The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
Background:New variants of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)continue to drive global epidemics and pose significant health risks.The pathogenicity of these variants evolves under immune press...Background:New variants of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)continue to drive global epidemics and pose significant health risks.The pathogenicity of these variants evolves under immune pressure and host factors.Understanding these changes is crucial for epidemic control and variant research.Methods:Human angiotensin-converting enzyme 2(hACE2)transgenic mice were in-tranasally challenged with the original strain WH-09 and the variants Delta,Beta,and Omicron BA.1,while BALB/c mice were challenged with Omicron subvariants BA.5,BF.7,and XBB.1.To compare the pathogenicity differences among variants,we con-ducted a comprehensive analysis that included clinical symptom observation,meas-urement of viral loads in the trachea and lungs,evaluation of pulmonary pathology,analysis of immune cell infiltration,and quantification of cytokine levels.Results:In hACE2 mice,the Beta variant caused significant weight loss,severe lung inflammation,increased inflammatory and chemotactic factor secretion,greater mac-rophage and neutrophil infiltration in the lungs,and higher viral loads with prolonged shedding duration.In contrast,BA.1 showed a significant reduction in pathogenicity.The BA.5,BF.7,and XBB.1 variants were less pathogenic than the WH-09,Beta,and Delta variants when infected in BALB/c mice.This was evidenced by reduced weight loss,diminished pulmonary pathology,decreased secretion of inflammatory factors and chemokines,reduced macrophage and neutrophil infiltration,as well as lower viral loads in both the trachea and lungs.Conclusion:In hACE2 mice,the Omicron variant demonstrated the lowest pathogenic-ity,while the Beta variant exhibited the highest.Pathogenicity of the Delta variant was comparable to the original WH-09 strain.Among BALB/c mice,Omicron subvari-ants BA.5,BF.7,and XBB.1 showed no statistically significant differences in virulence.展开更多
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help...Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.展开更多
Radio frequency capacitively coupled plasmas(RF CCPs)operated in Ar/O_(2)gas mixtures which are widely adopted in microelectronics,display,and photovoltaic industry,are investigated based on an equivalent circuit mode...Radio frequency capacitively coupled plasmas(RF CCPs)operated in Ar/O_(2)gas mixtures which are widely adopted in microelectronics,display,and photovoltaic industry,are investigated based on an equivalent circuit model coupled with a global model.This study focuses on the effects of singlet metastable molecule O_(2)(b^(1)∑_(8)^(+)),highly excited Herzberg states O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-)),and the negative ion O_(2)^(-),which are usually neglected in simulation studies.Specifically,their impact on particle densities,electronegativity,electron temperature,voltage drop across the sheath,and absorbed power in the discharge is analyzed.The results indicate that O_(2)(b^(1)∑_(8)^(+))and O_(2)^(-)exhibit relatively high densities in argon-oxygen discharges.While O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-))play a critical role in O_(2)b1S+g production,especially at higher pressure.The inclusion of these particles reduces the electronegativity,electron temperature,and key species densities,especially the O^(-)and O^(*)densities.Moreover,the sheath voltage drop,as well as the inductance and resistance of the plasma bulk are enhanced,while the sheath dissipation power and total absorbed power decrease slightly.With the increasing pressure,the influence of these particles on the discharge properties becomes more significant.The study also explores the generation and loss of main neutral species and charged particles within the pressure range of 20 mTorr-100 mTorr(1 Torr=1.33322×10^(2)Pa),offering insights into essential and non-essential reactions for future low-pressure O_(2)and Ar/O_(2)CCP discharge modeling.展开更多
