BACKGROUND Gliomas are the most common primary central nervous system neoplasm.Despite recent advances in the diagnosis and treatment of gliomas,patient prognosis remains dismal.Therefore,it is imperative to identify ...BACKGROUND Gliomas are the most common primary central nervous system neoplasm.Despite recent advances in the diagnosis and treatment of gliomas,patient prognosis remains dismal.Therefore,it is imperative to identify novel diagnostic biomarkers and therapeutic targets of glioma to effectively improve treatment outcomes.AIM To investigate the association between oligodendrocyte transcription factor 2(Olig2)expression and the outcomes of glioma patients.METHODS The PubMed,Embase,Cochrane Library,and China National Knowledge Infrastructure databases were searched for studies(published up to October 2023)that investigated the relationship between Olig2 expression and prognosis of glioma patients.The quality of the studies was assessed using the Newcastle Ottawa Scale.Data analyses were performed using Stata Version 12.0 software.RESULTS A total of 1205 glioma patients from six studies were included in the metaanalysis.High Olig2 expression was associated with better outcomes in glioma patients[hazard ratio(HR):0.81;95%(confidence interval)CI:0.51-1.27;P=0.000].Furthermore,the results of subgroup meta-analysis showed that high expression of Olig2 was associated with poor overall survival in European patients(HR:1.34;95%CI:0.79-2.27)and better prognosis in Asian patients(HR:0.43;95%CI:0.22-0.84).The sensitivity analysis showed that no single study had a significant effect on pooled HR,and there was also no indication of publication bias according to the Egger’s and Begger’s P value test or funnel plot test.CONCLUSION High Olig2 expression may have a positive impact on the prognosis of glioma patients,and should be investigated further as a prognostic biomarker and therapeutic target for glioma.展开更多
AIM: To explore the association between AT-rich interactive domain 1A (ARID1A) protein loss by immunohistochemistry and both clinicopathologic characteristics and prognosis in patients with colorectal cancer.
Green sand is a mixture of silica sand,bentonite,water and coal powder,and other additives.Moisture content is an important index to characterize the properties of green sand.Based on the dielectric characteristics of...Green sand is a mixture of silica sand,bentonite,water and coal powder,and other additives.Moisture content is an important index to characterize the properties of green sand.Based on the dielectric characteristics of green sand and transmission line theory,a method for rapidly measuring the moisture content of green sand by means of a low frequency multiprobe detector was proposed.A system was constructed,where six detectors with different arrangements and probes were designed.The experimental results showed that the voltage difference of transmission line increases with the increasing frequency before 29 MHz while decreases after 35 MHz.A voltage difference platform occurs in the range of 29-35 MHz,which is suitable for measuring the moisture content due to its insensitivity to frequency.The electric field intensity gradually decreases with the increase of the probe depth,and the intensity of central probe is always greater than that of the edge probe.When the distance of the probe away from the sand sample surface is 80 mm,the electric field intensity of the edge probe is found to be very weak.The optimal excitation frequency for measuring the moisture content of green sand is 29-33 MHz.The optimal detector is the one with one center probe and three edge probes,and their lengths are 80 mm and 60 mm,respectively.The distance between the center and edge probes is 25 mm,and the diameter of probes is 5 mm.Taking the voltage difference of transmission line,bentonite content,coal powder content and compactability as parameters of the input layer,and the moisture content as a parameter of the output layer,a three-layer BP artificial neural network model for predicting the moisture content of green sand was constructed according to the experimental results at 33 MHz.The prediction error of the model is not higher than 3.3% when the moisture content of green sand is within the range of 3wt.%-7wt.%.展开更多
文摘BACKGROUND Gliomas are the most common primary central nervous system neoplasm.Despite recent advances in the diagnosis and treatment of gliomas,patient prognosis remains dismal.Therefore,it is imperative to identify novel diagnostic biomarkers and therapeutic targets of glioma to effectively improve treatment outcomes.AIM To investigate the association between oligodendrocyte transcription factor 2(Olig2)expression and the outcomes of glioma patients.METHODS The PubMed,Embase,Cochrane Library,and China National Knowledge Infrastructure databases were searched for studies(published up to October 2023)that investigated the relationship between Olig2 expression and prognosis of glioma patients.The quality of the studies was assessed using the Newcastle Ottawa Scale.Data analyses were performed using Stata Version 12.0 software.RESULTS A total of 1205 glioma patients from six studies were included in the metaanalysis.High Olig2 expression was associated with better outcomes in glioma patients[hazard ratio(HR):0.81;95%(confidence interval)CI:0.51-1.27;P=0.000].Furthermore,the results of subgroup meta-analysis showed that high expression of Olig2 was associated with poor overall survival in European patients(HR:1.34;95%CI:0.79-2.27)and better prognosis in Asian patients(HR:0.43;95%CI:0.22-0.84).The sensitivity analysis showed that no single study had a significant effect on pooled HR,and there was also no indication of publication bias according to the Egger’s and Begger’s P value test or funnel plot test.CONCLUSION High Olig2 expression may have a positive impact on the prognosis of glioma patients,and should be investigated further as a prognostic biomarker and therapeutic target for glioma.
基金Supported by National High Technology Research and Development Program of China(863 Program),No.2012AA02A506National Natural Science Foundation of China,No.81372570+1 种基金the Science and Technology Foundation of Guangdong Province,China,No.2012B031800088the Science and Technology Foundation of Guangdong Province,China,No.C2011019
文摘AIM: To explore the association between AT-rich interactive domain 1A (ARID1A) protein loss by immunohistochemistry and both clinicopathologic characteristics and prognosis in patients with colorectal cancer.
基金financially supported by the National Natural Science Foundation of China (Grant No.51975165)。
文摘Green sand is a mixture of silica sand,bentonite,water and coal powder,and other additives.Moisture content is an important index to characterize the properties of green sand.Based on the dielectric characteristics of green sand and transmission line theory,a method for rapidly measuring the moisture content of green sand by means of a low frequency multiprobe detector was proposed.A system was constructed,where six detectors with different arrangements and probes were designed.The experimental results showed that the voltage difference of transmission line increases with the increasing frequency before 29 MHz while decreases after 35 MHz.A voltage difference platform occurs in the range of 29-35 MHz,which is suitable for measuring the moisture content due to its insensitivity to frequency.The electric field intensity gradually decreases with the increase of the probe depth,and the intensity of central probe is always greater than that of the edge probe.When the distance of the probe away from the sand sample surface is 80 mm,the electric field intensity of the edge probe is found to be very weak.The optimal excitation frequency for measuring the moisture content of green sand is 29-33 MHz.The optimal detector is the one with one center probe and three edge probes,and their lengths are 80 mm and 60 mm,respectively.The distance between the center and edge probes is 25 mm,and the diameter of probes is 5 mm.Taking the voltage difference of transmission line,bentonite content,coal powder content and compactability as parameters of the input layer,and the moisture content as a parameter of the output layer,a three-layer BP artificial neural network model for predicting the moisture content of green sand was constructed according to the experimental results at 33 MHz.The prediction error of the model is not higher than 3.3% when the moisture content of green sand is within the range of 3wt.%-7wt.%.
