This large-scale study indicates that leukocytapheresis, including intensive procedure, is a safe and effective therapeutic option for active ulcerative colitis.

Background— Although both 123 I-metaiodobenzylguanidine ( 123 I-MIBG) imaging and 11 C-hydroxyephedrine ( 11 C-HED) positron emission tomography (PET) are used for assessing cardiac sympathetic innervation, their relationship remains unknown. The aims were to determine whether 123 I-MIBG parameters such as heart-to-mediastinum ratio (H/M) are associated with quantitative measures by 11 C-HED PET and to compare image quality, defect size, and location between 123 I-MIBG single-photon emission computed tomography (SPECT) and 11 C-HED PET. Methods and Results— Twenty-one patients (mean left ventricular ejection fraction, 39±15%) underwent 123 I-MIBG imaging and 11 C-HED PET. Early (15-minute), late (3-hour) H/M, and washout rate (WR) were calculated for 123 I-MIBG. Myocardial retention and WR was calculated for 11 C-HED. Using a polar map approach, defect was defined as the area with relative activity <60% of the maximum. Both the early ( r =0.76) and late ( r =0.84) 123 I-MIBG H/M were correlated with 11 C-HED retention. 123 I-MIBG WR was correlated with 11 C-HED WR ( r =0.57). Defect size could not be measured in 3 patients because of poor quality 123 I-MIBG SPECT, whereas 11 C-HED defect was measurable in all patients. Although defect size measured by early or late 123 I-MIBG SPECT was closely correlated with that by 11 C-HED PET (early: r =0.94; late: r =0.88), the late 123 I-MIBG overestimated defect size particularly in the inferior and septal regions. Conclusions— 123 I-MIBG H/M gives a reliable estimate of cardiac sympathetic innervation as measured by 11 C-HED PET. Furthermore, despite the close correlation in defect size, 11 C-HED PET appears to be more suitable for assessing regional abnormalities than does 123 I-MIBG SPECT.

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Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.

In recent years, the number of traffic accidents caused by elderly drivers has increased in Japan. However, a car is an important mode of transportation for the elderly. Therefore, to ensure safe driving, a system that can assist elderly drivers is required. In this study, we propose a driver-agent system that provides support to elderly drivers during and after driving and encourages them to improve their driving. This paper describes the prototype system based on the analysis of the teaching records of a human instructor, and the subjective evaluation of driving support to elderly and non-elderly driver from three different agent forms, a voice, visual, and robot. The result revealed that the robot form is more noticeable, familiar, and acceptable to the elderly and non-elderly than other forms.