Background:SARS-CoV-2,first identified in late 2019,has given rise to numerous variants of concern(VOCs),posing a significant threat to human health.The emer-gence of Omicron BA.1.1 towards the end of 2021 led to a pa...Background:SARS-CoV-2,first identified in late 2019,has given rise to numerous variants of concern(VOCs),posing a significant threat to human health.The emer-gence of Omicron BA.1.1 towards the end of 2021 led to a pandemic in early 2022.At present,the lethal mouse model for the study of SARS-CoV-2 needs supplementation,and the alterations in neutrophils and monocytes caused by different strains remain to be elucidated.Methods:Human ACE2 transgenic mice were inoculated with the SARS-CoV-2 proto-type and Omicron BA.1,respectively.The pathogenicity of the two strains was evalu-ated by observing clinical symptoms,viral load and pathology.Complete blood count,immunohistochemistry and flow cytometry were performed to detect the alterations of neutrophils and monocytes caused by the two strains.Results:Our findings revealed that Omicron BA.1 exhibited significantly lower vir-ulence compared to the SARS-CoV-2 prototype in the mouse model.Additionally,we observed a significant increase in the proportion of neutrophils late in infection with the SARS-CoV-2 prototype and Omicron BA.1.We found that the proportion of monocytes increased at first and then decreased.The trends in the changes in the proportions of neutrophils and monocytes induced by the two strains were similar.Conclusion:Our study provides valuable insights into the utility of mouse models for simulating the severe disease of SARS-CoV-2 prototype infection and the milder manifestation associated with Omicron BA.1.SARS-CoV-2 prototype and Omicron BA.1 resulted in similar trends in the changes in neutrophils and monocytes.展开更多
BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as...BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.展开更多
BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset D...BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset DF and develop a robust prediction model for hospitalized patients with type 2 diabetes.METHODS We included 6301 hospitalized patients with type 2 diabetes from January 2016 to December 2021.A univariate Cox model and least absolute shrinkage and selection operator analyses were applied to select the appropriate predictors.Nonlinear associations between continuous variables and the risk of DF were explored using restricted cubic spline functions.The Cox model was further employed to evaluate the impact of risk factors on DF.The area under the curve(AUC)was measured to evaluate the accuracy of the prediction model.RESULTS Seventy-five diabetic inpatients experienced DF.The incidence density of DF was 4.5/1000 person-years.A long duration of diabetes,lower extremity arterial disease,lower serum albumin,fasting plasma glucose(FPG),and diabetic nephropathy were independently associated with DF.Among these risk factors,the serum albumin concentration was inversely associated with DF,with a hazard ratio(HR)and 95%confidence interval(CI)of 0.91(0.88-0.95)(P<0.001).Additionally,a U-shaped nonlinear relationship was observed between the FPG level and DF.After adjusting for other variables,the HRs and 95%CI for FPG<4.4 mmol/L and≥7.0 mmol/L were 3.99(1.55-10.25)(P=0.004)and 3.12(1.66-5.87)(P<0.001),respectively,which was greater than the mid-range level(4.4-6.9 mmol/L).The AUC for predicting DF over 3 years was 0.797.CONCLUSION FPG demonstrated a U-shaped relationship with DF.Serum albumin levels were negatively associated with DF.The prediction nomogram model of DF showed good discrimination ability using diabetes duration,lower extremity arterial disease,serum albumin,FPG,and diabetic nephropathy(Clinicaltrial.gov NCT05519163).展开更多
Blended learning is an important practice of teaching reform in universities,which effectively integrates online and offline teaching resources.Through the participation of teachers in the learning process and helping...Blended learning is an important practice of teaching reform in universities,which effectively integrates online and offline teaching resources.Through the participation of teachers in the learning process and helping students construct knowledge,the teaching philosophy of“learning as the center”is realized,which plays an important role in improving the quality of teaching courses and cultivating professional talents.This article analyzes the problems in course teaching,proposes a hybrid teaching design strategy based on the ADDIO2OE model,analyzes the specific requirements of each stage,and conducts research and discussion to form a complete teaching model,aiming to deepen teaching reform and improve teaching quality.展开更多
