ObjectiveTo investigate the influence of empagliflozin combined with donafenib or lenvatinib on the pharmacokinetic parameters of each drug， and to provide a reference for combined medication in clinical practice. MethodsA total of 48 healthy male Sprague-Dawley rats were divided into 8 groups： empagliflozin group 1 and 2， donafenib group， lenvatinib group， donafenib pretreatment+empagliflozin group， lenvatinib pretreatment + empagliflozin group， empagliflozin pretreatment+donafenib group， and empagliflozin pretreatment+lenvatinib group， with 6 rats in each group. The doses of empagliflozin， donafenib， and lenvatinib were 2.5 mg/kg， 40 mg/kg， and 1.2 mg/kg， respectively. The rats in the empagliflozin group， donafenib group， and lenvatinib group were given a blank solvent by gavage for 7 consecutive days， followed by a single dose of empagliflozin， donafenib， or lenvatinib on day 7 after the administration of the blank solvent； the rats in the pretreatment groups were given the pretreatment drug by gavage for 7 consecutive days， followed by a single dose of drug combination on day 7 after administration of the pretreatment drug. Blood samples were collected at different time points， and plasma was separated to measure the concentration of each drug. A validated ultra-performance liquid chromatography-tandem mass spectrometry method was used to measure the plasma concentrations of donafenib， lenvatinib， and empagliflozin， and a non-compartmental model was used to calculate the main pharmacokinetic parameters of each drug （area under the plasma concentration-time curve ［AUC］， time to peak ［T_max］， peak concentration ［C_max］， and half-life time ［t_1/2］）. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups. ResultsCompared with the empagliflozin group， the donafenib pretreatment+empagliflozin group had significant increases in the AUC_0-t and AUC_0-∞ of empagliflozin （P=0.011 and 0.008）， while the lenvatinib pretreatment+empagliflozin group had no significant change in the AUC of empagliflozin， with a slightly shorter T_max （P=0.019）. Compared with the donafenib group， the empagliflozin pretreatment+donafenib group had significant increases in the AUC_0-t and AUC_0-∞ of donafenib （P=0.027 and 0.025）， as well as a significant increase in C_max （P=0.015） and significant reductions in CLz/F and Vz/F （P=0.005 and 0.004）； compared with the lenvatinib group， the empagliflozin pretreatment+lenvatinib group had a reduction in the t_1/2 of lenvatinib by approximately 5 hours （P=0.002）， with a trend of reduction in AUC_0-t （P0.05）. ConclusionEmpagliflozin combined with donafenib may alter the pharmacokinetic parameters of both drugs， leading to a significant increase in the exposure levels of both drugs， and efficacy and adverse reactions should be monitored during co-administration. There are no significant changes in the exposure levels of empagliflozin and lenvatinib during co-administration.

Objective: To investigate the spatio-temporal clustering characteristics of influenza in Ningxia Hui Autonomous Region from 2014 to 2023, so as to provide the basis for strengthening influenza prevention and control. Methods: Data pertaining to influenza cases reported in Ningxia Hui Autonomous Region from 2014 to 2023 were retrieved from the Infectious Disease Surveillance System of the Chinese Disease Prevention and Control Information System, including age, sex, current residence, onset date, and reporting date. The seasonal incidence of influenza was analyzed using seasonal index. The spatio-temporal clustering characteristics of influenza were identified using spatial autocorrelation analysis and spatio-temporal scan analysis. Results: A total of 20 377 influenza cases were reported in Ningxia Hui Autonomous Region from 2014 to 2023, with a male-to-female ratio of 1.15∶1. The majority were children under 15 years, with 10 950 cases accounting for 53.74%. Influenza was highly prevalent in January, February, March, and December, with seasonal indices of 219.06%, 111.00%, 246.65%, and 366.24%, respectively. The average annual reported incidence was 29.55/100 000, among which Pengyang County, Jinfeng District, Dawukou District, Xiji County, and Litong District had higher average annual reported incidence, at 63.99/100 000, 55.71/100 000, 55.70/100 000, 49.49/100 000, and 49.04/100 000, respectively. Spatial autocorrelation analysis showed that in 2023, there was spatial clustering of influenza cases in Ningxia Hui Autonomous Region (Moran's I=0.333, P<0.05), with a high-high cluster in Jingyuan County, while in other years, the distribution of influenza cases was random (all P>0.05). Spatio-temporal scan analysis showed that from 2014 to 2023, there were four space-time clusters in Ningxia Hui Autonomous Region, including one type Ⅰ cluster in Hongsibao District of Wuzhong City, with the clustering period from January 20 to 26, 2014; and three type Ⅱ clusters, mainly in January, February, March and December, covering one area in Shizuishan City, five areas in Guyuan City, one area in Zhongwei City, three areas in Wuzhong City, and four areas in Yinchuan City. Conclusions From 2014 to 2023, children under 15 years were the primary population affected by influenza in Ningxia Hui Autonomous Region, with distinct spatio-temporal distribution characteristics. The peak incidence occurred during the winter and spring seasons, and the main clustering areas were in the southern regions.

