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- 저자 : Kazunori Hashimoto (검색결과 3 건)
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Current status of the diagnosis of chronic pancreatitis by ultrasonographic elastography
( Kazunori Nakaoka ),( Senju Hashimoto ),( Ryoji Miyahara ),( Hiroki Kawashima ),( Eizaburo Ohno ),( Takuya Ishikawa ),( Takamichi Kuwahara ),( Hiroyuki Tanaka ),( Yoshiki Hirooka ) 대한내과학회 2022 The Korean Journal of Internal Medicine Vol.37 No.1
Chronic pancreatitis (CP) is pathologically characterized by the loss of exocrine pancreatic parenchyma, irregular fibrosis, cellular infiltration, and ductal abnormalities. Diagnosing CP objectively is difficult because standard diagnostic criteria are insufficient. The change of parenchymal hardness is the key factor for the diagnosis and understanding of the se verity of CP. The ultrasonography (US) or endoscopic ultrasonography (EUS) elastography have been used to diagnose pancreatic diseases. Both strain elastography (SE) and shear wave elastography are specific diagnostic techniques for measuring tissue hardness. Most previous studies were conducted with SE. There are three methods of interpreting SE; the method of recognizing the patterns in SE distribution images in the region of interest, the method of using strain ratio to compare the hardness of adipose tissue or connective tissue with that of the lesion, and the method of evaluating the hardness distribution of a target by histogram analysis. These former two methods have been used primarily for neoplastic diseases, and histograms analysis has been used to assess hardness distribu tion in the evaluation of CP. Since the hardness of the pancreas increases with aging, it is necessary to consider the age in the diagnosis of pancreatic disorders using US or EUS elastography.
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Munetoshi Akazawa,Kazunori Hashimoto,Katsuhiko Noda,Kaname Yoshida 대한산부인과학회 2021 Obstetrics & Gynecology Science Vol.64 No.3
ObjectiveMost women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patientswho develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probabilityof recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinicaldata. MethodsWe enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetricsstage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used,including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boostedtree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic,stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracyand the area under the curve (AUC). ResultsThe highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the bestpredictive model for this analysis was LR. ConclusionThe performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The useof a machine learning model made it possible to predict recurrence in early stage endometrial cancer.
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Seo, Hyunwoong,Ichida, Daiki,Hashimoto, Shinji,Itagaki, Naho,Koga, Kazunori,Shiratani, Masaharu,Nam, Sang-Hun,Boo, Jin-Hyo American Scientific Publishers 2016 Journal of Nanoscience and Nanotechnology Vol.16 No.5
<P>The multiple exciton generation characteristics of quantum dots have been expected to enhance the performance of photochemical solar cells. In previous work, we first introduced Si quantum dot for sensitized solar cells. The Si quantum dots were fabricated by multi-hollow discharge plasma chemical vapor deposition, and were characterized optically and morphologically. The Si quantum dot-sensitized solar cells had poor performance due to significant electron loss by charge recombination. Although the large Si particle size resulted in the exposure of a large TiO2 surface area, there was a limit to ho much the particle size could be decreased due to the reduced absorbance of small particles. Therefore, this work focused on decreasing the internal impedance to improve charge transfer. TiO2 was electronically modified by doping with vanadium, which can improve electron transfer in the TiO2 network, and which is stable in the redox electrolyte. Photogenerated electrons can more easily arrive at the conductive electrode due to the decreased internal impedance. The dark photovoltaic properties confirmed the reduction of charge recombination, and the photon-to-current conversion efficiency reflected the improved electron transfer. Impedance analysis confirmed a decrease in internal impedance and an increased electron lifetime. Consequently, these improvements by vanadium doping enhanced the overall performance of Si quantum dot-sensitized solar cells.</P>
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