Objective: To analyze the clinical effects of the patient participation health model in the health management of type 2 diabetes mellitus. Methods: A total of 124 patients with type 2 diabetes admitted to the hospital...Objective: To analyze the clinical effects of the patient participation health model in the health management of type 2 diabetes mellitus. Methods: A total of 124 patients with type 2 diabetes admitted to the hospital from June 2023 to June 2024 were randomly assigned to either the control group (64 patients) or the intervention group (60 patients). Patients in the control group received routine health management, while those in the intervention group were managed using a patient-participation health model with progressive, stage-based interventions. Outcomes were assessed based on blood glucose control, disease awareness, and self-management behaviors. Adverse reactions during health management were closely monitored in both groups. Results: Patients in the intervention group showed significantly better outcomes in blood glucose control, disease awareness, and self-management behaviors compared to the control group. Conclusion: The patient participation health model demonstrated significant clinical value, effectively enhancing self-management abilities, improving glycemic control, and increasing disease awareness. This model is recommended for widespread adoption in the health management of type 2 diabetes to achieve better therapeutic outcomes and improve patient quality of life.展开更多
Porous liquid-conducting micro-heat exchangers have garnered considerable attention for their role in efficient heat dissipation in small electronic devices.This demand highlights the need for advanced mathematical mo...Porous liquid-conducting micro-heat exchangers have garnered considerable attention for their role in efficient heat dissipation in small electronic devices.This demand highlights the need for advanced mathematical models to optimize the selection of mixed heat exchange media and equipment design.A capillary bundle evaporation model for porous liquid-conducting media was developed based on the conjugate mass transfer evaporation rate prediction model of a single capillary tube,supplemented by mercury injection experimental data.Theoretical and experimental comparisons were conducted using 1,2-propanediol-glycerol(PG-VG)mixtures at molar ratios of 1:9,3:7,5:5,and 7:3 at 120,150,and 180℃.The Jouyban-Acree model was implemented to enhance the evaporation rate predictions.For the 7:3 PG-VG mixture at 180℃under the experimental conditions of the thermal medium,the model's error reduced from 16.75%to 10.84%post-correction.Overall,the mean relative error decreased from 11.76%to 5.98%after correction.展开更多
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金National Science and Technology Infrastructure of China,Grant/Award Number:National Pathogen Resource Center-NPRC-32National Key Research and Development Program of China,Grant/Award Number:2023YFF0724800CAMS Innovation Fund for Medical Sciences,Grant/Award Number:2021-I2M-1-035。
文摘Background:New variants of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)continue to drive global epidemics and pose significant health risks.The pathogenicity of these variants evolves under immune pressure and host factors.Understanding these changes is crucial for epidemic control and variant research.Methods:Human angiotensin-converting enzyme 2(hACE2)transgenic mice were in-tranasally challenged with the original strain WH-09 and the variants Delta,Beta,and Omicron BA.1,while BALB/c mice were challenged with Omicron subvariants BA.5,BF.7,and XBB.1.To compare the pathogenicity differences among variants,we con-ducted a comprehensive analysis that included clinical symptom observation,meas-urement of viral loads in the trachea and lungs,evaluation of pulmonary pathology,analysis of immune cell infiltration,and quantification of cytokine levels.Results:In hACE2 mice,the Beta variant caused significant weight loss,severe lung inflammation,increased inflammatory and chemotactic factor secretion,greater mac-rophage and neutrophil infiltration in the lungs,and higher viral loads with prolonged shedding duration.In contrast,BA.1 showed a significant reduction in pathogenicity.The BA.5,BF.7,and XBB.1 variants were less pathogenic than the WH-09,Beta,and Delta variants when infected in BALB/c mice.This was evidenced by reduced weight loss,diminished pulmonary pathology,decreased secretion of inflammatory factors and chemokines,reduced macrophage and neutrophil infiltration,as well as lower viral loads in both the trachea and lungs.Conclusion:In hACE2 mice,the Omicron variant demonstrated the lowest pathogenic-ity,while the Beta variant exhibited the highest.Pathogenicity of the Delta variant was comparable to the original WH-09 strain.Among BALB/c mice,Omicron subvari-ants BA.5,BF.7,and XBB.1 showed no statistically significant differences in virulence.
