Objective This study conducted molecular biological identification of the viruses carried by Culex pipiens pallens specimens collected in Shunyi District, Beijing in 2023, and observed the changes in the infection rate of the viruses carried by Cx. pipiens pallens at different collection times. Methods Cx. pipiens pallens were collected using carbon dioxide mosquito traps. The mosquito samples were ground in batches and analyzed by molecular biology technologies. The virus infection rate at different collection times was analyzed statistically. Results 17 strains of Culex pipiens pallens densovirus(CppDNV)were identified from Cx. pipiens pallens samples collected in Shunyi District, Beijing, in 2023. The nucleotide sequence analysis of the virus genome coding region showed that CppDNV was a single-stranded DNA virus with a total length of 3 335 nt, encoding 2 non-structural proteins(NS1, NS2)and 1 capsid protein(VP). The nucleotide(amino acid)sequence lengths of the three proteins were 2 376 nt(791 aa),1 092 nt(363 aa)and 1 071 nt(356 aa), respectively. Phylogenetic analysis showed that CppDNV was located in genus Brevihamaparvovirus. Statistical analysis showed no significant difference in infection rates across collection times(χ2=4.429, P=0.194). Conclusions CppDNV was identified in Cx. pipiens pallens in Beijing, and it was stably maintained in this natural population.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Aim To explore the molecular mechanism of Tongluo Tangtai(TLTT)prescription in the preven-tion and treatment of diabetic peripheral neuropathy(DPN)based on network pharmacology and in vitro experimental verification.Methods The chemical composition information of Tongluo Tangtai was searched by TCMSP database and literature search,and the target of chemical composition was collected by PubChem and SwissTargetPrediction database.DPN targets were collected through GeneCards database.GO function and KEGG pathway enrichment of common target proteins were analyzed using DAVID database.The correlation network diagram was constructed using Cytoscape software,and the main active components and target genes were screened for molecular docking study.The in vitro model of DPN was constructed in Schwann cells induced by high glucose.The effect of TLTT on the survival rate of RSC96 cells was detected by CCK-8 method,and the gene expression of Wnt/β-catenin pathway related target molecules in RSC96 cells was detected by Real-time PCR.The expression levels of Wnt/β-catenin pathway-related proteins in RSC96 cells were detected by Western blot.Results The main active components such as stigmasterol,β-si-tosterol and quercetin were screened,which mainly ac-ted on EGFR,AKT1,MAPK3 and Wnt,PI3K-Akt and MAPK signaling pathways.The molecular docking re-sults showed that stigmasterol,β-sitosterol,quercetin and other components could dock well with EGFR,AKT1,MAPK3 and other targets.The results of cell experiment showed that 10％TLTT drug-containing ser-um could promote the viability of high-glucose Schwann cells,up-regulate the expression of β-catenin protein,and down-regulate the expression of GSK-3β and Wif-1 protein.Conclusions TLTT has the characteristics of multi-target-multi-pathway in the treatment of DPN.The possible mechanism is that TLTT activates the Wnt/β-catenin signaling pathway,improves the inhibi-tory effect of high glucose on the proliferation of Schwann cells,promotes the proliferation of Schwann cells,and thus improves the status of DPN.

Aim To explore the molecular mechanism of Tongluo Tangtai(TLTT)prescription in the preven-tion and treatment of diabetic peripheral neuropathy(DPN)based on network pharmacology and in vitro experimental verification.Methods The chemical composition information of Tongluo Tangtai was searched by TCMSP database and literature search,and the target of chemical composition was collected by PubChem and SwissTargetPrediction database.DPN targets were collected through GeneCards database.GO function and KEGG pathway enrichment of common target proteins were analyzed using DAVID database.The correlation network diagram was constructed using Cytoscape software,and the main active components and target genes were screened for molecular docking study.The in vitro model of DPN was constructed in Schwann cells induced by high glucose.The effect of TLTT on the survival rate of RSC96 cells was detected by CCK-8 method,and the gene expression of Wnt/β-catenin pathway related target molecules in RSC96 cells was detected by Real-time PCR.The expression levels of Wnt/β-catenin pathway-related proteins in RSC96 cells were detected by Western blot.Results The main active components such as stigmasterol,β-si-tosterol and quercetin were screened,which mainly ac-ted on EGFR,AKT1,MAPK3 and Wnt,PI3K-Akt and MAPK signaling pathways.The molecular docking re-sults showed that stigmasterol,β-sitosterol,quercetin and other components could dock well with EGFR,AKT1,MAPK3 and other targets.The results of cell experiment showed that 10％TLTT drug-containing ser-um could promote the viability of high-glucose Schwann cells,up-regulate the expression of β-catenin protein,and down-regulate the expression of GSK-3β and Wif-1 protein.Conclusions TLTT has the characteristics of multi-target-multi-pathway in the treatment of DPN.The possible mechanism is that TLTT activates the Wnt/β-catenin signaling pathway,improves the inhibi-tory effect of high glucose on the proliferation of Schwann cells,promotes the proliferation of Schwann cells,and thus improves the status of DPN.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To investigate the effectiveness of the 5A (Ask, Assess, Advise, Assist, Arrange follow-up) management model based on personalized health education records in community patients with type 2 diabetes mellitus.Methods:A convenience sampling method was used to select patients with type 2 diabetes mellitus who were enrolled with family doctors in Yuxin Community, Haidian District, Beijing, from January 2022 to May 2023. Using a random number table method, the patients were divided into an observation group ( n=68) and a control group ( n=68). The control group received routine community management, while the observation group received management based on the 5A model and personalized health education records. Differences in metabolic indicators, including glycated hemoglobin (HbA1c), fasting blood glucose, 2-hour postprandial blood glucose, body mass index (BMI), and waist circumference, as well as self-management behaviors were compared between the two groups at 3, 6, and 12 months of intervention. Results:During the entire intervention period, four cases were lost in the observation group and eight cases were lost in the control group. Finally, data from 124 patients were included in the analysis, including 64 cases in the observation group and 60 cases in the control group. Repeated measures analysis of variance showed interaction effects between time points and groups for HbA1c, BMI, waist circumference, and self-management behavior scores among patients with diabetes, with statistically significant differences ( P<0.05) . Conclusions:The 5A management model based on personalized health education records has a positive impact on improving metabolic indicators and self-management behaviors in community patients with type 2 diabetes mellitus.

