ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

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.

Objective To understand the HIV/AIDS burden and the disease burden attributed to various risk factors in four countries with different socio-demographic index (SDI) (China, United States, Russia, and Afghanistan) from 1990 to 2019, and to predict the HIV/AIDS attributable disease burden from 2020 to 2029. Methods The 2019 Global Burden of Disease Study data was used to describe and compare the incidence, prevalence, death, and DALYs of HIV/AIDS in the four countries. The standardized DALYs attributed to various risk factors in different age groups of HIV/AIDS in the four countries in 1990 and 2019 were compared. R4.3.0 was used to construct an autoregressive moving average mixed model to predict the attributable disease burden in each country over the next decade. Results Compared with 1990, in 2019, the standardized incidence rate, standardized prevalence rate, standardized mortality rate, and standardized DALYs rate in China and the other two countries, except the United States, showed an increase. People aged 10 to 49 years old were a key group for disease burden, and the main risk factors for disease burden varied among different countries and age groups. The autoregressive moving average mixed model predicted that the main risk factor for Russia in the next decade would be injecting drugs, while unsafe sexual behavior would occur in the other three countries. Conclusion There are differences in disease burden and risk factors among different genders and age groups globally and in the four different SDI countries. Therefore, differences should be fully considered to determine the focus of HIV/AIDS prevention and control and rationally allocate health resources.

An improved U-shaped multi-lesion segmentation model,namely dense dilated attention pyramid UNet(DDAPNet),is proposed to overcome the difficulty in learning multi-scale features and address the issue of blurry boundaries in diabetic retinopathy(DR)segmentation task.DR images are treated with Patch processing to enhance the model's ability to capture local lesion features.After backbone feature extraction,a redesigned dense dilated attention pyramid module is introduced to expand the receptive field and address the issue of blurry lesion boundaries;and simultaneously,pyramid split attention module is used for feature enhancement;and then,the features output by the two modules are fused.Additionally,an improved residual attention module is embedded within skip connections to reduce interference from shallow redundant information.The joint validation on DDR dataset and real dataset from a specific hospital shows that compared with the original model,DDAPNet model improves the Dice similarity coefficient for segmentations of microaneurysms,hemorrhages,soft exudates and hard exudates by 4.31%,2.52%,3.39%and 4.29%,respectively,and increases mean intersection over union by 1.80%,2.24%,4.28%and 1.98%,respectively.The proposed model makes the segmentation of lesion edges smoother and more continuous,notably enhancing the segmentation performance for conditions like soft exudates in retinal lesions.

Objective:To analyze the status quo and influencing factors of post-traumatic growth of parents of children and adolescents with type 1 diabetes mellitus (T1DM) .Methods:The convenient sampling method was used to select parents of children with T1DM who were admitted to the diabetes care consultation clinic of Beijing Children's Hospital, Capital Medical University from March 2022 to March 2023 as the research objects. The parents of children were investigated by general information questionnaire, Posttramnatic Growth Inventory, Simplified Coping Style Questionnaire and Perceived Social Support Scale.Results:A total of 200 questionnaires were sent out in this study, and 187 were effectively collected, with an effective recovery rate of 93.5% (187/200). The total score of Posttramnatic Growth Inventory for parents of T1DM children was (60.28±18.37). The positive coping score in Simplified Coping Style Questionnaire was (22.40±6.94) and the negative coping score was (8.94±4.58). The total score of Perceived Social Support Scale was (57.39±14.57). The results of multiple linear regression analysis showed that HbA1c, monthly family income, educational level, positive coping strategies and perceived social support were the influencing factors for the post-traumatic growth of parents of T1DM children, explaining 33.8% of the variation in post-traumatic growth of parents of T1DM children.Conclusions:The post-traumatic growth of parents of T1DM children is at a moderate level. Medical staff should pay more attention to parents of children with poor blood sugar control, low educational level and low monthly family income, guide them from the perspective of positive psychology, encourage parents to adopt positive and effective coping strategies and emphasize the importance of social support, so as to improve the level of post-traumatic growth of parents of children with T1DM.

Objective This article aims to investigate the association between hypertension and the risk of GSD by conducting a national multicenter study,a systematic review,and a meta-analysis.Methods The study was conducted in three stages.In the first stage,subjects were recruited for health examination in four hospitals in Chengdu,Tianjin,Beijing,and Chongqing,China,from 2015 to 2020,and the multivariate logistic regression analysis was used to investigate the association between hypertension and the risk of GSD in each center.In the second stage,Embase,PubMed,Wanfang Data,VIP,and CNKI databases were searched for related studies published up to May 2021,and a meta-analysis was conducted to further verify such association.In the third stage,the random effects model was used for pooled analysis of the results of the multicenter cross-sectional study and the findings of previous literature.Results A total of 633 948 participants were enrolled in the cross-sectional study,and the prevalence rate of GSD was 7.844%.The multivariate logistic regression analysis showed that hypertension was positively associated with the risk of GSD(P＜0.05).Subgroup analysis showed that there was no significant difference in the association between hypertension and GSD between individuals with different sexes,ages,and subtypes of GSD.A total of 80 articles were included in the systematic review and the meta-analysis,and the results showed that the risk of GSD was increased by 1.022 times for every 10 mmHg increase in diastolic pressure and 1.014 times for every 10 mmHg increase in systolic pressure.Conclusion Hypertension significantly increases the risk of GSD,and the findings of this study will provide a basis for the etiology of GSD and the identification of high-risk groups.

