To explore the mediating roles of sleep duration and depressive symptoms in the relationship between instrumental activities of daily living (IADL) and self-rated health (SRH) among older adults.

A total of 8 363 older adults (≥60 years) from the 2020 China Health and Retirement Longitudinal Study (CHARLS) were included. Spearman’s rank correlation analysis was employed to assess correlations among IADL, sleep duration, depressive symptoms, and SRH. Bootstrap analysis was used to examine the mediating effects of sleep duration and depressive symptoms on the association between IADL and SRH.

IADL exerted a significant direct effect on SRH (β=-0.074, 95% CI: -0.081~-0.066). Significant mediating effects were observed for sleep duration alone (β=-0.002, 95% CI: -0.003~-0.001), depressive symptoms alone (β=-0.029, 95% CI: -0.032~-0.001), and the chained pathway through sleep duration and depressive symptoms (β=-0.002, 95% CI: -0.003~-0.001).

IADL limitations may directly and negatively impact SRH in older adults, and may also indirectly affect SRH through the independent or sequential mediating effects of sleep duration and depressive symptoms.Interventions aimed at enhancing IADL capacity, optimizing sleep duration, and alleviating depressive symptoms could improve SRH and promote physical and mental well-being among older adults.

To explore the mechanism of soy isoflavones (SIF) in improving memory in aging ovariectomized rats.

Forty 8-week-old SD female rats were randomly assigned to the sham control (CON), aging combined with ovariectomy(OVX/D-Gal), 160 mg/kg SIF (SIF) and 0.8 mg/kg estradiol replacement therapy (ERT). Except for the CON group, rats in other groups underwent bilateral ovariectomy combined with intraperitoneal injection of D-galactose (100 mg/kg·d) for 12 weeks.After the intervention, memory was assessed using the shuttle box test. Transmission electron microscopy was used to observe the synaptic ultrastructure of hippocampal neurons. The protein P53, PSD-95, GluA1, P38 and the phosph-P38 (p-P38)expressions were measured by Western blot. The levels of TNF-α and estradiol were detected by ELISA. One-way ANOVA method was used to analyze the overall differences. LSD method was used to analyze the pairwise comparison between groups with homogeneous variance, and Dunnett’s test was used to analyze those with uneven variance, with α=0.05.

Significant differences were observed among the groups in avoidance latency time (ALT), active avoidance response rate (AAR),synaptic interface curvature (SIC), synaptic cleft width (SCW), postsynaptic density (PSD) thickness, serum E2 levels, protein expressions of P53, PSD-95, GluA1 and P38 in the hippocampus, and p-P38/P38 (F-value=10.905, P-value<0.010; F-value=9.825, P-value<0.010; F-value=8.027, P-value<0.010; F-value=124.228, P-value<0.001; F-value=24.9810, P-value<0.001; F-value=16.410, P-value<0.001; F-value=5.667, P-value<0.050; F-value=6.091, P-value<0.050; F-value=8.766, P-value<0.050; F-value=25.218, P-value<0.001; F-value=19.014, P-value<0.001). Compared with CON group, rats in the OVX/D-Gal group exhibited significantly reduced serum E2 and increased hippocampal P53, accompanied by prolonged ALT and decreased AAR. The SIC and PSD thickness declined, and the SCW widened. The level of TNF-α and the ratio of p-P38/P38 in the hippocampus increased, while the protein expressions of P38, PSD-95 and GluA1 were decreased (all P-value<0.050).In the SIF group, there was no significant change in serum E2 level compared to the OVX/D-Gal group, while P53 expression was decreased. The ALT shortened and the AAR increased. Besides, higher SIC, increased PSD thickness, and narrower SCW were observed in the SIF group. The level of TNF-α and the ratio of p-P38/P38 in the hippocampus declined, while the protein expressions of PSD-95, GluA1 and P38 increased (all P-value<0.050).

SIF can improve the memory ability of aging rats with ovariectomy, which may be related to the enhancement of synaptic plasticity through inhibiting TNF-α and its downstream P38MAPK, thereby increasing GluA1 expression.

To compare and analyze the reported incidence of adverse events following immunization (AEFI) before and after the adjustment of the poliomyelitis immunization schedule in Tianjin.

We obtained AEFI case data and the vaccine doses for poliomyelitis vaccine from the Immunization Program Information System from May 2016 to December 2019(pre-adjustment) and from January 2020 to September 2024 (post-adjustment). Reported incidence rates were calculated and compared between the periods.

