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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.
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.
Azusa Kawasaki,Kunihiro Tsuji,Noriya Uedo,Takashi Kanesaka,Hideaki Miyamoto,Ryosuke Gushima,Yosuke Minoda,Eikichi Ihara,Ryosuke Amano,Kenshi Yao,Yoshihide Naito,Hiroyuki Aoyagi,Takehiro Iwasaki,Kunihi 대한소화기내시경학회 2023 Clinical Endoscopy Vol.56 No.1
Background/Aims: The etiology of superficial non-ampullary duodenal epithelial tumors (SNADETs) remains unclear. Recent studieshave reported conflicting associations between duodenal tumor development and Helicobacter pylori infection or endoscopic gastricmucosal atrophy. As such, the present study aimed to clarify the relationship between SNADETs and H. pylori infection and/or endoscopicgastric mucosal atrophy. Methods: This retrospective case-control study reviewed data from 177 consecutive patients with SNADETs who underwent endoscopicor surgical resection at seven institutions in Japan over a three-year period. The prevalence of endoscopic gastric mucosal atrophyand the status of H. pylori infection were compared in 531 sex- and age-matched controls selected from screening endoscopies attwo of the seven participating institutions. Results: For H. pylori infection, 85 of 177 (48.0%) patients exhibited SNADETs and 112 of 531 (21.1%) control patients were non-infected(p<0.001). Non-atrophic mucosa (C0 to C1) was observed in 96 of 177 (54.2%) patients with SNADETs and 112 of 531 (21.1%)control patients (p<0.001). Conditional logistic regression analysis revealed that non-atrophic gastric mucosa was an independent riskfactor for SNADETs (odds ratio, 5.10; 95% confidence interval, 2.44–8.40; p<0.001). Conclusions: Non-atrophic gastric mucosa, regardless of H. pylori infection status, was a factor independently associated with SNADETs.