Objective To study the clinical efficacy of computer-assisted stereotactic brain transplantation of human retinal pigment epithelium (hRPE) cells into the patients with Parkinson disease (PD). Methods Under the guidance of computed X-ray tomography and magnetic resonance imaging image mergence, 4 × 106 hRPE cells were transplanted into the putamen and ventriculus laterlis of 17 cases of PD by stereotactic surgery. The transplantation sites were contralateral to the side of main symptoms and signs. The curative efficacy were observed at the 7th day, 1st month, and 3rd month after the transplantation. Results The contralateral symptoms were ameliorated continuously after the transplantation. Three months after the surgery, the total effective rate of cell transplantation was 88. 2 %, and 82. 4 % of the cases got significant improvement. The cases that got ipsilateral improvement soon after the surgery gave a total effective rate as high as 88. 2 % at the 3rd month during follow-up period, and 64. 7% among these cases improved significantly. Only a minority of cases had transient dizziness and hemiparesis, but the duration was short. Conclusion The therapy, computer-assisted stereotactic transplantation of hRPE ceils in the treatment of PD, is safe and efficient.

Objective:To propose a model using the maximum intensity projection (MIP) of lung field computed tomography (CT) images and deep convolution neural network (CNN) and explore its value in identifying chronic obstructive pulmonary disease (COPD).Methods:A total of 201 subjects were selected from the Second Hospital of Dalian Medical University from January 2010 to May 2021. All subjects were included according to the inclusion criteria and were divided into COPD group (101 cases) and healthy controls group (100 cases). Each patient underwent a high-resolution CT scan of the chest and pulmonary function test. First, the lung field was extracted from CT images and the intrapulmonary MIP images were acquired. Second, with these MIP images as input, the model for identifying COPD was constructed based on a modified residual network (ResNet). Finally, the influence of the number of residual blocks on the performance of the models was investigated. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the identification efficiency.Results:The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of ResNet26 was 76.1%, 76.2%, 76.0%, 76.2%, and 76.0%, respectively; and the AUC of the test was 0.855 (95% CI: 0.799-0.901). The accuracy, sensitivity, specificity, PPV, NPV of ResNet50 was 77.6%, 76.2%, 79.0%, 78.6%, and 76.7%, respectively; and the AUC of the test was 0.854 (95% CI: 0.797-0.900). The accuracy, sensitivity, specificity, PPV, NPV of ResNet26d was 82.1%, 83.2%, 81.0%, 81.6%, and 82.7%, respectively; and the AUC of the test was 0.885 (95% CI: 0.830-0.926). Conclusions:The COPD identification model via MIP images from CT images within the lung and deep CNN is successfully constructed and achieves accurate COPD identification. And it can provide an effective tool for COPD screening.