目的 检测Claudin-1蛋白在非小细胞肺癌（NSCLC）原发癌组织及其淋巴结转移癌组织中的表达，并探讨其与NSCLC各临床病理特征和预后的关系。 方法 利用组织芯片技术，采用免疫组织化学染色方法检测Claudin-1在1998年1月－2003年12月收集的279例NSCLC原发癌组织及其55例淋巴结转移癌组织、54例癌旁正常肺组织中的表达。运用SPSS 13.00统计软件对相关数据进行统计分析。 结果 Claudin-1在279例NSCLC原发癌组织和55例淋巴结转移癌组织中的阳性表达率分别为69.9%和50.9%，在鳞癌和腺癌中的阳性表达率分别为83.7%和55.7%，在高、中分化癌组和低分化癌组中的阳性表达率分别为78.6%和62.7%。Claudin-1在原发癌组织中的阳性表达率高于其淋巴结转移癌组织（P＜0.05）；在鳞癌中的阳性表达率高于腺癌（P＜0.05），且在高、中分化癌组中的阳性表达率高于低分化癌（P＜0.05）。Claudin-1阳性表达之NSCLC患者的生存率高于阴性表达者（P＜0.05）。 结论 Claudin-1在NSCLC中的表达与患者的组织学类型及病理分级有关，且Claudin-1是NSCLC侵袭、转移的抑制因子，并且可能是一个有用的预后因子。
The establishment of brain metabolic network is based on 18fluoro-deoxyglucose positron emission computed tomography (18F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.
Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.