Before the adjustment, the reported incidence rates of AEFI in Tianjin were 37.00 per 100 000 doses for bOPV and 50.52 per 100 000 doses for IPV, the incidence rates of common adverse reactions were 30.06 per 100 000 doses (bOPV) and 43.87 per 100 000 doses (IPV), while those of abnormal reactions were 5.26 per 100 000 doses(bOPV) and 5.32 per 100 000 doses (IPV). After the adjustment, the reported incidence rates of AEFI declined to 26.70 per 100 000 doses for bOPV and 33.13 for IPV per 100 000 doses, the incidence rates of common adverse reactions were 21.10 per 100 000 doses (bOPV) and 28.24 per 100 000 doses (IPV), and abnormal reactions were 4.62 per 100 000 doses (bOPV) and 3.57 per 100 000 doses (IPV). There were statistically significant differences in the reported incidence of AEFI before and after the adjustment (χ²=7.45, P<0.01; χ²=24.20, P<0.01).

After the adjustment to the immunization strategy of polio vaccine in Tianjin, AEFI reporting rates decreased, with rare serious adverse reactions and favorable overall safety.

To analyze the drug resistance and molecular network characteristics of HIV/AIDS treatment failure patients in Urumqi City, and provide a theoretical basis for AIDS prevention and control.

A total of 1 281 patients who received antiviral treatment in AIDS antiviral designated medical institutions in Urumqi City from 2018 to 2023 and had a viral load (VL) ≥1 000 copies/mL were classified as treatment failure patients and included. The pol region gene of HIV-1 was amplified by nested PCR, and a phylogenetic tree was constructed using the neighbor-joining method. χ² text and logistic regression were used to analyze the factors related to drug resistance, and a molecular network was constructed using 1.5%optimal gene distance. The drug resistance mutation sites were analyzed through the online tool HIV drug resistance database of Stanford University in the United States.

Five gene subtypes were identified in 1 281 sequences, with the dominant subtype being CRF07_BC (92.58%). The HIV-1 resistance rate was 55.27%, while the resistance rates to Nucleoside reverse transcriptase inhibitors (NRTIs), Non-nucleoside reverse transcriptase inhibitors (NNRTIs), and Protease inhibitors (PIs) were 25.53%, 48.40%, and 7.57%. The main resistance sites were K103 (29.98%), M184 (22.79%), and Q58 (5.31%). Multivariable logistic regression indicated lower risk of drug resistance among females (OR=0.686, 95% CI=0.538-0.873), those aged 25-49 years (OR=0.444, 95% CI=0.245-0.804), patients on AZT+3TC+NVP/EFV (OR=0.506, 95% CI=0.375-0.683), and those with baseline CD4+ T-cell counts of 200-349 (cells/μL) (OR=0.547, 95% CI=0.400-0.750). By constructing a molecular network, a total of 62 transmission clusters were identified, with 402 patients entering the network, resulting in a network entry rate of 31.38%. A total of 173 patients developed drug resistance, with a resistance rate of 43.03%. Male patients had a higher network entry rate than female patients,and genetic subtypes CRF07_BC, CRF01_AE, and CRF55_01B all appeared in clusters.

The drug resistance rate of HIV/AIDS treatment failure patients in Urumqi is relatively high, and drug-resistant strains appear in clusters in the molecular network. It is necessary to strengthen drug resistance monitoring and reduce the clustered transmission of drug-resistant strains.

To investigate the association between the serum uric acid to albumin ratio (UAR) and hypertension.

This study included data from 23 552 U.S. adults from the NHANES database(2007—2016). Multivariable logistic regression models and smooth curve fitting were used to examine the linear and nonlinear relationships between UAR and hypertension. Threshold effect analysis was applied to identify inflection points, and subgroup analyses were conducted to assess the association across different populations. Finally, the diagnostic performance of UAR for hypertension was evaluated using receiver operating characteristic(ROC) curves.

After adjusting for all confounding factors, multivariable logistic regression analysis showed that higher UAR was significantly associated with an increased prevalence of hypertension (OR=1.11, 95% CI: 1.09-1.13). Compared with the low UAR group, the middle (OR=1.14, 95% CI: 1.05-1.24) and high (OR=1.46, 95% CI: 1.33-1.60) UAR groups had significantly increased risks of hypertension(P<0.05). Smooth curve fitting revealed a J-shaped nonlinear relationship between UAR and hypertension. Threshold effect analysis indicated that below the threshold of 4.66, UAR was negatively correlated with hypertension (OR=0.75, 95% CI: 0.61-0.92), whereas above 4.66, a positive correlation was observed (OR=1.12, 95% CI: 1.10-1.14). Consistent results were obtained in subgroup analyses, and interaction effects were observed across subgroups stratified by sex, age, alcohol consumption, BMI, and diabetes (P<0.05). The ROC curve demonstrated that UAR had moderate predictive ability for hypertension, with an area under the curve (AUC) of 0.639.

