dis- played as a nomogram，and the predictive performance of the model for survival in glioma patients was evaluated. Results : In the validation set，the C-index value of the radiomics model based on the peritumoral region 10 mm a- way from the tumor edge based on T2 weighted image (T2WI) images was 0. 663 (95% CI = 0. 72－0. 78) ，resul- ting in the best prediction performance．On the training set and validation set，the C-index of the nomogram was 0. 770 and 0. 730，respectively，indicating that the prediction performance of nomogram was better than those of the radiomics model and clinical prediction model．The model had the highest prediction effect on the 3-year survival rate of glioma patients (training set area under curve (AUC) = 0. 93，95% CI = 0. 83 － 0. 98 ; validation set AUC = 0. 88，95% CI = 0. 76 －0. 99) ．The calibration curve showed that the joint prediction nomogram in both the training set and the validation set had good performance． Conclusion The combined prediction model based on the preoperative T2WI images in the peritumoral region 10 mm from the tumor edge and the clinicopathological risk factors can accurately predict the prognosis of glioma，providing the best effect of prediction on the 3-year survival rate of glioma.