Purpose: Stroke survivors and their informal family caregivers may share the impact of the disease, which may affect family functioning and quality of life (QoL) for both. This study compared the perceptions of stroke survivors and informal family caregivers regarding family functioning and QoL and examined the QoL of those reporting effective versus ineffective family functioning. Methods: A cross-sectional study design and convenience sampling were used. Stroke survivoreinformal family caregiver dyads were recruited from a medical university hospital. We assessed participants’ demographic and clinical variables, including disease severity, family functioning, and QoL. Independent t-test, paired t-test, Wilcoxon signed-rank test, and ManneWhitney U test were used to analyze the data. Results: Seventy-one stroke survivoreinformal family caregiver dyads participated in the current study. Most stroke survivors and informal family caregivers reported effective family functioning, with no significant differences. However, significant differences existed in the seven domains (physical functioning, role-physical, bodily pain, general health, vitality, social functioning, and role-emotional) of QoL, except emotional health. Stroke survivors reporting ineffective family functioning had a significantly lower mental component summary score, unlike informal family caregivers. Conclusions Our findings suggest that family functioning is crucial to ensure stroke survivors’ QoL, particularly regarding their mental health. Health professionals should prioritize mental health assessments and provide appropriate care interventions for stroke survivors in the first 1e6 months after stroke onset.

Objective: We aimed to investigate whether 2-[ 18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[ 18F]FDG PET/CT) can aid in evaluating the risk of malignancy in ampullary tumors detected by endoscopy. Materials and Methods: This single-center retrospective cohort study analyzed 155 patients (79 male, 76 female; mean age, 65.7 ± 12.7 years) receiving 2-[ 18F]FDG PET/CT for endoscopy-detected ampullary tumors 5–87 days (median, 7 days) after the diagnostic endoscopy between June 2007 and December 2020. The final diagnosis was made based on histopathological findings. The PET imaging parameters were compared with clinical data and endoscopic features. A model to predict the risk of malignancy, based on PET, endoscopy, and clinical findings, was generated and validated using multivariable logistic regression analysis and an additional bootstrapping method. The final model was compared with standard endoscopy for the diagnosis of ampullary cancer using the DeLong test. Results: The mean tumor size was 17.1 ± 7.7 mm. Sixty-four (41.3%) tumors were benign, and 91 (58.7%) were malignant. Univariable analysis found that ampullary neoplasms with a blood-pool corrected peak standardized uptake value in earlyphase scan (SUVe) ≥ 1.7 were more likely to be malignant (odds ratio [OR], 16.06; 95% confidence interval [CI], 7.13–36.18;P < 0.001). Multivariable analysis identified the presence of jaundice (adjusted OR [aOR], 4.89; 95% CI, 1.80–13.33; P = 0.002), malignant traits in endoscopy (aOR, 6.80; 95% CI, 2.41–19.20; P < 0.001), SUVe ≥ 1.7 in PET (aOR, 5.43; 95% CI, 2.00–14.72; P < 0.001), and PET-detected nodal disease (aOR, 5.03; 95% CI, 1.16–21.86; P = 0.041) as independent predictors of malignancy. The model combining these four factors predicted ampullary cancers better than endoscopic diagnosis alone (area under the curve [AUC] and 95% CI: 0.925 [0.874–0.956] vs. 0.815 [0.732–0.873], P < 0.001). The model demonstrated an AUC of 0.921 (95% CI, 0.816–0.967) in candidates for endoscopic papillectomy. Conclusion Adding 2-[ 18F]FDG PET/CT to endoscopy can improve the diagnosis of ampullary cancer and may help refine therapeutic decision-making, particularly when contemplating endoscopic papillectomy.

Objective To explore the correlation between left atrial diameter (LAD) and renal function in patients with atrial fibrillation. Methods A total of 364 patients with atrial fibrillation were selected as study subjects. Clinical data of the patients were collected, including gender, age, height, body weight, smoking history, drinking history, atrial fibrillation type, history of hypertension, coronary heart disease, diabetes, heart failure, medication history, high-sensitivity troponin I, brain natriuretic peptide, C-reactive protein, creatinine, urea nitrogen, LAD, left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular posterior wall thickness (LVPWTd), and interventricular septal thickness (IVSTd). Estimated glomerular filtration rate (eGFR) and body mass index were calculated. Spearman correlation analysis was used to explore the correlations of LAD with creatinine, urea nitrogen, and eGFR. With eGFR < 60 mL/(min·1.73 m²) setting as renal insufficiency, univariate Logistic regression analysis was used to explore the influencing factors of renal insufficiency in patients with atrial fibrillation. Binary Logistic regression analysis was used to explore the relationship between LAD and renal insufficiency in patients with atrial fibrillation. Results Spearman correlation analysis showed that LAD was positively correlated with creatinine (r=0.279, P < 0.001) and urea nitrogen (r=0.190, P < 0.001) in patients with atrial fibrillation, and negatively correlated with eGFR (r=-0.263, P < 0.001). Univariate Logistic regression analysis showed that gender, history of diabetes, hypertension, coronary heart disease, heart failure, atrial fibrillation type, sodium-glucose cotransporter 2 inhibitor medication history, diuretic medication history, LAD, LVEF, LVDd, and IVSTd were all influencing factors of renal insufficiency in patients with atrial fibrillation (P < 0.05). Binary Logistic regression analysis showed that compared with the first quartile of LAD (LAD ≤ 38 mm), the risk of renal insufficiency in patients with atrial fibrillation in the fourth quartile of LAD (LAD>47 mm) increased by 5.199 times(OR=5.199; 95 %CI, 1.210 to 22.337; P=0.027). Conclusion LAD of patients with atrial fibrillation is significantly related to renal function, and LAD is an influencing factor of renal insufficiency in patients with atrial fibrillation.