基金supported by National Key Research and Development Program of China (2023YFB3307800)National Natural Science Foundation of China (Key Program: 62136003, 62373155)+1 种基金Major Science and Technology Project of Xinjiang (No. 2022A01006-4)the Fundamental Research Funds for the Central Universities。
文摘Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.
基金supported by the National Natural Science Foundation of China(Grant Nos.12020101005,12475202,12347131,and 12405289).
文摘Radio frequency capacitively coupled plasmas(RF CCPs)operated in Ar/O_(2)gas mixtures which are widely adopted in microelectronics,display,and photovoltaic industry,are investigated based on an equivalent circuit model coupled with a global model.This study focuses on the effects of singlet metastable molecule O_(2)(b^(1)∑_(8)^(+)),highly excited Herzberg states O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-)),and the negative ion O_(2)^(-),which are usually neglected in simulation studies.Specifically,their impact on particle densities,electronegativity,electron temperature,voltage drop across the sheath,and absorbed power in the discharge is analyzed.The results indicate that O_(2)(b^(1)∑_(8)^(+))and O_(2)^(-)exhibit relatively high densities in argon-oxygen discharges.While O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-))play a critical role in O_(2)b1S+g production,especially at higher pressure.The inclusion of these particles reduces the electronegativity,electron temperature,and key species densities,especially the O^(-)and O^(*)densities.Moreover,the sheath voltage drop,as well as the inductance and resistance of the plasma bulk are enhanced,while the sheath dissipation power and total absorbed power decrease slightly.With the increasing pressure,the influence of these particles on the discharge properties becomes more significant.The study also explores the generation and loss of main neutral species and charged particles within the pressure range of 20 mTorr-100 mTorr(1 Torr=1.33322×10^(2)Pa),offering insights into essential and non-essential reactions for future low-pressure O_(2)and Ar/O_(2)CCP discharge modeling.
基金supported by Beijing Natural Science Foundation(Grant No.Z210014)National Natural Science Foundation of China(Grant No.32070543)+1 种基金National Key Research and Development Project of China(Grant No.2022YFC2303404)CAMS Innovation Fund for Medical Sciences(CIFMS)(Grant No.2022-12M-CoV19-002)
文摘Background:SARS-CoV-2,first identified in late 2019,has given rise to numerous variants of concern(VOCs),posing a significant threat to human health.The emer-gence of Omicron BA.1.1 towards the end of 2021 led to a pandemic in early 2022.At present,the lethal mouse model for the study of SARS-CoV-2 needs supplementation,and the alterations in neutrophils and monocytes caused by different strains remain to be elucidated.Methods:Human ACE2 transgenic mice were inoculated with the SARS-CoV-2 proto-type and Omicron BA.1,respectively.The pathogenicity of the two strains was evalu-ated by observing clinical symptoms,viral load and pathology.Complete blood count,immunohistochemistry and flow cytometry were performed to detect the alterations of neutrophils and monocytes caused by the two strains.Results:Our findings revealed that Omicron BA.1 exhibited significantly lower vir-ulence compared to the SARS-CoV-2 prototype in the mouse model.Additionally,we observed a significant increase in the proportion of neutrophils late in infection with the SARS-CoV-2 prototype and Omicron BA.1.We found that the proportion of monocytes increased at first and then decreased.The trends in the changes in the proportions of neutrophils and monocytes induced by the two strains were similar.Conclusion:Our study provides valuable insights into the utility of mouse models for simulating the severe disease of SARS-CoV-2 prototype infection and the milder manifestation associated with Omicron BA.1.SARS-CoV-2 prototype and Omicron BA.1 resulted in similar trends in the changes in neutrophils and monocytes.
文摘BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.
基金Supported by National Natural Science Foundation of China,No.81972947Academic Promotion Programme of Shandong First Medical University,No.2019LJ005.