Objective:To explore the relationship between urinary cadmium levels and thyroid hormone levels in people aged 40-89 years old in selected areas of China.Methods:Based on the "Investigation of the Impact of Soil Quality of Agricultural Land on Human Health in Typical Areas" project from October 2019 to August 2020, a multi-stage stratified random sampling method was used to include 6 588 middle-aged and older adults aged 40-89. Demographic characteristics, dietary frequency and disease status were collected through the questionnaire and physical examination. Urinary cadmium and urinary creatinine were detected by random midstream urine. Fasting venous blood was collected for the detection of Triiodothyronine (T3) and Thyroxine (T4). The linear mixed effects model was used to explore the association of urine cadmium levels with thyroid hormone levels. Its dose-response relationship was explored by using the restricted cubic spline.Results:The age of the subjects was (63.48±12.18) years, with males accounting for 51.28%. The M ( Q 1,Q 3) of urinary cadmium level, T3 and T4 was 2.48 (1.36, 4.42) μg/g·creatinine, (1.96±0.51) nmol/L and (113.75±29.11) nmol/L, respectively. The linear mixed effects model showed that the changes of T3 and T4 were 0.027 (0.009, 0.044) nmol/L and 2.019 (1.084, 2.953) nmol/L for each one-unit increase (natural logarithm transformed) of urinary cadmium. The restricted cubic spline showed that there was a positive nonlinear association between urinary cadmium and T3 as well as T4 (all Pnonlinear<0.05). Conclusion:In selected areas of China, the urinary cadmium level of middle-aged and older adults aged 40-89 years is positively associated with T3 and T4.

Objective:To explore the relationship between urinary cadmium levels and thyroid hormone levels in people aged 40-89 years old in selected areas of China.Methods:Based on the "Investigation of the Impact of Soil Quality of Agricultural Land on Human Health in Typical Areas" project from October 2019 to August 2020, a multi-stage stratified random sampling method was used to include 6 588 middle-aged and older adults aged 40-89. Demographic characteristics, dietary frequency and disease status were collected through the questionnaire and physical examination. Urinary cadmium and urinary creatinine were detected by random midstream urine. Fasting venous blood was collected for the detection of Triiodothyronine (T3) and Thyroxine (T4). The linear mixed effects model was used to explore the association of urine cadmium levels with thyroid hormone levels. Its dose-response relationship was explored by using the restricted cubic spline.Results:The age of the subjects was (63.48±12.18) years, with males accounting for 51.28%. The M ( Q 1,Q 3) of urinary cadmium level, T3 and T4 was 2.48 (1.36, 4.42) μg/g·creatinine, (1.96±0.51) nmol/L and (113.75±29.11) nmol/L, respectively. The linear mixed effects model showed that the changes of T3 and T4 were 0.027 (0.009, 0.044) nmol/L and 2.019 (1.084, 2.953) nmol/L for each one-unit increase (natural logarithm transformed) of urinary cadmium. The restricted cubic spline showed that there was a positive nonlinear association between urinary cadmium and T3 as well as T4 (all Pnonlinear<0.05). Conclusion:In selected areas of China, the urinary cadmium level of middle-aged and older adults aged 40-89 years is positively associated with T3 and T4.

Objective:To evaluate the levels and source of cadmium exposure by dietary pathway among middle-aged and elderly people ≥40 in cadmium-contaminated areas of China.Methods:A total of 7 193 people aged 40-89 years from four typical cadmium-contaminated areas in China were selected as the study subjects. Food Frequency Questionnaire (FFQ), Total Diet Study (TDS) and a 3-day-24-hour dietary recall survey were conducted. Dietary cadmium intake and food sources through dietary pathways were assessed based on cadmium content in foods, consumption amounts and intake frequencies.Results:The mean age of the participants was 63.39±12.21 years, with 50.05% being males. The average monthly dietary cadmium intake was 7.39 μg/(kg·BW). Staple foods and vegetables were the primary sources of dietary cadmium intake, accounting for 57.51% and 32.48%, respectively. The monthly dietary cadmium intake in all surveyed regions did not exceed the Provisional Tolerable Monthly Intake (PTMI) recommended by the Joint FAO/WHO Expert Committee on Food Additives (JECFA).Conclusion:The monthly dietary cadmium intake among middle-aged and elderly people in cadmium-contaminated areas of China is relatively low, with the risk remaining at an acceptable level. Staple foods and vegetables are the most significant contributors to dietary cadmium intake.