肝门部胆管癌的分型分期系统及其临床应用价值

肝门部胆管癌（hilar cholangiocarcinoma，HC）分型和分期系统较为庞杂，包括改良Bismuth-Corlette分型，Gazzaniga T分期，Blumgart T分期，美国纪念Sloan-Kattering癌症中心（Memorial Sloan Katering Cancer Center，MSKCC）T分期，Mayo分期系统，美国癌症联合委员会（American Joint Committee on Cancer，AJCC）和国际抗癌联盟（International Union Against Cancer，UICC）联合发布的TNM分期，欧洲肝胆胰协会分期（Consensus classification from the European Hepato-Pancreato-Biliary Association，EHPBA），以及日本肝胆胰外科学会（Japanese Society of Hepato-Biliary-Pancreatic Surgery，JSHBPS）分期，各分期系统所包含的内容差异较大，各具特色和优缺点，本文就HC常见的分型和分期系统及其在临床中的应用价值做一综述。     

输尿管支架管结壳患者尿液菌群的分布特点

目的探讨输尿管支架管结壳患者尿液菌群的分布特点。方法选取2018年10月至2019年3月在山东省立第三医院、山东大学齐鲁医院、济南市中心医院和济南市济钢医院就诊的35例输尿管支架管置入术后患者。纳入标准:年龄18~65岁;输尿管镜碎石术后留置内支架管4周。排除标准:尿液细菌培养阳性;严重肉眼血尿;近期口服抗生素;存在明显残石患者。本研究采用横断面研究方法(临床研究注册号为ChiCTR1800020025),根据有无支架管结壳将患者分为结壳组23例和无结壳组12例。收集拔管当日患者尿液行细菌16s DNA检测。使用UPARSE、UCHIME和RDP calssifier等软件分析两组患者尿液菌群分布特点,明确两组患者尿液中细菌种类总数、细菌丰度,以及丰度占比较大的细菌类别,比较两组患者尿液细菌种类、数量及细菌丰度的差异,明确结壳组患者尿液中丰度占比较大的细菌菌属。结果两组患者的年龄、性别、体质指数、置管侧别、内支架管型号及结石成分差异均无统计学意义(P>0.05)。16s DNA检测结果显示,结壳组丰度占比>1%的菌属数量为11个,丰度占比>0.01%的菌属数量为74个;无结壳组丰度占比>1%的菌属数量为7个,丰度占比>0.01%的菌属数量为11个,两组丰度占比>1%的菌属数量比较差异有统计学意义(t=5.12,P=0.000)。结壳组中菌属丰度占比前3位分别为乳杆菌属(23.1%)、拟杆菌属(18.8%)和未分级拟杆菌属(17.1%),非结壳组中菌属丰度占比前3位分别是为埃希菌-志贺菌属(32.2%)、肠球菌属(24.9%)和假单胞菌属(18.2%)。两组间差异最大的3种细菌是乳杆菌属(P=0.010),拟杆菌属(P=0.004)和未分级拟杆菌属(P=0.004)。结论支架管结壳患者尿液中细菌种类和数量都明显多于非支架管结壳患者。拟杆菌属细菌在支架管结壳患者尿液中的细菌种类丰度较大。     

Binding mode of conformations and structure-based pharmacophore development for farnesyltransferase inhibitors

Farnesyltransferase (FTase) is one of the prenyltransferase family enzymes that catalyse the transfer of 15-membered isoprenoid (farnesyl) moiety to the cysteine of CAAX motif-containing proteins including Rho and Ras family of G proteins. Inhibitors of FTase act as drugs for cancer, malaria, progeria and other diseases. In the present investigation, we have developed two structure-based pharmacophore models from protein–ligand complex (3E33 and 3E37) obtained from the protein data bank. Molecular dynamics (MD) simulations were performed on the complexes, and different conformers of the same complex were generated. These conformers were undergone protein–ligand interaction fingerprint (PLIF) analysis, and the fingerprint bits have been used for structure-based pharmacophore model development. The PLIF results showed that Lys164, Tyr166, TrpB106 and TyrB361 are the major interacting residues in both the complexes. The RMSD and RMSF analyses on the MD-simulated systems showed that the absence of FPP in the complex 3E37 has significant effect in the conformational changes of the ligands. During this conformational change, some interactions between the protein and the ligands are lost, but regained after some simulations (after 2 ns). The structure-based pharmacophore models showed that the hydrophobic and acceptor contours are predominantly present in the models. The pharmacophore models were validated using reference compounds, which significantly identified as HITs with smaller RMSD values. The developed structure-based pharmacophore models are significant, and the methodology used in this study is novel from the existing methods (the original X-ray crystallographic coordination of the ligands is used for the model building). In our study, along with the original coordination of the ligand, different conformers of the same complex (protein–ligand) are used. It concluded that the developed methodology is significant for the virtual screening of novel molecules on different targets.