Background:Cachexia is a metabolic state with weight and muscle mass loss as its basic characteristics.This study aims to reveal the influ-ence of weight loss on the progression of cancer cachexia,and to determine its...Background:Cachexia is a metabolic state with weight and muscle mass loss as its basic characteristics.This study aims to reveal the influ-ence of weight loss on the progression of cancer cachexia,and to determine its impact on the patient prognosis.Methods:A total of 2990 cancer patients were enrolled in this retrospective study.Demographic information,clinical materials,and follow-up data were collected for all patients.A receiver operating characteristic curve was used to determine threshold values for weight loss within the past six months(WL).Kaplan-Meier curves and Cox proportional hazard regression models were adopted for survival analyses.Results:After excluding ineligible patients,2480 patients were included in the analysis,705(28.4%)of whom were considered to be ca-chexic.A WL of 10%was determined to be the optimal threshold for diagnosing malnutrition according to the Patient-Generated Subjective Global Assessment.Notably,WL>10%was a predictor of survival outcomes only in the general population(HR=1.218,95%Cl=1.002-1.481,P=0.048),but not in the cachexic population,based on the multivariable Cox regression model.A larger proportion of cachexic pa-tients with WL>10%had a nutritional risk screening 2002 score≥3(25.7%vs 13.7%,P<0.001)and a modified Glasgow Prognosis Score=2(12.8%vs 7.8%,P=0.032).No significant difference was observed in the degree of decreased muscle strength or quality of life(P>0.05).Conclusions:Weight loss is a predictor of impaired survival in the general population,but not in the cachexic population.The present study shows that cachexic patients with severe weight loss had a higher risk of malnutrition,a worse systemic inflammation status,and more severe malnutrition,but that the weight loss itself was not associated with the prognosis of these patients or the progression of their cachexia.展开更多
BACKGROUND Diabetes mellitus type 2(T2DM)is formed by defective insulin secretion with the addition of peripheral tissue resistance of insulin action.It has been affecting over 400 million people all over the world.AI...BACKGROUND Diabetes mellitus type 2(T2DM)is formed by defective insulin secretion with the addition of peripheral tissue resistance of insulin action.It has been affecting over 400 million people all over the world.AIM To explore the pathogenesis of T2DM and to develop and implement new prevention and treatment strategies for T2DM.METHODS Receiver operating characteristic(ROC)curve analysis was used to conduct diagnostic markers.The expression level of genes was determined by reverse transcription-PCR as well as Western blot.Cell proliferation assays were performed by cell counting kit-8(CCK-8)tests.At last,T2DM mice underwent Roux-en-Y gastric bypass surgery.RESULTS We found that NPAS2 was significantly up-regulated in isletβcell apoptosis of T2DM.The ROC curve revealed that NPAS2 was capable of accurately diagnosing T2DM.NPAS2 overexpression did increase the level of KANK1.In addition,the CCK-8 test revealed knocking down NPAS2 and KANK1 increased the proliferation of MIN6 cells.At last,we found that gastric bypass may treat type 2 diabetes by down-regulating NPAS2 and KANK1.CONCLUSION This study demonstrated that NPAS2 inducedβcell dysfunction by regulating KANK1 expression in type 2 diabetes,and it may be an underlying therapy target of T2DM.展开更多
The design and preparation of novel quantum materials with atomic precision are crucial for exploring new physics and for device applications.Electron irradiation has been demonstrated as an effective method for prepa...The design and preparation of novel quantum materials with atomic precision are crucial for exploring new physics and for device applications.Electron irradiation has been demonstrated as an effective method for preparing novel quantum materials and quantum structures that could be challenging to obtain otherwise.It features the advantages of precise control over the patterning of such new materials and their integration with other materials with different functionalities.Here,we present a new strategy for fabricating freestanding monolayer SiC within nanopores of a graphene membrane.By regulating the energy of the incident electron beam and the in-situ heating temperature in a scanning transmission electron microscope(STEM),we can effectively control the patterning of nanopores and subsequent growth of monolayer SiC within the graphene lattice.The resultant SiC monolayers seamlessly connect with the graphene lattice,forming a planar structure distinct by a wide direct bandgap.Our in-situ STEM observations further uncover that the growth of monolayer SiC within the graphene nanopore is driven by a combination of bond rotation and atom extrusion,providing new insights into the atom-by-atom self-assembly of freestanding two-dimensional(2D)monolayers.展开更多
Development of metal oxide semiconductors-based methane sensors with good response and low power consumption is one of the major challenges to realize the real-time monitoring of methane leakage.In this work,a self-as...Development of metal oxide semiconductors-based methane sensors with good response and low power consumption is one of the major challenges to realize the real-time monitoring of methane leakage.In this work,a self-assembled mulberry-like ZnO/SnO_(2)hierarchical structure is constructed by a two-step hydrothermal method.The resultant sensor works at room temperature with excellent response of~56.1%to 2000 ppm CH_(4)at 55%relative humidity.It is found that the strain induced at the ZnO/SnO_(2)interface greatly enhances the piezoelectric polarization on the ZnO surface and that the band bending results in the accumulation of chemically adsorbed O_(2)^(-)ions close to the interface,leading to significant improvement in the sensing performance of the methane gas sensor at room temperature.展开更多
