目的 檢測Claudin-1蛋白在非小細胞肺癌（NSCLC）原發癌組織及其淋巴結轉移癌組織中的表達，并探討其與NSCLC各臨床病理特征和預后的關系。 方法 利用組織芯片技術，采用免疫組織化學染色方法檢測Claudin-1在1998年1月－2003年12月收集的279例NSCLC原發癌組織及其55例淋巴結轉移癌組織、54例癌旁正常肺組織中的表達。運用SPSS 13.00統計軟件對相關數據進行統計分析。 結果 Claudin-1在279例NSCLC原發癌組織和55例淋巴結轉移癌組織中的陽性表達率分別為69.9%和50.9%，在鱗癌和腺癌中的陽性表達率分別為83.7%和55.7%，在高、中分化癌組和低分化癌組中的陽性表達率分別為78.6%和62.7%。Claudin-1在原發癌組織中的陽性表達率高于其淋巴結轉移癌組織（P＜0.05）；在鱗癌中的陽性表達率高于腺癌（P＜0.05），且在高、中分化癌組中的陽性表達率高于低分化癌（P＜0.05）。Claudin-1陽性表達之NSCLC患者的生存率高于陰性表達者（P＜0.05）。 結論 Claudin-1在NSCLC中的表達與患者的組織學類型及病理分級有關，且Claudin-1是NSCLC侵襲、轉移的抑制因子，并且可能是一個有用的預后因子。
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.