Background: Basal Metabolic Rate (BMR) is the quantum of calories needed for optimum body function when at rest. This has long been an indicator of one’s health and the basis for determining the metabolic age of indi...Background: Basal Metabolic Rate (BMR) is the quantum of calories needed for optimum body function when at rest. This has long been an indicator of one’s health and the basis for determining the metabolic age of individuals. Many scholastic projects have led to the establishment of mathematical models and inventions that measure the BMR and other body composition parameters. However, existing computations have limitations as they do not offer accurate results for Ghanaians. Aim: The purpose of the study was to model BMR metrics that are most suitable for Ghanaians and to investigate the effect of caloric difference on weight, Lean Body Mass (LBM) and % fat composition that can be implemented with Information Technology. Research Methods and Procedures: This was an experimental study that adopted a quantitative approach. BMR and body composition were measured in a sample of 242 Ghanaian adults (141 males and 101 females) from 19 to 30 years of age. Body composition was measured using bioelectrical impendence analysis (BIA) in all participants. Each participant was under study for 7 days. A simple linear regression model was used to examine associations between BMR/calorie intake and total body weight and LBM. Results: There was a significant statistical relation between BMR and LBM and between BMR and weight of both men and women. Equations for BMR and weight were established for males and females. Furthermore, caloric intake differences affected changes in total weight as well as differences in % fat composition. Caloric intake however did not affect the difference in LBM. Conclusion: Caloric difference had an impact on total body weight and Lean Body Mass. The model derived from the study predicts weight change and BMR of Ghanaians from 19 to 30 years of age. It is termed the Health and Age Monitoring System (HAMS).展开更多
BMR （basal metabolic rate）, body mass and organ masses of tree sparrows （Passer montanus） were measured to analyze the correlation between organ masses and BMR in tree sparrows, and to evaluate the underlying phys...BMR （basal metabolic rate）, body mass and organ masses of tree sparrows （Passer montanus） were measured to analyze the correlation between organ masses and BMR in tree sparrows, and to evaluate the underlying physiological causes of difference in BMR. Adult tree sparrows were live-trapped by mist net in Qiqihar City, Heilongjiang Province （47°29′N, 124°02′E）. The closed circuit respirometer was used to measure the metabolic rate （MR）, and controlled the ambient temperature by using a water bath （±0.5℃）. Body masses were measured to the nearest 0.01 g before and after BMR measurements with a Sartorius balance （model BT25S）. The mean value was recorded as body mass. Wet and dry masses of several organs were measured, too. BMR was （4.276± 0.385） mL O2/（g·h） and mean body mass was （18.522±0.110） g. Since not all the variables were normal distributed, a log10- transformation of those variables was employed to linearize them, prior to analyses. Simple regression analyses indicated that most organ masses showed a significant high correlation with body mass. Both the small intestine and rectum masses were notable exception to that trend. The body-mass-adjusted residual analysis showed that only the kidney wet mass, brain mass, stomach mass, small mass and rectum wet mass correlated with BMR. In addition, correlations between several organ masses and BMR were observed. Because of the inter-correlations of organ masses, a principal component analysis （PCA） was performed to redefine the morphological variability. The first four components whose eigenvalues were greater than 1 could explain 75.2% variance of BMR. The first component, whose proportion reached 30.19%, was affected mainly by stomach mass, small intestine mass and rectum mass. Therefore, the results supported the hypothesis that BMR was controlled by some ＂expensive metabolic＂ organs展开更多
Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theo...Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis.Background:In recent years,deep learning has been widely used in academia,industry,andmedicine.An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning.Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques.For now,however,there is no practical training platform that can help radiologists learn automated medical image analysis systematically.ApplicationDesign:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this platform.The platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.展开更多
文摘Background: Basal Metabolic Rate (BMR) is the quantum of calories needed for optimum body function when at rest. This has long been an indicator of one’s health and the basis for determining the metabolic age of individuals. Many scholastic projects have led to the establishment of mathematical models and inventions that measure the BMR and other body composition parameters. However, existing computations have limitations as they do not offer accurate results for Ghanaians. Aim: The purpose of the study was to model BMR metrics that are most suitable for Ghanaians and to investigate the effect of caloric difference on weight, Lean Body Mass (LBM) and % fat composition that can be implemented with Information Technology. Research Methods and Procedures: This was an experimental study that adopted a quantitative approach. BMR and body composition were measured in a sample of 242 Ghanaian adults (141 males and 101 females) from 19 to 30 years of age. Body composition was measured using bioelectrical impendence analysis (BIA) in all participants. Each participant was under study for 7 days. A simple linear regression model was used to examine associations between BMR/calorie intake and total body weight and LBM. Results: There was a significant statistical relation between BMR and LBM and between BMR and weight of both men and women. Equations for BMR and weight were established for males and females. Furthermore, caloric intake differences affected changes in total weight as well as differences in % fat composition. Caloric intake however did not affect the difference in LBM. Conclusion: Caloric difference had an impact on total body weight and Lean Body Mass. The model derived from the study predicts weight change and BMR of Ghanaians from 19 to 30 years of age. It is termed the Health and Age Monitoring System (HAMS).
基金Supported by Natural Foundation for Youth of Daqing Normal College (YZQ004)
文摘BMR （basal metabolic rate）, body mass and organ masses of tree sparrows （Passer montanus） were measured to analyze the correlation between organ masses and BMR in tree sparrows, and to evaluate the underlying physiological causes of difference in BMR. Adult tree sparrows were live-trapped by mist net in Qiqihar City, Heilongjiang Province （47°29′N, 124°02′E）. The closed circuit respirometer was used to measure the metabolic rate （MR）, and controlled the ambient temperature by using a water bath （±0.5℃）. Body masses were measured to the nearest 0.01 g before and after BMR measurements with a Sartorius balance （model BT25S）. The mean value was recorded as body mass. Wet and dry masses of several organs were measured, too. BMR was （4.276± 0.385） mL O2/（g·h） and mean body mass was （18.522±0.110） g. Since not all the variables were normal distributed, a log10- transformation of those variables was employed to linearize them, prior to analyses. Simple regression analyses indicated that most organ masses showed a significant high correlation with body mass. Both the small intestine and rectum masses were notable exception to that trend. The body-mass-adjusted residual analysis showed that only the kidney wet mass, brain mass, stomach mass, small mass and rectum wet mass correlated with BMR. In addition, correlations between several organ masses and BMR were observed. Because of the inter-correlations of organ masses, a principal component analysis （PCA） was performed to redefine the morphological variability. The first four components whose eigenvalues were greater than 1 could explain 75.2% variance of BMR. The first component, whose proportion reached 30.19%, was affected mainly by stomach mass, small intestine mass and rectum mass. Therefore, the results supported the hypothesis that BMR was controlled by some ＂expensive metabolic＂ organs
基金This work is supported in part by the Major Fundamental Research of Natural Science Foundation of Shandong Province under Grant ZR2019ZD05Joint Fund for Smart Computing of Shandong Natural Science Foundation under Grant ZR2020LZH013+1 种基金the Scientific Research Platform and Projects of Department of Education of Guangdong Province under Grant 2019GKQNCX121the Intelligent Perception and Computing Innovation Platform of the Shenzhen Institute of Information Technology under Grant PT2019E001.
文摘Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis.Background:In recent years,deep learning has been widely used in academia,industry,andmedicine.An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning.Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques.For now,however,there is no practical training platform that can help radiologists learn automated medical image analysis systematically.ApplicationDesign:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this platform.The platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.