UAR is closely associated with hypertension and may serve as a promising biomarker for assessing early hypertension risk.

To investigate the longitudinal association between sarcopenic obesity and cardiovascular disease (CVD) in the elderly in China.

Data from the China Health and Retirement Longitudinal Study (CHARLS) from 2015 to 2020 were used. A total of 4 349 elderly people aged 60 years and above were enrolled and divided into four groups based on body roundness index (BRI) and sarcopenia status,namely (1) normal BRI without sarcopenia group; (2) normal BRI with sarcopenia group; (3) high BRI without sarcopenia group; and (4) high BRI with sarcopenia group. Kaplan-Meier curves were used to estimate the cumulative incidence of CVD, stroke, and heart disease at different times. Multivariable-adjusted Cox proportional hazards regression models were then used to control for confounding factors to analyze the associations between sarcopenic obesity status and CVD, stroke, and heart disease. Finally, subgroup analyses were performed to test the consistency of the association effects of the four groups on CVD, stroke, and cardiac events.

Based on different sarcopenic obesity status, Kaplan-Meier survival curves showed differences in cumulative incidence between different groups (P<0.001). After full adjustment of covariates, high BRI sarcopenia significantly increased the risk of CVD (HR=1.41, 95% CI: 1.14-1.75), stroke(HR=1.77, 95% CI: 1.26-2.49) and heart disease (HR=1.32, 95% CI: 1.03-1.69) compared with normal BRI without sarcopenia. This further proved that sarcopenic obesity was an important risk factor for CVD, stroke and heart disease. In the subgroup analysis, it was consistently found that high BRI with sarcopenia was associated and CVD, stroke and heart disease.

In the elderly population in China, high BRI with sarcopenia is significantly associated with the risk of CVD,stroke, and heart disease. It is recommended that preventing and improving sarcopenia, controlling weight and exercising moderately can help delay the progression of CVD and promote healthy aging in the elderly.

To quantitatively assess the causal association between tuberculosis（TB）prevention and control models in Xinjiang and the number of TB incidence in Kashgar region, and to provide a scientific basis for optimising TB prevention and control strategies.

Monthly reported incidence data of tuberculosis in Kashgar region from 2013 to 2023 were collected in this study. The epidemiological profile of TB in Kashgar was described using Joinpoint regression, and a Bayesian structural time series model was used to construct a counterfactual simulation of the number of TB cases in Kashgar to evaluate the effectiveness of the ‘Xinjiang model’ in a multidimensional way.

Joinpoint regression analysis showed that the average annual percentage change（APC）reversed from +21.24% before the policy implementation to -25.06%（P<0.05）, which was highly consistent with the time of policy initiation. The Bayesian structural time series model reflected inter-regional heterogeneity: the effectiveness of the intervention in Kashgar was outstanding（posterior probability 99.4%, P=0.006）, with a cumulative total of 30 659 TB cases prevented.

By using Joinpoint regression to capture the development trend of tuberculosis, and relying on the counterfactual framework to deduct the spatial and temporal heterogeneity, it was confirmed that the Xinjiang model could effectively control the existing tuberculosis epidemic in Kashgar, and provide scientific support for the optimisation of the policy of infectious disease prevention and control, as well as the practice of precision prevention and control in Xinjiang.

To develop and validate a risk prediction model for liver cirrhosis applicable to the general population, to reveal the association between common biomarkers and the risk of liver cirrhosis, and to provide a scientific basis for the assessment of liver cirrhosis risk in clinical and public health practice.

This study utilized data on 28 biomarkers and 6 covariates from the UK Biobank. Participants were enrolled between 2006 and 2010 and followed up until October 31, 2022. The primary outcome was new cases of liver cirrhosis. All participants meeting the inclusion and exclusion criteria were randomly divided into a training set and a test set at a ratio of 7:3. Based on the training set, three machine learning algorithms, namely XGBoost, Lasso-Cox regression, and random survival forest, were used to screen for key predictive variables influencing the occurrence of liver cirrhosis. Key feature variables were then used to construct a liver cirrhosis risk prediction model based on the Cox proportional hazards model. The performance of the prediction model was evaluated by comparing it with traditional scores, FIB-4 score and APRI score, and validated in the validation set.

