Objective To observe the inhibitory effects of gene transfer of canstatin on retinal neovascularization in mice. Methods Fifty-six 7-day-old C57BL/6J mice were randomly divided into control group,oxygen-induced retinopathy (OIR) group, empty vector group and treated group,14 mices in each group. Except for the control group,the mice in the other groups were exposed to (75plusmn;2)% oxygen for 5 days and then back to the normal air to establish the model of OIR. On postnatal 12 day, the treated group was received intravitreal injection of canstatin pCMV-HA, while the empty vector group was received the same volume of empty plasmid.The changes of retinal vessels were observed by Evans blue angiography on postnatal 17 day. With parafin section which stained by hematoxylin and eosin, then the number of endotheliocyte nuclei breaking throuhgh the internal limiting membrane（ILM） was observed and counted by optical microscope.Results Retinal blood vessels distributed regularly in treated group compared with OIR group and empty vector group.The differences of the number of endotheliocyte nuclei breaking throuhgh ILM in treated group was significant compared with the other two groups（F=39.006，Plt;0.001).Conclusion The canstatin pCMV-HA can effectively inhibit the retinalneovascularization in OIR.
目的 探讨急性百草枯(PQ)中毒鼠肺组织病理损伤和肺组织血红素氧合酶-1（HO-1）的表达及三七总皂甙（PNS）的保护作用。 方法 150只SD雄性鼠分为正常对照组（C组）30只、PQ中毒组（PQ组）60只及PNS组60只。PQ组和PNS组一次性灌胃PQ 25 mg/kg染毒，C组给予等体积生理盐水灌胃。其中PNS组于染毒前15 min以PNS 50 mg/kg阴茎静脉注射保护，以后1次/d给药直至处死前；PQ组、C组分别在同时间点给予等体积生理盐水。观察各组大鼠在中毒后6、12 h，1、3、5、7 d肺组织病理改变，采用蛋白质印迹法分析肺组织HO-1蛋白表达和反转录-聚合酶链反应方法测定鼠肺组织HO-1 mRNA的表达。 结果 C组HO-1蛋白和HO-1 mRNA绝大多数标本有弱表达，个别标本不表达；与C组相比PQ组及PNS组HO-1蛋白和HO-1 mRNA表达增强，差异有统计学意义（P＜0.05）；PQ组HO-1蛋白和HO-1 mRNA的表达在1 d达高峰之后下降，第3天基本恢复到C组水平；PNS组与PQ组相似，但在6 h、12 h、1 d及3 d高于PQ组，差异有统计学意义（P＜0.05），至第5天和第7天二者相比差异无统计学意义（P＜0.05）。PQ组肺组织病理损伤评分在6、12 h，1、3 、5、7 d各亚组均高于PNS相应组，差异有统计学意义（P＜0.05），C组肺组织病理大致正常，与PQ组及PNS组相比，差异有统计学意义（P＜0.001）。 结论 HO-1参与PQ中毒所致急性肺损伤，PNS对PQ中毒所致急性肺损伤有保护作用。
ObjectiveTo evaluate the feasibility and accuracy of a novel three dimensional (3D) preoperative simulation software in a clinical setting for patients undergoing precise hepatectomy. MethodsThe clinical data of 85 patients with hepatocellular carcinoma underwent precise hepatectomy were retrospectively studied. All the patients received CT screening and subsequent evaluation on the liver resection volume and margin and the percentage of resected tumor by 3D preoperative simulation software, which compared with the actual resection liver values. The operation plan was optimized by virtual hepatectomy. ResultsThe liver, tumor as well as blood vessel could be clearly showed and reconstructed by 3D preoperative simulation software. All the patients underwent precise hepatectomy. After operation ascites occurred in 3 patients on 2 d, moderate pleural effusion occurred in 2 patients on 2 d, and bile leakage appeared in 4 patients on 5 d, which were improved by conservative treatment. The length of stay in all patients ranged from 6 to 88 d (mean 23 d), and no recurrence and death occurred within 30 d of operation. The predicted resection liver volume was significantly correlated with the actual resection volume (r=0.960, Plt;0.001), and the difference between the mean volume of predicted and actual resection liver was not significant (896.7 ml vs. 819.1 ml， t=1.851, P=0.068). In addition, the predicted resection margin was also correlated with the actual resection margin (r=0.972, Plt;0.001), with the difference in the mean resection margin was not significant too (12.2 mm vs. 11.9 mm， t=1.143, P=0.256). No patients suffered from severe postoperative complications. ConclusionsThe 3D preoperative simulation software is able to evaluate and simulate liver resection accurately, which may contribute to a safe precise hepatectomy plan.
Large Language Models (LLMs) are highly sophisticated deep learning models pre-trained on massive datasets, with ChatGPT representing a prominent application of LLMs in the field of generative models. Since the release of ChatGPT at the end of 2022, generative chatbots have become widely employed across various medical disciplines. As a crucial discipline guiding clinical practices, the usage of generative chatbots like ChatGPT in Evidence-Based Medicine (EBM) is gradually increasing. However, the potential, challenges, and intricacies of their application in the domain of EBM remain unclear. This paper aims to explore and discuss the prospects, challenges, and considerations associated with the application of ChatGPT in the field of EBM through a review of relevant literature. The discussion spans four aspects: evidence generation, synthesis, assessment, dissemination, and implementation, providing researchers with insights into the latest developments and future research suggestions.