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks

Objective To explore the mediating role of activities of daily living (ADL) in pain and depressive symptoms in the elderly in China. Methods Utilizing the data from 2020 China Health and Retirement Longitudinal Study, 4403 Chinese elderly individuals aged ≥ 60 years old were selected as the research subjects. Depression Scale (CES-D 10) of the Center for Epidemiological Survey and ADL scale were used in the study. The PROCESS4.1 macro was used to test the mediating effect of daily living activities between pain and depressive symptoms, and the Bootstrap method was applied for verification of the mediating variables. Results A total of 2368 cases of depressive symptoms were detected in the elderly in China, with a detection rate of 53.78%. Pain was positively correlated with depressive symptoms (r=0.27, P<0.01), and activities of daily living were negatively correlated with pain and depressive symptoms (r=-0.27, -0.337, P<0.01). The results showed that the total effect value of pain on depressive symptoms was 0.33, the direct effect value was 0.24, and the mediating effect value of daily living activities was 0.09, accounting for 27.27%. Conclusion Pain and activities of daily living are important factors influencing depressive symptoms in the elderly, and activities of daily living play a partial mediating role in the relationship between pain and depressive symptoms in the elderly.

Objective:This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depression in the elderly using the shapley additive interpretation (SHAP) method.Methods:Based on entries from the 2018 China Health and Retirement Longitudinal Study database, a sample of 5 954 elderly individuals was selected. Feature selection using Support Vector Machine Recursive Feature Elimination, Extreme Gradient Boosting (XGBoost) - Recursive Feature Elimination (RFE), and the Lasso algorithm, which was combined with five classifiers-logistic regression, decision trees, random forests, support vector machines, and XGBoost-to explore the classification effectiveness for depressive symptoms in the elderly. Finally, the SHAP method was used to interpret the analysis of the model with the highest receiver operating characteristic curve areas under the curve (AUC).Results:The accuracy of 15 prediction models ranged from 0.702 to 0.743, with AUC between 0.730 and 0.795. Sensitivity was reported at 0.546 to 0.588, while specificity ranges from 0.783 to 0.865. The model XGBoost-RFE-XGBoost presented the highest AUC. Based on SHAP values, the top four factors influencing depressive symptoms in older adults were life satisfaction, duration of nighttime sleep, disability status, and self-rated health.Conclusion:This study developed a highly efficient and interpretable risk prediction model for depressive symptoms in older adults, which could help identify high-risk older adults and give personalized interventions.

Type 2 diabetes mellitus(T2DM)is an insulin resistance disease.Improving insulin resis-tance and controlling blood glucose are the main means of clinical treatment for T2DM.Empa-gliflozin is a highly selective sodium-dependent glu-cose transporters(SGLT)2 inhibitor,which is inde-pendent of insulin.It can effectively control blood glucose levels,reduce blood pressure and body weight,protect heart and kidney function,reduce the rehospitalization rate and the risk of death in patients with heart failure(HF),and does not in-crease the risk of hypoglycemia.Empagliflozin can be used alone or in combination with other hypo-glycemic drugs to control blood glucose.This arti-cle reviews the mechanism of action,clinical bene-fits,and combination with other drugs of empa-gliflozin,aiming to provide reference for the clinical use of empagliflozin.

