Purpose: Although exercise and sleep duration habits are associated with cognitive function, their beneficial effects on cognitive function remain unclear. We aimed to examine the effect of sleep duration and daily ph...Purpose: Although exercise and sleep duration habits are associated with cognitive function, their beneficial effects on cognitive function remain unclear. We aimed to examine the effect of sleep duration and daily physical activity on cognitive function, elucidating the neural mechanisms using near-infrared spectroscopy(NIRS).Methods: A total of 23 healthy young adults(age 22.0 ± 2.2 years) participated in this study. Exercise amount was assessed using a uniaxial accelerometer. We evaluated total sleep time(TST) and sleep efficiency by actigraphy. Cognitive function was tested using the N-back task, the Wisconsin Card Sorting Test(WCST), and the Continuous Performance Test—Identical Pairs(CPT-IP), and the cortical oxygenated hemoglobin levels during a word fluency task were measured with NIRS.Results: Exercise amount was significantly correlated with reaction time on 0- and 1-back tasks(r = —0.602, p = 0.002; r = —0.446, p = 0.033,respectively), whereas TST was significantly correlated with % corrects on the 2-back task(r = 0.486, p = 0.019). Multiple regression analysis,including exercise amount, TST, and sleep efficiency, revealed that exercise amount was the most significant factor for reaction time on 0- and 1-back tasks(b = —0.634, p = 0.002; b = —0.454, p = 0.031, respectively), and TST was the most significant factor for % corrects on the 2-back task(b = 0.542, p = 0.014). The parameter measured by WCST and CPT-IP was not significantly correlated with TST or exercise amount. Exercise amount, but not TST, was significantly correlated with the mean area under the NIRS curve in the prefrontal area(r = 0.492, p = 0.017).Conclusion: Exercise amount and TST had differential effects on working memory and cortical activation in the prefrontal area. Daily physical activity and appropriate sleep duration may play an important role in working memory.展开更多
Objective： To systematically analyze and summarize non-thyrogenous masses of the neck （NTMN） by consideration of new areas, a large sample size and multiple-aspect analysis. Methods： Our research involved 3125 NTM...Objective： To systematically analyze and summarize non-thyrogenous masses of the neck （NTMN） by consideration of new areas, a large sample size and multiple-aspect analysis. Methods： Our research involved 3125 NTMN cases. We summarized the proportion of various NTMN and the distribution of the neck diseases based on the new international classification. The clinical traits such as sexual proportion and age, etc, were analyzed along with the unknown primary cervical metastatic carcinomas （UPCMC）, and built up a mathematical model based on the data above. Results： There were 68 different diseases identified. Among all the NTMN, the percentage of metastatic carcinomas was 63.3%. The neck masses with a focus above the clavicle comprised 62.3% of the metastatic carcinomas whose focuses were clear. Moreover, other results almost supported the ＂rule of 80%＂. There was an obvious distribution of traits at every sub level. For example, there were 23 different diseases in level Ⅲ, of which the most common was lymphoma. UPCMC made up 12.3% of all metastatic carcinomas. The clinic cases could be analyzed by our model even to form a primary diagnosis which showed a high coincident rate with clinic diagnosis. Conclusion： NTMN are complex and various, with a definite distribution in each neck level. Data relating component character, sex ratio and UPCMC et al to the clinical traits of NTMN will provide vigorous support for clinical applications. The mathematical model could be an efficient method to synthetically analyze complicate data of NTMN.展开更多
基金supported by the Japan Society for the Promotion of Science, KAKENHI (25282210,15H05935)by Grants-in-Aid from the Comprehensive Research on Disability Health and Welfare+3 种基金the Ministry of Health,Labor and Welfare of Japanthe Academic Frontier Project for Private UniversitiesComparative Cognitive Science InstitutesMeijo University
文摘Purpose: Although exercise and sleep duration habits are associated with cognitive function, their beneficial effects on cognitive function remain unclear. We aimed to examine the effect of sleep duration and daily physical activity on cognitive function, elucidating the neural mechanisms using near-infrared spectroscopy(NIRS).Methods: A total of 23 healthy young adults(age 22.0 ± 2.2 years) participated in this study. Exercise amount was assessed using a uniaxial accelerometer. We evaluated total sleep time(TST) and sleep efficiency by actigraphy. Cognitive function was tested using the N-back task, the Wisconsin Card Sorting Test(WCST), and the Continuous Performance Test—Identical Pairs(CPT-IP), and the cortical oxygenated hemoglobin levels during a word fluency task were measured with NIRS.Results: Exercise amount was significantly correlated with reaction time on 0- and 1-back tasks(r = —0.602, p = 0.002; r = —0.446, p = 0.033,respectively), whereas TST was significantly correlated with % corrects on the 2-back task(r = 0.486, p = 0.019). Multiple regression analysis,including exercise amount, TST, and sleep efficiency, revealed that exercise amount was the most significant factor for reaction time on 0- and 1-back tasks(b = —0.634, p = 0.002; b = —0.454, p = 0.031, respectively), and TST was the most significant factor for % corrects on the 2-back task(b = 0.542, p = 0.014). The parameter measured by WCST and CPT-IP was not significantly correlated with TST or exercise amount. Exercise amount, but not TST, was significantly correlated with the mean area under the NIRS curve in the prefrontal area(r = 0.492, p = 0.017).Conclusion: Exercise amount and TST had differential effects on working memory and cortical activation in the prefrontal area. Daily physical activity and appropriate sleep duration may play an important role in working memory.
文摘Objective： To systematically analyze and summarize non-thyrogenous masses of the neck （NTMN） by consideration of new areas, a large sample size and multiple-aspect analysis. Methods： Our research involved 3125 NTMN cases. We summarized the proportion of various NTMN and the distribution of the neck diseases based on the new international classification. The clinical traits such as sexual proportion and age, etc, were analyzed along with the unknown primary cervical metastatic carcinomas （UPCMC）, and built up a mathematical model based on the data above. Results： There were 68 different diseases identified. Among all the NTMN, the percentage of metastatic carcinomas was 63.3%. The neck masses with a focus above the clavicle comprised 62.3% of the metastatic carcinomas whose focuses were clear. Moreover, other results almost supported the ＂rule of 80%＂. There was an obvious distribution of traits at every sub level. For example, there were 23 different diseases in level Ⅲ, of which the most common was lymphoma. UPCMC made up 12.3% of all metastatic carcinomas. The clinic cases could be analyzed by our model even to form a primary diagnosis which showed a high coincident rate with clinic diagnosis. Conclusion： NTMN are complex and various, with a definite distribution in each neck level. Data relating component character, sex ratio and UPCMC et al to the clinical traits of NTMN will provide vigorous support for clinical applications. The mathematical model could be an efficient method to synthetically analyze complicate data of NTMN.