In this study,ferric nitrate modified carbon nanotube composites (FCNT) were prepared by isovolumetric impregnation using carbon nanotubes (CNTs) as the carrier and ferric nitrates the active agent.The batch experimen...In this study,ferric nitrate modified carbon nanotube composites (FCNT) were prepared by isovolumetric impregnation using carbon nanotubes (CNTs) as the carrier and ferric nitrates the active agent.The batch experiments showed that FCNT could effectively oxidize As(III) to As(V) and react with it to form stable iron arsenate precipitates.When the dosage of FCNT was 0.1 g·L^(–1),pH value was 5–6,reaction temperature was 35℃ and reaction time was 2 h,the best arsenic removal effect could be achieved,and the removal rate of As(V) could reach 99.1%,which was always higher than 90%under acidic conditions.The adsorption results of FCNT were found to be consistent with Langmuir adsorption by static adsorption isotherm fitting,and the maximum adsorption capacity reached 118.3 mg·g^(-1).The material phase and property analysis by scanning electron microscopy,Brunauer–Emmett–Teller,Fourier transform infrared spectoscopy,X-ray photoelectron spectroscopy and other characterization methods,as well as adsorption isotherm modeling,were used to explore the adsorption mechanism of FCNT on arsenic.It was found that FCNT has microporous structure and nanostructure,and iron nanoparticles are loosely distributed on CNTs,which makes the material have good oxidation,adsorption and magnetic separation properties.Arsenic migrates on the surface of FCNT composites is mainly removed by forming insoluble compounds and co-precipitation.All the results show that FCNT treats arsenic at low cost with high adsorption efficiency,and the results also provide the experimental data basis and theoretical basis for arsenic contamination in groundwater.展开更多
An empirical stellar spectral library with large coverage of stellar parameters is essential for stellar population synthesis and studies of stellar evolution.In this work,we present Stellar Spectra Factory(SSF),a too...An empirical stellar spectral library with large coverage of stellar parameters is essential for stellar population synthesis and studies of stellar evolution.In this work,we present Stellar Spectra Factory(SSF),a tool to generate empirical-based stellar spectra from arbitrary stellar atmospheric parameters.The relative flux-calibrated empirical spectra can be predicted by SSF given arbitrary effective temperature,surface gravity,and metallicity.SSF constructs the interpolation approach based on the Stellar LAbel Machine,using ATLAS-A library,which contains spectra covering from O type to M type,as the training data set.SSF is composed of four data-driven sub-models to predict empirical stellar spectra.Sub-model SSF-N can generate spectra from A to K type and some M giant stars,covering 3700<T_(eff)<8700 K,0<logg<dex,and-1.5<[M/H]<0.5 dex.Sub-model SSF-gM is mainly used to predict M giant spectra with 3520<T_(eff)<4000 K and-1.5<[M/H]<0.4 dex.Sub-model SSF-dM is for generating M dwarf spectra with 3295<T_(eff)<4040 K,-1.0<[M/H]<0.1 dex.Sub-model SSF-B can predict B-type spectra with 9000<T_(eff)<24,000 K and-5.2<M_(G)<1.5 mag.The accuracy of the predicted spectra is validated by comparing the flux of predicted spectra to those with same stellar parameters selected from the known spectral libraries,MILES and MaStar.The averaged difference of flux over optical wavelength between the predicted spectra and the corresponding ones in MILES and MaStar is less than 5%.More verification is conducted between the magnitudes calculated from the integration of the predicted spectra and the observations in PS1 and APASS bands with the same stellar parameters.No significant systematic difference is found between the predicted spectra and the photometric observations.The uncertainty is 0.08 mag in the r band for SSF-gM when comparing with the stars with the same stellar parameters selected from PS1.The uncertainty becomes 0.31 mag in the i band for SSF-dM when comparing with the stars with the same stellar parameters selected from APASS.展开更多
基金provided by the Doctor of Excellence Program from The First Hospital of Jilin University(No.JDYY-DEP-2022024)
文摘Background:Cachexia is a metabolic state with weight and muscle mass loss as its basic characteristics.This study aims to reveal the influ-ence of weight loss on the progression of cancer cachexia,and to determine its impact on the patient prognosis.Methods:A total of 2990 cancer patients were enrolled in this retrospective study.Demographic information,clinical materials,and follow-up data were collected for all patients.A receiver operating characteristic curve was used to determine threshold values for weight loss within the past six months(WL).Kaplan-Meier curves and Cox proportional hazard regression models were adopted for survival analyses.Results:After excluding ineligible patients,2480 patients were included in the analysis,705(28.4%)of whom were considered to be ca-chexic.A WL of 10%was determined to be the optimal threshold for diagnosing malnutrition according to the Patient-Generated Subjective Global Assessment.Notably,WL>10%was a predictor of survival outcomes only in the general population(HR=1.218,95%Cl=1.002-1.481,P=0.048),but not in the cachexic population,based on the multivariable Cox regression model.A larger proportion of cachexic pa-tients with WL>10%had a nutritional risk screening 2002 score≥3(25.7%vs 13.7%,P<0.001)and a modified Glasgow Prognosis Score=2(12.8%vs 7.8%,P=0.032).No significant difference was observed in the degree of decreased muscle strength or quality of life(P>0.05).Conclusions:Weight loss is a predictor of impaired survival in the general population,but not in the cachexic population.The present study shows that cachexic patients with severe weight loss had a higher risk of malnutrition,a worse systemic inflammation status,and more severe malnutrition,but that the weight loss itself was not associated with the prognosis of these patients or the progression of their cachexia.
