Objective To obtain the heavy chain variable region (VH) gene of monoclonal antibody 2F2 against Japanese encephalitis virus (JEV).Methods Total RNA was isolated with Trizol from hybridoma 2F2 cells,and cDNA of VH was amplified with reverse transcription polymerase chain reaction (RT-PCR) and sequenced.The putative VH gene was expressed in E.coli,and the expressed products was detected with enzyme linked immunosorbent assay (ELISA) to determine the activity to bind JEV.Results The VH gene was 354 bp in length which encodes 118 amino acids.Tricine-sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) showed that the VH gene was successfully expressed and purified from inclusion bodies.ELISA result also demonstrated that VH gene expressed products bind purified JEV.Conclusions The VH gene of monoclonal antibody 2F2 against JEV had been cloned.

Aim To study the mimicry peptide of JEV E protein by screening a phage 15-mer peptide library with anti-JEV E protein mAb 2H4. Methods After three rounds biopanning, the enriched positive phage clones were identified by ELISA. 10 positive phage clones were sequenced and compared homologously with JEV E protein. Results The short peptide displayed on screened positive phage could bind specifically to mAb 2H4, and the binding could be inhibited by natural JEV Ag. Amino acid sequences of the 10 positive phage clones were consensus, that is, RQDPQWPYANSTIAR. By homology analysis, two higher homologous sequences STXAR and WXXAXST were found in different regions of JEV E protein. The peptide displayed on positive phage could react specifically with the mouse antiserum against natural JEV Ag . Conclusion This peptide displayed on positive phage may mimic partial antigenicity of JEV E protein.

Objective:To strengthen the integration and sharing of medical data resources, provide high-quality and usable data for clinical researchers, and promote the medical data use in clinical research.Methods:According to the development and application goals of the medical big data platform, data from different major clinical information systems in our hospital are integrated and then cleaned, processed and analyzed, and finally aggregated into a unified platform and turned to valuable and usable data resources.Results:A medical big data platform for clinical research in our hospital has been developed. It has stored over 13.42 million patients′ clinical data of more than 50 million visits since 2004 in our hospital; an analysis-oriented common data model (CDM) for clinical research has been designed; clinical researchers can query and extract clinical data according to CDM; the standard clinical research data service mechanisms have been established.Conclusions:The medical big data platform in our hospital helps to provide usable data of good quality for high-level scientific research based on medical big data, and improve the efficiency and quality of clinical research; at the same time, it also provides a efficient way to manage and control clinical research data use while ensuring data security and regulatory compliance.