Objective To investigate the mutations of the gene in Chinese patients with X linked juvenile retinoschisis (XLRS), and to provide the genetic diagnosis and consultation of heredity for the patients and their families. Methods Genomic DNA was isolated from leukocytes of 29 male patients with XLRS, 38 female carriers and 100 normal controls (the patients and the carriers were from 12 families). All 6 exons of XLRS1 gene were amplified by polhism (SSCP) assay. The positions and types of XLRS1 gene mutations were determined by direct sequencing. Results Eleven different XLRS1 mutations were identified in these 12 families, including one frameshift mutation due to base loss of the first exon: c.22delT（L9CfsX20）, one nonsense mutation due to base loss of the first exon (Trp163X), one splice donor site mutation(c.52+2 Trarr;C; IVS1+2T to C), and eight missense mutation due to base replacement（Ser73Pro, Arg102Gln, Asp145His, Arg156Gly, Arg200Cys, Arg209His, Arg213Gln, and Cys223Arg). No gene mutation was detected in the control group. Four new mutations included frmaeshift mutation（L9CfsX20）and mutations of Asp145His, Arg156Gly, and Trp163X at the fifth exon. A newly discovered non-disease-related polymorphism (NSP) was the c.576C to T (Pro192Pro) change at the sixth exon. Conclusion Eleven different XLRS1 mutations were detected, which is the cause of XLRS in Chinese people. The detection of gene mutations may provide the guidance of genetic diagnosis and the consultation of family heredity for the patients and their families. (Chin J Ocul Fundus Dis, 2006, 22: 77-81)
To address the needs of geometric modeling and hemodynamic analysis in the noninvasive functional assessment of coronary heart disease, this study establishes a deep learning-based integrated framework for coronary artery segmentation, three-dimensional reconstruction, and hemodynamic analysis. The nnU-Net model was used to achieve automatic coronary artery segmentation, and point cloud modeling techniques based on the Point Cloud Library (PCL) were further applied to construct patient-specific coronary artery models. Based on the reconstructed models, coronary hemodynamic numerical simulations were performed to analyze the variation characteristics of key indicators, including the blood flow velocity field, fractional flow reserve derived from computed tomography (CT-FFR), and wall shear stress (WSS), under different degrees of coronary artery stenosis. The results showed that, with increasing stenosis severity, local flow acceleration and high-velocity jet flow in the stenotic segment were enhanced, while the pressure distal to the stenosis decreased, leading to a gradual reduction in CT-FFR. Meanwhile, WSS increased in the stenotic region and adjacent vessel walls, and the high-WSS region expanded. These results indicate that the combination of machine learning-based three-dimensional reconstruction and hemodynamic analysis can help compensate for the limitations of conventional CT imaging in functional assessment, providing a numerical simulation basis for the noninvasive functional evaluation of coronary artery stenosis.