Objective:This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depression in the elderly using the shapley additive interpretation (SHAP) method.Methods:Based on entries from the 2018 China Health and Retirement Longitudinal Study database, a sample of 5 954 elderly individuals was selected. Feature selection using Support Vector Machine Recursive Feature Elimination, Extreme Gradient Boosting (XGBoost) - Recursive Feature Elimination (RFE), and the Lasso algorithm, which was combined with five classifiers-logistic regression, decision trees, random forests, support vector machines, and XGBoost-to explore the classification effectiveness for depressive symptoms in the elderly. Finally, the SHAP method was used to interpret the analysis of the model with the highest receiver operating characteristic curve areas under the curve (AUC).Results:The accuracy of 15 prediction models ranged from 0.702 to 0.743, with AUC between 0.730 and 0.795. Sensitivity was reported at 0.546 to 0.588, while specificity ranges from 0.783 to 0.865. The model XGBoost-RFE-XGBoost presented the highest AUC. Based on SHAP values, the top four factors influencing depressive symptoms in older adults were life satisfaction, duration of nighttime sleep, disability status, and self-rated health.Conclusion:This study developed a highly efficient and interpretable risk prediction model for depressive symptoms in older adults, which could help identify high-risk older adults and give personalized interventions.

Type 2 diabetes mellitus(T2DM)is an insulin resistance disease.Improving insulin resis-tance and controlling blood glucose are the main means of clinical treatment for T2DM.Empa-gliflozin is a highly selective sodium-dependent glu-cose transporters(SGLT)2 inhibitor,which is inde-pendent of insulin.It can effectively control blood glucose levels,reduce blood pressure and body weight,protect heart and kidney function,reduce the rehospitalization rate and the risk of death in patients with heart failure(HF),and does not in-crease the risk of hypoglycemia.Empagliflozin can be used alone or in combination with other hypo-glycemic drugs to control blood glucose.This arti-cle reviews the mechanism of action,clinical bene-fits,and combination with other drugs of empa-gliflozin,aiming to provide reference for the clinical use of empagliflozin.

Objective To investigate the changing trend and epidemiological characteristics of the incidence and mortality of chronic kidney disease (CKD) with age, period and birth cohort in Chinese population. Methods Based on the data of incidence and mortality of CKD in Chinese population aged 20-80 years from 1990 to 2019 in GHDx database, joinpoint regression model was used to analyze the incidence and mortality trend of CKD. An age-period-cohort model was constructed to analyze the effects of age, period, and birth cohort on the trend of CKD incidence and mortality. Results Joinpoint regression analysis showed that the standardized incidence rate of chronic kidney disease in Chinese population increased from 146.37/100 000 in 1990 to 161.52/100 000 in 2019, while the standardized mortality rate decreased from 12.98/100 000 in 1990 to 11.23/100 000 in 2019. The APC model analysis showed that the risk of CKD incidence and death in the Chinese population increased with age, while the risk of CKD incidence increased with the increase of period. The risk of death did not change significantly with the increase of period. The cohort born later had a lower risk of CKD incidence and death compared to the cohort born earlier. Conclusion At present, the age effect and period effect of the incidence and death risk of chronic kidney disease in China are dominant. It is important to take effective measures and intervene in a timely manner, especially to strengthen the protection of older high-risk groups born earlier.

Objective　To explore the mediating role of cognitive function in the association between literacy level and depressive symptoms in middle-aged and elderly people in China. Methods　 Using the fourth national follow-up data of the China Health and Elderly Care Tracking Survey 2018, 8 124 middle-aged and elderly people aged 45 years and above were included as the study subjects. The PROCESS 4.0 program was used to test the mediating effect of cognitive function between literacy level and depressive symptoms, and the Bootstrap method was used for the mediator variable validation. Results The detection rate of depressive symptoms among middle-aged and elderly people in China was 38.10%. After controlling for gender, place of residence, marital status, smoking, alcohol consumption, and exercise, literacy level was a negative predictor of depressive symptoms in middle-aged and elderly people (β =－0.480, t =－11.248, P<0.001). Cognitive function accounted for 58.75% of the amount of mediating effect between literacy level and depressive symptoms. Conclusion 　Literacy level and cognitive function are associated with depressive symptoms in middle-aged and elderly people. Literacy level can influence depressive symptoms directly or indirectly through the mediation of cognitive dysfunction.