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.

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 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.

Paclitaxel(PTX)is a first-line chemotherapy drug for breast cancer,but its resistance issues significantly impact clinical treatment efficacy.Fucosylation,especially core fucosylation,is closely related to tumor chemoresistance,resulting in poor chemotherapy responses and poor prognosis in patients.In this study,we investigated the effect and mechanism of the fucosylation inhibitor 2-fluorofucose(2-F-Fuc)on the chemosensitivity of paclitaxel-resistant breast cancer MCF-7/PTX cells.The drug resistanceindex(RI)of MCF-7/PTX cells was 8.49 by MTT assays.Western blotting,real-time PCR,enzyme-linked immu-nosorbent assay(ELISA)and Lens Culinaris Agglutinin(LCA)lectin imprinting showed that compared with MCF-7 cells,the expression of FUT8,MDR1and core fucosylation in MCF-7/PTX cells was high.Western blotting showed that 2-F-Fuc had a significant inhibitory effect on the growth of MCF-7/PTX cells,and the expression levels of FUT8 and MDR1 were significantly down-regulated after 2-F-Fuc treatment,and the down-regulation was more pronounced in the PTX and 2-F-Fuc combination group(P＜0.05).Compared to the control,expression of PCNA in MCF-7/PTX cells in the PTX and the 2-F-Fuc group were down-regulated,and the apoptosis-related proteins,such as cleaved caspase-3 and Bax/Bcl-2 were in-creased.The level of p-PI3K and p-AKT were down-regulated,and the changes in the combination of 2-F-Fuc and PTX were more robust(P＜0.05).The above results showed that the core fucosylation level of MCF-7/PTX cells was significantly increased,and 2-F-Fuc could reduce the core fucosylation level of MCF-7/PTX cells by inhibiting the expression of FUT8,and enhance the sensitivity of drug-resistant cells to PTX,which may correlate with the downregulation of PI3K/AKT signaling pathway proteins.

Objective:This study aimed to investigate the atherosclerotic cardiovascular disease(ASCVD)risk stratification and target achievement of lipid and blood pressure control among community-based hypertensive patients,with the goal of optimizing integrated management strategies.Methods:A total of 2832 hypertensive patients registered in 2021 at the Bicheng Community Health Service Center in Bishan District of Chongqing,were included.Baseline data were collected through retrospective analysis of health records.Non-high-density lipoprotein cholesterol(non-HDL-C)levels and estimated glomerular filtration rate(eGFR)were calculated.ASCVD risk stratification was performed,and target achievement for lipid and blood pressure control were analyzed,including comparisons among patients with different comorbidities.Results:Based on ASCVD risk stratification,patients were categorized as follows:ultra-high risk(22 cases,0.78％),very high risk(111 cases,3.92％),high risk(1324 cases,46.75％),moderate risk(997 cases,35.20％),and low risk(378 cases,13.35％).The LDL-C target achievement rate was 4.55％(1/22)in the ultra-high risk group and 15.32％(17/111)in the very high risk group,with blood pressure target achievement rate of 18.18％(4/22)and 11.71％(13/111),respectively.In the high-risk group,LDL-C and blood pressure target achievement rate were only 4.76％(63/1324)and 8.08％(107/1324),while moderate-risk groups showed 25.68％(256/997)and 26.18％(261/997),respectively.The low-risk group achieved 99.74％(377/378)LDL-C target achievement and 30.69％(116/378)blood pressure target achievement.Patients with ischemic stroke had a significantly higher lipid target achievement rate(13.73％,7/51)compared to non-ischemic stroke patients(6.40％,178/2781)(P＜0.05).Similarly,those with coronary heart disease(12.65％,13/87)exhibited higher lipid target achievement than non-coronary heart disease patients(6.27％,172/2745)(P＜0.05).However,no significant difference was observed between hypertensive patients with diabetes(8.04％,52/647)and non-diabetic patients(6.09％,133/2185)(P＞0.05),or between those with chronic kidney disease(CKD)stages 3/4(6.72％,16/238)and non-CKD 3/4 patients(6.52％,169/2594)(P＞0.05).Conclusion:Over half of the community-based hypertensive patients were classified as high-risk or above in ASCVD stratification,yet their lipid and blood pressure target achievement rates were markedly suboptimal.Hypertension patients with comorbidities,particularly diabetes or CKD stages 3/4,showed poor lipid target achievement.These findings underscore the necessity of incorporating ASCVD risk stratification into community management assessments for hypertensive patients,enhancing personalized management for high-risk populations,and prioritizing lipid target achievement in those with diabetes or CKD stages 3/4.

Objective To explore the application value of physical examination information annotation combined with artificial intelli-gence (AI) in CT diagnosis of rib fractures. Methods The clinical data of 100 patients with chest trauma who underwent rib CT examina-tion with physical examination information annotation were collected. The images were analyzed by two physicians in the department of radiology with different seniorities using four methods[diagnosed by physicians independently (group A),diagnosed by physicians combined with physical examination information annotation (group B),diagnosed by physicians under the assistance of AI (group C),and diagnosed by physicians combined with physical examination information annotation under the assistance of AI (group D)]. The diagnostic efficacy and diagnostic time of two radiologists using different methods for rib fractures were compared. Results The sensitivities of two radiologists with different seniorities in the diagnosis of rib fracture in the group A were lower than those in the groups B,C and D (P<0.05),but there was no significant difference in the sensitivity of rib fracture among groups B,C and D (P>0.05). The diagnostic sensitivity of resident physician in the group A was lower than that of the attending physicians (P<0.05),and there was no significant difference in the diagnostic sensitivity of rib fracture in the other groups between the two physicians (P>0.05). There was no statistically significant difference in the false-positive rate of rib fractures among groups between two physicians (P>0.05). There was statistically significant difference in the diagnostic time among groups between two physicians (P<0.05),among which group A took the longest diagnosis time and group C took the shortest. Conclusion The assistance of AI and conbinatin of physical examination information annotation can increase the sensitivity of the physician in the diagnosis of rib fractures,shorten the diagnostic time and improve the work efficiency.

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.

This study investigated the role of the nuclear factor of activated T cells c3 (NFATc3) in vascular smooth muscle cells (VSMCs) during aortic aneurysm and dissection (AAD) progression and the underlying molecular mechanisms. Cytoplasmic and nuclear NFATc3 levels were elevated in human and mouse AAD. VSMC-NFATc3 deletion reduced thoracic AAD (TAAD) and abdominal aortic aneurysm (AAA) progression in mice, contrary to VSMC-NFATc3 overexpression. VSMC-NFATc3 deletion reduced extracellular matrix (ECM) degradation and maintained the VSMC contractile phenotype. Nuclear NFATc3 targeted and transcriptionally upregulated matrix metalloproteinase 9 (MMP9) and MMP2, promoting ECM degradation and AAD development. NFATc3 promoted VSMC phenotypic switching by binding to eukaryotic elongation factor 2 (eEF2) and inhibiting its phosphorylation in the VSMC cytoplasm. Restoring eEF2 reversed the beneficial effects in VSMC-specific NFATc3-knockout mice. Cabamiquine-targets eEF2 and inhibits protein synthesis-inhibited AAD development and progression in VSMC-NFATc3-overexpressing mice. VSMC-NFATc3 promoted VSMC switch and ECM degradation while exacerbating AAD development, making it a novel potential therapeutic target for preventing and treating AAD.