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Obesity is a major risk factor for various chronic diseases such as diabetes, cardiovascular disease, and cancer; hence, there is an urgent need for an effective strategy to prevent this disorder. Currently, the anti-obesity effects of food ingredients are drawing attention. Therefore, we focused on carob, which has high antioxidant capacity and various physiological effects, and examined its anti-obesity effect. Carob is cultivated in the Mediterranean region, and its roasted powder is used as a substitute for cocoa powder. We investigated the effect of carob pod polyphenols (CPPs) on suppressing increases in adipose tissue weight and adipocyte hypertrophy in high fat diet-induced obesity model mice, and the mechanism by which CPPs inhibit the differentiation of 3T3-L1 preadipocytes into adipocytes in vitro. In an in vivo experimental system, we revealed that CPPs significantly suppressed the increase in adipose tissue weight and adipocyte hypertrophy. Moreover, in an in vitro experimental system, CPPs acted at the early stage of differentiation of 3T3-L1 preadipocytes and suppressed cell proliferation because of differentiation induction. They also suppressed the expression of transcription factors involved in adipocyte differentiation, thereby reducing triacylglycerol synthesis ability and triglycerol (TG) accumulation. Notably, CPPs regulated CCAAT/enhancer binding protein (C/EBP)β, which is expressed at the early stage of differentiation, at the posttranscriptional level. These results demonstrate that CPPs suppress the differentiation of adipocytes through the posttranscriptional regulation of C/EBPβ and may serve as an effective anti-obesity compound.

We examined a newly developed digitized Trail Making Test using an iPad (iTMT) as a brief of cognitive function screening test. We found that the iTMT part-A (iTMT-A) can estimate generalized cognitive function in elderly rehabilitation inpatients examined using the Mini-Mental State Examination (MMSE). Forty-two hospitalized participants undergoing rehabilitation (rehab participants), 30 of whom had cerebral infarction/hemorrhage (stroke participants), performed the iTMT three times and the MMSE. Each iTMT-A trial’s completion time was divided into the move and dwell times, for which the deviations in the left-right, forward-backward, or diagonal direction or position were also calculated. In stepwise analysis, the power of estimating the MMSE score was increased by using the dwell and move times with the deviation quadrant extracted from the iTMT-A, even for subjects with low MMSE scores. Moreover, the use of data from repeated iTMT-A trials reduced the error and improved the estimation ability. The final remaining factors included factors associated with physical disability of upper limbs and the cerebral lesion side. The iTMT-A extracts significant factors temporally and spatially, and by incorporating the learning effect of repeated trials, it may be possible to screen cognitive and physical functions for rehabilitation patients, including elderly or stroke patients.

Trail making test (TMT) is one of the most extensively used neuropsychological tests. In this study, we examined the equivalence between the iPad version of TMT part A (iTMT-A) and the paper version of TMT part A (pTMT-A), and predicted the cognitive function with various data extracted from repeated TMT-A. Forty-two patients who performed five repeated TMT-A (1st–3rd: iTMT-A, 4th: pTMT-A, 5th: inverse version of iTMT-A) and Mini-Mental State Examination (MMSE) were included. The Kruskal–Wallis one-way analysis of variance revealed no statistical differences between the completion times of iTMT-A and pTMT-A. Factors contributing to the MMSE prediction were selected by stepwise multiple regression analysis and Bland–Altman plots. Then, the prediction abilities of the three models—multiple linear, partial least squares (PLS), and neural network regression—were compared. When using the completion time, the linear regression model with the 1st–5th results exhibited the highest prediction ability. However, when the move time and dwell time were used, the multiple linear and PLS regression models using the 1st and 2nd iTMT-A data exhibited the highest prediction ability. Compared with pTMT-A, iTMT-A extracted a large amount of data with fewer repetitions, and the prediction accuracy of cognitive function was improved.