Production and Storage of Virus Simulants

We have examined isolation and identification protocols for three virus simulant candidates to biological warfare agents. MS2 phage, a simulant for yellow fever virus and Hantaan virus, was propagated using as a host an E. coli strain with F pilus. MS2 phage genome was examined by reverse transcription and polymerase chain reaction (RT-PCR). Coat protein of the phage preparation was examined by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and mass spectrometric analysis. Cydia pomonella granulosis virus (CpGV) is a virus simulant candidate to smallpox virus. CpGV was isolated from a commercialized CpGV pellet. In this study, we developed new isolation and identification protocols for CpGV. One disadvantage of using CpGV is that it is not easy to determine viability of the virus. Here, we have included T4 phage as an alternative. We established a high titer production protocol and developed an easy genome identification protocol that does not require purified phage DNA. Stability of these virus preparations was also examined under various storage conditions. When the virus preparations were not subjected to freeze drying, MS2 phage was most stable when it was stored in liquid nitrogen but unstable at 4℃. In contrast, T4 phage was most stable when it was stored at 4℃. CpGV was stable at −20℃ but not at 4℃. Stability during or after freeze drying was also investigated. The result showed that 70~80% MS2 survived the freeze drying process. In contrast, only about 15% of T4 phage survived during the freeze drying. CpGV was found to be degraded during freeze drying.

Discovery of Outpatient Care Process of a Tertiary University Hospital Using Process Mining / 대한의료정보학회지

OBJECTIVES: There is a need for effective processes in healthcare clinics, especially in tertiary hospitals, that consist of a set of complex steps for outpatient care, in order to provide high quality care and reduce the time cost. This study aimed to discover the potential of a process mining technique to determine an outpatient care process that can be utilized for further improvements. METHODS: The outpatient event log was defined, and the log data for a month was extracted from the hospital information system of a tertiary university hospital. That data was used in process mining to discover an outpatient care process model, and then the machine-driven model was compared with a domain expert-driven process model in terms of the accuracy of the matching rate. RESULTS: From a total of 698,158 event logs, the most frequent pattern was found to be "Consultation registration > Consultation > Consultation scheduling > Payment > Outside-hospital prescription printing" (11.05% from a total cases). The matching rate between the expert-driven process model and the machine-driven model was found to be approximately 89.01%, and most of the processes occurred with relative accuracy in accordance with the expert-driven process model. CONCLUSIONS: Knowledge regarding the process that occurs most frequently in the pattern is expected to be useful for hospital resource assignments. Through this research, we confirmed that process mining techniques can be applied in the healthcare area, and through detailed and customized analysis in the future, it can be expected to be used to improve actual outpatient care processes.