A total of 237 965 eligible subjects were screened for this study, including 1 064 patients with liver cirrhosis and 236 801 non-liver cirrhosis patients. Five key variables influencing the occurrence of liver cirrhosis were identified by combining three machine learning methods and clinical application scenarios, namely γ-glutamyl transferase, aspartate aminotransferase, cystatin C, body mass index, and albumin. The liver cirrhosis risk prediction model constructed based on these five characteristic variables significantly outperformed the traditional FIB-4 score model and APRI score model in terms of predictive performance. The AUC values of the liver cirrhosis risk prediction model, FIB-4 score model, and APRI score model in the training set were 0.827（0.806-0.848）, 0.665（0.637-0.693）, and 0.740（0.713-0.766）, respectively; in the validation set, they were 0.811（0.776-0.845）, 0.664（0.621-0.708）, and 0.711（0.668-0.755）, respectively. The AUC values of the prediction model established in this study were superior to those of the two traditional scoring prediction models, and the differences were statistically significant（P<0.05）. Compared with the traditional FIB-4 score model and APRI score model, the liver cirrhosis risk prediction model constructed in this study demonstrated significant advantages in the two key indicators of net reclassification index（NRI）and integrated discrimination improvement index（IDI）（P<0.001）, and the results were highly consistent in both the training set and the test set. The results of the liver cirrhosis risk prediction model showed that cystatin C had high specificity and predictive value in the prediction of liver fibrosis diseases [HR=2.51, 95% CI: 2.22-2.83].

γ-glutamyl transferase, aspartate aminotransferase, cystatin C, body mass index and albumin are important factors influencing the onset of liver cirrhosis. The prediction model for the risk of liver cirrhosis constructed by using the feature variables obtained through multiple screening by machine learning has strong predictive ability and provides an effective tool for the early screening of the risk of liver cirrhosis.

To improve the structural quality of influenza surveillance data and enhance the performance of predictive models by leveraging an epidemic dynamics-informed data assimilation approach.

This study constructed a data assimilation framework that combined a Susceptible–Infectious–Recovered–Susceptible（SIRS）model with the Ensemble Adjustment Kalman Filter（EAKF）to assimilate weekly Goldstein indices（Influenza-like Illness Percentage × Positive Rate）from 21 cities in Sichuan Province. The signal-to-noise ratio（SNR）was used to evaluate the effect of data structure optimization, and predictive performance before and after assimilation was assessed using three forecasting models: ARIMA, RNN, and LSTM. Linear mixed-effect models were applied to quantify the impact of assimilation on SNR and root mean squared error（RMSE）. Statistical significance was defined as P<0.05.

After applying the SIRS-EAKF assimilation framework, the SNR significantly increased in 18 cities（85.7%）, with an average improvement of 3.26（P<0.01）. Across all three prediction models, RMSE were significantly reduced in the assimilated data group（all P<0.05）.

The SIRS-EAKF framework effectively suppressed the noise and enhanced the expression of the dominant epidemic signal through incorporating dynamical mechanisms. Without altering the structure of the prediction model, it significantly improved both the quality of influenza surveillance data and the predictive accuracy. This approach provides a robust methodological foundation for real-time epidemic forecasting and public health decision-making.

To compare the spatial-temporal multicomponent model with different spatial weight settings in different terrains, and to explore the optimal weight settings in different terrains. Then to study the distribution and influencing factors of influenza incidence in different terrains of Sichuan Province, so as to provide scientific basis for spatial weight settings and identification of risk influencing factors as well as the formulation of prevention and control measures.

Based on the influenza incidence data in Sichuan Province from 2016 to 2018, we conducted a descriptive analysis of the spatial and temporal influenza situation in different terrain areas（plains, hills and plateau mountainous regions）, explored the optimal weight settings for different terrains using a spatial-temporal multicomponent model, and further analyzed the spatial and temporal influenza propagation dynamics through the optimal model to quantify the effects of meteorological and socio-economic factors.

The optimal weights were inverse distance weights for plains and Power-law weights for hills and plateau and mountainous regions. The results of the factor analysis of the optimal model showed that the average temperature in the plains（RR=0.970, 95% CI: 0.958-0.982, P<0.001）, hills（RR=0.960, 95% CI: 0.942-0.978, P<0.001）and plateau and mountainous regions（RR=0.913, 95% CI: 0.868-0.960, P<0.001）were all inhibitory to the risk of influenza. Air pressure was protective in the plains（RR=0.990, 95% CI: 0.979-1.000, P=0.049）, while showing elevated risk in the hills（RR=1.044, 95% CI: 1.016-1.072, P=0.002）and plateau and mountainous regions（RR=1.005, 95% CI: 1.002-1.009, P=0.004）.

The spatial weights of plains, hills, plateau and mountainous regions should be carefully considered when constructing the model. Inverse distance weights are more suitable for plains, while Power-law weights are more suitable for hills, plateau and mountainous regions. Meanwhile, there are differences in the influence of influenza morbidity risk in different topographic areas, and it is suggested that different early warning and control measures should be developed according to the key risk factors in different areas and key risk characteristics of the areas.