AIM to evaluate gender differences in the aspect of ghrelin,nociception-related genes and psychological aspects and the quality of life(Qo L) in Korean functional dyspepsia(FD) patients.METHODS Total of 191 persons we...AIM to evaluate gender differences in the aspect of ghrelin,nociception-related genes and psychological aspects and the quality of life(Qo L) in Korean functional dyspepsia(FD) patients.METHODS Total of 191 persons were prospectively enrolled between March 2013 and May 2016 in Seoul National Bundang Hospital,and classified into control and FD group based on ROME Ⅲ criteria. Questionnaire included assessment for dyspepsia symptoms,Qo L and anxiety or depression. Preproghrelin and nociception genes in the gastric mucosa and plasma acyl/des-acyl ghrelin were measured. RESULTS Lower level of plasma acyl ghrelin in FD patients compared to control was significant only in male(15.9 fmol/m L vs 10.4 fmol/m L,P = 0.017). Significantly higher m RNA expressions of nerve growth factor and transient receptor potential vanilloid receptor 1 were observed in male(P = 0.002 and P = 0.014,respectively) than in female. In contrast,female FD patients had a higher anxiety and depression score than male FD(P = 0.029),and anxiety score was correlated with epigastric pain only in female FD patients(female: Spearman rho = 0.420,P = 0.037). The impairment of overall Qo L was more prominent in female FD patients than male patients(5.4 ± 0.3 vs 6.5 ± 0.3,P = 0.020). CONCLUSION Gender differences of ghrelin and nociception-related genes in male and psychological factors in female underlie FD symptoms. More careful assessment of psychological or emotional status is required particularly for the female FD patients.展开更多
The staggered-grid finite-difference （SGFD） method has been widely used in seismic forward modeling. The precision of the forward modeling results directly affects the results of the subsequent seismic inversion and...The staggered-grid finite-difference （SGFD） method has been widely used in seismic forward modeling. The precision of the forward modeling results directly affects the results of the subsequent seismic inversion and migration. Numerical dispersion is one of the problems in this method. The window function method can reduce dispersion by replacing the finite-difference operators with window operators, obtained by truncating the spatial convolution series of the pseudospectral method. Although the window operators have high precision in the low-wavenumber domain, their precision decreases rapidly in the high-wavenumber domain. We develop a least squares optimization method to enhance the precision of operators obtained by the window function method. We transform the SGFD problem into a least squares problem and find the best solution iteratively. The window operator is chosen as the initial value and the optimized domain is set by the error threshold. The conjugate gradient method is also adopted to increase the stability of the solution. Approximation error analysis and numerical simulation results suggest that the proposed method increases the precision of the window function operators and decreases the numerical dispersion.展开更多
Selecting differentially expressed genes(DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers.However,the small samples typically used in current mic...Selecting differentially expressed genes(DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers.However,the small samples typically used in current microarray studies may only partially reflect the widely altered gene expressions in complex diseases,which would introduce low reproducibility of gene lists selected by statistical methods.Here,by analyzing seven cancer datasets,we showed that,in each cancer,a wide range of functional modules have altered gene expressions and thus have high disease classification abilities.The results also showed that seven modules are shared across diverse cancers,suggesting hints about the common mechanisms of cancers.Therefore,instead of relying on a few individual genes whose selection is hardly reproducible in current microarray experiments,we may use functional modules as functional signatures to study core mechanisms of cancers and build robust diagnostic classifiers.展开更多
基金Supported by Support Program for Women in Science,Engineering and Technology through the National Research Foundation of Korea funded by the Ministry of Science,ICT and Future Planning,no.2016H1C3A1903202
文摘AIM to evaluate gender differences in the aspect of ghrelin,nociception-related genes and psychological aspects and the quality of life(Qo L) in Korean functional dyspepsia(FD) patients.METHODS Total of 191 persons were prospectively enrolled between March 2013 and May 2016 in Seoul National Bundang Hospital,and classified into control and FD group based on ROME Ⅲ criteria. Questionnaire included assessment for dyspepsia symptoms,Qo L and anxiety or depression. Preproghrelin and nociception genes in the gastric mucosa and plasma acyl/des-acyl ghrelin were measured. RESULTS Lower level of plasma acyl ghrelin in FD patients compared to control was significant only in male(15.9 fmol/m L vs 10.4 fmol/m L,P = 0.017). Significantly higher m RNA expressions of nerve growth factor and transient receptor potential vanilloid receptor 1 were observed in male(P = 0.002 and P = 0.014,respectively) than in female. In contrast,female FD patients had a higher anxiety and depression score than male FD(P = 0.029),and anxiety score was correlated with epigastric pain only in female FD patients(female: Spearman rho = 0.420,P = 0.037). The impairment of overall Qo L was more prominent in female FD patients than male patients(5.4 ± 0.3 vs 6.5 ± 0.3,P = 0.020). CONCLUSION Gender differences of ghrelin and nociception-related genes in male and psychological factors in female underlie FD symptoms. More careful assessment of psychological or emotional status is required particularly for the female FD patients.
基金jointly supported by the NSF(No.41720104006)the Strategic Priority Research Program of the Chinese Academy of Sciences(A)(No.XDA14010303)+2 种基金the National Oil and Gas Project(Nos.2016ZX05002-005-007HZ and 2016ZX05014-001-008HZ)the Shandong Innovation Project(No.2017CXGC1602)the Qingdao Innovation Project(Nos.16-5-1-40-jch and 17CX05011)
文摘The staggered-grid finite-difference （SGFD） method has been widely used in seismic forward modeling. The precision of the forward modeling results directly affects the results of the subsequent seismic inversion and migration. Numerical dispersion is one of the problems in this method. The window function method can reduce dispersion by replacing the finite-difference operators with window operators, obtained by truncating the spatial convolution series of the pseudospectral method. Although the window operators have high precision in the low-wavenumber domain, their precision decreases rapidly in the high-wavenumber domain. We develop a least squares optimization method to enhance the precision of operators obtained by the window function method. We transform the SGFD problem into a least squares problem and find the best solution iteratively. The window operator is chosen as the initial value and the optimized domain is set by the error threshold. The conjugate gradient method is also adopted to increase the stability of the solution. Approximation error analysis and numerical simulation results suggest that the proposed method increases the precision of the window function operators and decreases the numerical dispersion.
基金supported by the National Natural Science Foundation of China (Grant Nos. 30170515,30370388 and 30970668)
文摘Selecting differentially expressed genes(DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers.However,the small samples typically used in current microarray studies may only partially reflect the widely altered gene expressions in complex diseases,which would introduce low reproducibility of gene lists selected by statistical methods.Here,by analyzing seven cancer datasets,we showed that,in each cancer,a wide range of functional modules have altered gene expressions and thus have high disease classification abilities.The results also showed that seven modules are shared across diverse cancers,suggesting hints about the common mechanisms of cancers.Therefore,instead of relying on a few individual genes whose selection is hardly reproducible in current microarray experiments,we may use functional modules as functional signatures to study core mechanisms of cancers and build robust diagnostic classifiers.