基金Supported by Natural Science Foundation of Heilongjiang Province,No.LH2021H105.
文摘BACKGROUND Diabetes mellitus type 2(T2DM)is formed by defective insulin secretion with the addition of peripheral tissue resistance of insulin action.It has been affecting over 400 million people all over the world.AIM To explore the pathogenesis of T2DM and to develop and implement new prevention and treatment strategies for T2DM.METHODS Receiver operating characteristic(ROC)curve analysis was used to conduct diagnostic markers.The expression level of genes was determined by reverse transcription-PCR as well as Western blot.Cell proliferation assays were performed by cell counting kit-8(CCK-8)tests.At last,T2DM mice underwent Roux-en-Y gastric bypass surgery.RESULTS We found that NPAS2 was significantly up-regulated in isletβcell apoptosis of T2DM.The ROC curve revealed that NPAS2 was capable of accurately diagnosing T2DM.NPAS2 overexpression did increase the level of KANK1.In addition,the CCK-8 test revealed knocking down NPAS2 and KANK1 increased the proliferation of MIN6 cells.At last,we found that gastric bypass may treat type 2 diabetes by down-regulating NPAS2 and KANK1.CONCLUSION This study demonstrated that NPAS2 inducedβcell dysfunction by regulating KANK1 expression in type 2 diabetes,and it may be an underlying therapy target of T2DM.
基金supports from the Electron Microscopy Center at the University of Chinese Academy of Sciencesfinancially supported by the Ministry of Science and Technology (MOST)of China (Grant No.2018YFE0202700)+3 种基金the Beijing Outstanding Young Scientist Program (Grant No.BJJWZYJH01201914430039)the China National Postdoctoral Program for Innovative Talents (Grant No.BX2021301)the Fundamental Research Funds for the Central Universitiesthe Research Funds of Renmin University of China (Grants No.22XNKJ30)。
文摘The design and preparation of novel quantum materials with atomic precision are crucial for exploring new physics and for device applications.Electron irradiation has been demonstrated as an effective method for preparing novel quantum materials and quantum structures that could be challenging to obtain otherwise.It features the advantages of precise control over the patterning of such new materials and their integration with other materials with different functionalities.Here,we present a new strategy for fabricating freestanding monolayer SiC within nanopores of a graphene membrane.By regulating the energy of the incident electron beam and the in-situ heating temperature in a scanning transmission electron microscope(STEM),we can effectively control the patterning of nanopores and subsequent growth of monolayer SiC within the graphene lattice.The resultant SiC monolayers seamlessly connect with the graphene lattice,forming a planar structure distinct by a wide direct bandgap.Our in-situ STEM observations further uncover that the growth of monolayer SiC within the graphene nanopore is driven by a combination of bond rotation and atom extrusion,providing new insights into the atom-by-atom self-assembly of freestanding two-dimensional(2D)monolayers.
