Objective According to literature,cancer patients have the highest incidence of malnutrition among hospital patients(40%-80%).Despite this high prevalence,this condition is still under-diagnosed.The aim of this study ...Objective According to literature,cancer patients have the highest incidence of malnutrition among hospital patients(40%-80%).Despite this high prevalence,this condition is still under-diagnosed.The aim of this study was to conduct a systematic literature review and meta-analysis to assess the diagnostic performance of the Malnutrition Screening Tool(MST),a simple tool that can be applied in a busy setting where a comprehensive assessment at screening would be impractical.Methods PubMed,EMBASE and Cochrane central register of controlled trials were systematically searched to identify records relevant to the research question.The QUADAS-2 was used to assess the quality of each included study and the meta-analysis was conducted using the hierarchical bivariate model in STATA.Results Seven records were included in this study and the overall sensitivity specificity,diagnostic odds ratio(DOR).The pooled sensitivity and specificity generated after the meta-analysis in STATA were 0.78(95%CI:0.64-0.88)and 0.82(95%CI:0.76-0.87),respectively.The corresponding DOR was 16.33(95%CI:7.08-37.67).The positive likelihood ratio(LR+)was+4.39(95%CI:3.02-6.38),and the negative likelihood ratio(LR-)were 0.27(95%CI:0.16-0.47)and the 1/LR-3.72(2.14-6.46).Conclusion These results showed that the MST provides weak diagnostic evidence when used to screen for malnutrition in adult cancer patients.展开更多
Proton magnetic resonance spectroscopy and diffusion tensor imaging are non-invasive techniques used to detect metabolites and water diffusion in vivo. Previous studies have confirmed a positive correlation of individ...Proton magnetic resonance spectroscopy and diffusion tensor imaging are non-invasive techniques used to detect metabolites and water diffusion in vivo. Previous studies have confirmed a positive correlation of individual fractional anisotropy values with N-acetylaspartate/creatine and N-acetylaspartate/choline ratios in tumors, edema, and normal white matter. This study divided the brain parenchyma into tumor, peritumoral edema, and normal-appearing white matter according to MRI data, and analyzed the correlation of metabolites with water molecular diffusion. Results demonstrated that in normal-appearing white matter, N-acetylaspartate/creatine ratios were positively correlated with fractional anisotropy values, negatively correlated with radial diffusivities, and positively correlated with maximum eigenvalues. Maximum eigenvalues and radial diffusivities in peritumoral edema showed a negative correlation with choline, N-acetylaspartate, and creatine. Radial diffusivities in tumor demonstrated a negative correlation with choline. These data suggest that the relationship between metabolism and structure is markedly changed from normal white matter to peritumoral edema and tumor. Neural metabolism in the peritumoral edema area decreased with expanding extracellular space. The normal relationship of neural function and microstructure disappeared in the tumor region.展开更多
Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by vario...Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.展开更多
文摘Objective According to literature,cancer patients have the highest incidence of malnutrition among hospital patients(40%-80%).Despite this high prevalence,this condition is still under-diagnosed.The aim of this study was to conduct a systematic literature review and meta-analysis to assess the diagnostic performance of the Malnutrition Screening Tool(MST),a simple tool that can be applied in a busy setting where a comprehensive assessment at screening would be impractical.Methods PubMed,EMBASE and Cochrane central register of controlled trials were systematically searched to identify records relevant to the research question.The QUADAS-2 was used to assess the quality of each included study and the meta-analysis was conducted using the hierarchical bivariate model in STATA.Results Seven records were included in this study and the overall sensitivity specificity,diagnostic odds ratio(DOR).The pooled sensitivity and specificity generated after the meta-analysis in STATA were 0.78(95%CI:0.64-0.88)and 0.82(95%CI:0.76-0.87),respectively.The corresponding DOR was 16.33(95%CI:7.08-37.67).The positive likelihood ratio(LR+)was+4.39(95%CI:3.02-6.38),and the negative likelihood ratio(LR-)were 0.27(95%CI:0.16-0.47)and the 1/LR-3.72(2.14-6.46).Conclusion These results showed that the MST provides weak diagnostic evidence when used to screen for malnutrition in adult cancer patients.
基金supported by the National Natural Science Foundation of China, No. 81171318Shaanxi Provincial Scientific Research Project, No. 2012K13-02-24
文摘Proton magnetic resonance spectroscopy and diffusion tensor imaging are non-invasive techniques used to detect metabolites and water diffusion in vivo. Previous studies have confirmed a positive correlation of individual fractional anisotropy values with N-acetylaspartate/creatine and N-acetylaspartate/choline ratios in tumors, edema, and normal white matter. This study divided the brain parenchyma into tumor, peritumoral edema, and normal-appearing white matter according to MRI data, and analyzed the correlation of metabolites with water molecular diffusion. Results demonstrated that in normal-appearing white matter, N-acetylaspartate/creatine ratios were positively correlated with fractional anisotropy values, negatively correlated with radial diffusivities, and positively correlated with maximum eigenvalues. Maximum eigenvalues and radial diffusivities in peritumoral edema showed a negative correlation with choline, N-acetylaspartate, and creatine. Radial diffusivities in tumor demonstrated a negative correlation with choline. These data suggest that the relationship between metabolism and structure is markedly changed from normal white matter to peritumoral edema and tumor. Neural metabolism in the peritumoral edema area decreased with expanding extracellular space. The normal relationship of neural function and microstructure disappeared in the tumor region.
基金financially supported by the National Natural Science Foundation of China (Nos. 61971208, 61671225 and 51864027)the Yunnan Applied Basic Research Projects (No. 2018FA034)+2 种基金the Yunnan Reserve Talents of Young and Middleaged Academic and Technical Leaders (Shen Tao, 2018)the Yunnan Young Top Talents of Ten Thousands Plan (Shen Tao, Zhu Yan, Yunren Social Development No. 2018 73)the Scientific Research Foundation of Kunming University of Science and Technology (No. KKSY201703016)。
文摘Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.
