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RISS 인기검색어
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- 저자 : Jun Uemura (검색결과 3 건)
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Imanishi, Toshiaki,Hanai, Taizo,Aoyagi, Ichiro,Uemura, Jun,Araki, Katsuhiro,Yoshimoto, Hiroshi,Harima, Takeshi,Honda , Hiroyuki,Kobayashi, Takeshi The Korean Society for Biotechnology and Bioengine 2002 Biotechnology and Bioprocess Engineering Vol.7 No.5
In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.
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Watanabe Shinichi,Iida Yuki,Hirasawa Jun,Naito Yuji,Mizutani Motoki,Uemura Akihiro,Nishimura Shogo,Suzuki Keisuke,Morita Yasunari 대한재활의학회 2023 Annals of Rehabilitation Medicine Vol.47 No.3
Objective: To investigate the effect on early mobilization in patients undergoing extra-corporeal membrane oxygenation (ECMO) and acute blood purification therapy in the intensive care unit (ICU).Methods: We conducted this multicenter retrospective cohort study by collecting data from six ICUs in Japan. Consecutive patients who were admitted to the ICU, aged ≥18 years, and received mechanical ventilation for >48 hours were eligible. The analyzed were divided into two groups: ECMO/blood purification or control group. Clinical outcomes; time to first mobilization, number of total ICU rehabilitations, mean and highest ICU mobility scale (IMS); and daily barrier changes were also investigated.Results: A total of 204 patients were included in the analysis, 43 in the ECMO/blood purification group and 161 in the control group. In comparison of clinical outcome, the ECMO/blood purification group had a significantly longer time to first mobilization: ECMO/blood purification group 6 vs. control group 4 (p=0.003), higher number of total ICU rehabilitations: 6 vs. 5 (p=0.042), lower mean: 0 vs. 1 (p=0.043) and highest IMS: 2 vs. 3 (p=0.039) during ICU stay. Circulatory factor were most frequently described as barriers to early mobilization on days 1 (51%), 2 (47%), and 3 (26%). On days 4 to 7, the most frequently described barrier was consciousness factors (21%, 16%, 19%, and 21%, respectively)Conclusion: The results of this study comparing the ECMO/blood purification group and the untreated group in the ICU showed that the ECMO/blood purification group had significantly longer days to mobilization and significantly lower mean and highest IMS.
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Takeshi Kobayashi,Toshiaki Imanishi,Taizo Hanai,Ichiro Aoyagi,Jun Uemura,Katsuhiro Araki,Hiroshi Yoshimoto,Takeshi Harima,Hiroyuki Honda 한국생물공학회 2002 Biotechnology and Bioprocess Engineering Vol.7 No.5
In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.
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