基金financially supported by the National Natural Science Foundation of China(No.12174092,21902046,U21A20500)Overseas Expertise Introduction Center for Discipline Innovation(D18025)+1 种基金Hubei Provincial Department of Science and Technology(No.2019CFA079)Wuhan Science and Technology Bureau(2020010601012163)
文摘Development of metal oxide semiconductors-based methane sensors with good response and low power consumption is one of the major challenges to realize the real-time monitoring of methane leakage.In this work,a self-assembled mulberry-like ZnO/SnO_(2)hierarchical structure is constructed by a two-step hydrothermal method.The resultant sensor works at room temperature with excellent response of~56.1%to 2000 ppm CH_(4)at 55%relative humidity.It is found that the strain induced at the ZnO/SnO_(2)interface greatly enhances the piezoelectric polarization on the ZnO surface and that the band bending results in the accumulation of chemically adsorbed O_(2)^(-)ions close to the interface,leading to significant improvement in the sensing performance of the methane gas sensor at room temperature.
基金supported by the National Natural Science Foundation of China (NSFC) on the micro behavior of heavy metal migration and transformation in lead–zinc tailings and its nano micro scale high targeted stabilization mechanism (51968033)the National Key Research and Development Program “long-term solidification of heavy metal tailings pollution/environmental functional materials, technologies and equipment of stabilizers” (2018YFC1801702)。
文摘In this study,ferric nitrate modified carbon nanotube composites (FCNT) were prepared by isovolumetric impregnation using carbon nanotubes (CNTs) as the carrier and ferric nitrates the active agent.The batch experiments showed that FCNT could effectively oxidize As(III) to As(V) and react with it to form stable iron arsenate precipitates.When the dosage of FCNT was 0.1 g·L^(–1),pH value was 5–6,reaction temperature was 35℃ and reaction time was 2 h,the best arsenic removal effect could be achieved,and the removal rate of As(V) could reach 99.1%,which was always higher than 90%under acidic conditions.The adsorption results of FCNT were found to be consistent with Langmuir adsorption by static adsorption isotherm fitting,and the maximum adsorption capacity reached 118.3 mg·g^(-1).The material phase and property analysis by scanning electron microscopy,Brunauer–Emmett–Teller,Fourier transform infrared spectoscopy,X-ray photoelectron spectroscopy and other characterization methods,as well as adsorption isotherm modeling,were used to explore the adsorption mechanism of FCNT on arsenic.It was found that FCNT has microporous structure and nanostructure,and iron nanoparticles are loosely distributed on CNTs,which makes the material have good oxidation,adsorption and magnetic separation properties.Arsenic migrates on the surface of FCNT composites is mainly removed by forming insoluble compounds and co-precipitation.All the results show that FCNT treats arsenic at low cost with high adsorption efficiency,and the results also provide the experimental data basis and theoretical basis for arsenic contamination in groundwater.
基金supported by the National Key R&D Program of China No.2019YFA0405500the National Natural Science Foundation of China(NSFC)with grant No.11835057+1 种基金Guo Shou Jing Telescope(the Large Sky Area Multi-Object Fiber Spectroscopic Telescope LAMOST)is a National Major Scientific Project built by the Chinese Academy of SciencesFunding for the project has been provided by the National Development and Reform Commission。
文摘An empirical stellar spectral library with large coverage of stellar parameters is essential for stellar population synthesis and studies of stellar evolution.In this work,we present Stellar Spectra Factory(SSF),a tool to generate empirical-based stellar spectra from arbitrary stellar atmospheric parameters.The relative flux-calibrated empirical spectra can be predicted by SSF given arbitrary effective temperature,surface gravity,and metallicity.SSF constructs the interpolation approach based on the Stellar LAbel Machine,using ATLAS-A library,which contains spectra covering from O type to M type,as the training data set.SSF is composed of four data-driven sub-models to predict empirical stellar spectra.Sub-model SSF-N can generate spectra from A to K type and some M giant stars,covering 3700<T_(eff)<8700 K,0<logg<dex,and-1.5<[M/H]<0.5 dex.Sub-model SSF-gM is mainly used to predict M giant spectra with 3520<T_(eff)<4000 K and-1.5<[M/H]<0.4 dex.Sub-model SSF-dM is for generating M dwarf spectra with 3295<T_(eff)<4040 K,-1.0<[M/H]<0.1 dex.Sub-model SSF-B can predict B-type spectra with 9000<T_(eff)<24,000 K and-5.2<M_(G)<1.5 mag.The accuracy of the predicted spectra is validated by comparing the flux of predicted spectra to those with same stellar parameters selected from the known spectral libraries,MILES and MaStar.The averaged difference of flux over optical wavelength between the predicted spectra and the corresponding ones in MILES and MaStar is less than 5%.More verification is conducted between the magnitudes calculated from the integration of the predicted spectra and the observations in PS1 and APASS bands with the same stellar parameters.No significant systematic difference is found between the predicted spectra and the photometric observations.The uncertainty is 0.08 mag in the r band for SSF-gM when comparing with the stars with the same stellar parameters selected from PS1.The uncertainty becomes 0.31 mag in the i band for SSF-dM when comparing with the stars with the same stellar parameters selected from APASS.