文摘BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset DF and develop a robust prediction model for hospitalized patients with type 2 diabetes.METHODS We included 6301 hospitalized patients with type 2 diabetes from January 2016 to December 2021.A univariate Cox model and least absolute shrinkage and selection operator analyses were applied to select the appropriate predictors.Nonlinear associations between continuous variables and the risk of DF were explored using restricted cubic spline functions.The Cox model was further employed to evaluate the impact of risk factors on DF.The area under the curve(AUC)was measured to evaluate the accuracy of the prediction model.RESULTS Seventy-five diabetic inpatients experienced DF.The incidence density of DF was 4.5/1000 person-years.A long duration of diabetes,lower extremity arterial disease,lower serum albumin,fasting plasma glucose(FPG),and diabetic nephropathy were independently associated with DF.Among these risk factors,the serum albumin concentration was inversely associated with DF,with a hazard ratio(HR)and 95%confidence interval(CI)of 0.91(0.88-0.95)(P<0.001).Additionally,a U-shaped nonlinear relationship was observed between the FPG level and DF.After adjusting for other variables,the HRs and 95%CI for FPG<4.4 mmol/L and≥7.0 mmol/L were 3.99(1.55-10.25)(P=0.004)and 3.12(1.66-5.87)(P<0.001),respectively,which was greater than the mid-range level(4.4-6.9 mmol/L).The AUC for predicting DF over 3 years was 0.797.CONCLUSION FPG demonstrated a U-shaped relationship with DF.Serum albumin levels were negatively associated with DF.The prediction nomogram model of DF showed good discrimination ability using diabetes duration,lower extremity arterial disease,serum albumin,FPG,and diabetic nephropathy(Clinicaltrial.gov NCT05519163).
文摘Blended learning is an important practice of teaching reform in universities,which effectively integrates online and offline teaching resources.Through the participation of teachers in the learning process and helping students construct knowledge,the teaching philosophy of“learning as the center”is realized,which plays an important role in improving the quality of teaching courses and cultivating professional talents.This article analyzes the problems in course teaching,proposes a hybrid teaching design strategy based on the ADDIO2OE model,analyzes the specific requirements of each stage,and conducts research and discussion to form a complete teaching model,aiming to deepen teaching reform and improve teaching quality.
文摘Objective: To analyze the clinical effects of the patient participation health model in the health management of type 2 diabetes mellitus. Methods: A total of 124 patients with type 2 diabetes admitted to the hospital from June 2023 to June 2024 were randomly assigned to either the control group (64 patients) or the intervention group (60 patients). Patients in the control group received routine health management, while those in the intervention group were managed using a patient-participation health model with progressive, stage-based interventions. Outcomes were assessed based on blood glucose control, disease awareness, and self-management behaviors. Adverse reactions during health management were closely monitored in both groups. Results: Patients in the intervention group showed significantly better outcomes in blood glucose control, disease awareness, and self-management behaviors compared to the control group. Conclusion: The patient participation health model demonstrated significant clinical value, effectively enhancing self-management abilities, improving glycemic control, and increasing disease awareness. This model is recommended for widespread adoption in the health management of type 2 diabetes to achieve better therapeutic outcomes and improve patient quality of life.
基金the funding support of National Natural Science Foundation of China(21978204)。
文摘Porous liquid-conducting micro-heat exchangers have garnered considerable attention for their role in efficient heat dissipation in small electronic devices.This demand highlights the need for advanced mathematical models to optimize the selection of mixed heat exchange media and equipment design.A capillary bundle evaporation model for porous liquid-conducting media was developed based on the conjugate mass transfer evaporation rate prediction model of a single capillary tube,supplemented by mercury injection experimental data.Theoretical and experimental comparisons were conducted using 1,2-propanediol-glycerol(PG-VG)mixtures at molar ratios of 1:9,3:7,5:5,and 7:3 at 120,150,and 180℃.The Jouyban-Acree model was implemented to enhance the evaporation rate predictions.For the 7:3 PG-VG mixture at 180℃under the experimental conditions of the thermal medium,the model's error reduced from 16.75%to 10.84%post-correction.Overall,the mean relative error decreased from 11.76%to 5.98%after correction.
