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 investigate the expression of CSF-1 and CSF-1R in the peripheral blood of children with immune thrombocytopenia (ITP) and its clinical significance. METHODS: Forty-four children with ITP treated in our hospital from February 2023 to January 2024 were selected as the observation group, and 40 healthy children were selected as the control group during the same period, and relevant clinical data were collected. Peripheral blood mononuclear cells (PBMC) of children with ITP and healthy children were separated, and the plasma levels of M1 macrophage-associated cytokines (TNF-α, IL-6), M2 macrophage-associated cytokines (IL-10, TGF-β), and CSF-1 were detected by ELISA in the children of both groups. The mRNA levels of M1 macrophage surface markers (CD86, iNOS), M2 macrophage surface markers (CD206, Arg-1) and CSF-1R were detected by RT-PCR in PBMC of children in both groups. Western blot was used to detect the expression of CSF-1R protein in PBMC of the two groups of children. The correlation between platelet count and CSF-1R mRNA expression in PBMC, TNF-α, IL-6, IL-10, TGF-β and CSF-1 in plasma was analyzed. RESULTS: Compared with the control group, the levels of IL-10, TGF-β, CSF-1 and platelet count in plasma of children with ITP were significantly decreased (P < 0.01), and the levels of TNF-α and IL-6 were significantly increased (P < 0.01); the mRNA levels of the M1 macrophage surface markers (CD86, iNOS) in PBMC of children with ITP were significantly increased (P < 0.05), mRNA levels of M2 macrophage surface marker CD206 in PBMC of children with ITP were decreased compared with controls but the difference was not statistically significant ( P >0.05), mRNA levels of Arg-1 were decreased, the difference was statistically significant (P < 0.05). The mRNA and protein levels of CSF-1R in PBMC of ITP children were higher than that in controls. CSF-1R expression in PBMC of ITP was positively correlated with platelet count, IL-10, CSF-1 were positively correlated (r =0.822,0.481,0.405). CONCLUSION CSF-1 is significantly reduced in the plasma of ITP, and CSF-1R mRNA and protein expression is significantly elevated in PBMC of ITP, which are involved in the regulation of macrophage M1/M2 imbalance, and could serve as a potential therapeutic target for ITP.
Humans ; Purpura, Thrombocytopenic, Idiopathic/blood* ; Macrophage Colony-Stimulating Factor/metabolism* ; Leukocytes, Mononuclear/metabolism* ; Child ; Interleukin-10/blood* ; Macrophages/metabolism* ; Tumor Necrosis Factor-alpha/blood* ; Interleukin-6/blood* ; Male ; Female ; Transforming Growth Factor beta/blood* ; Receptor, Macrophage Colony-Stimulating Factor/metabolism* ; Clinical Relevance

OBJECTIVE: To investigate the effect of zedoarondiol on neovascularization of atherosclerotic (AS) plaque by exosomes experiment. METHODS: ApoE^-/- mice were fed with high-fat diet to establish AS model and treated with high- and low-dose (10, 5 mg/kg daily) of zedoarondiol, respectively. After 14 weeks, the expressions of anti-angiogenic protein thrombospondin 1 (THBS-1) and its receptor CD36 in plaques, as well as platelet activation rate and exosome-derived miR-let-7a were detected. Then, zedoarondiol was used to intervene in platelets in vitro, and miR-let-7a was detected in platelet-derived exosomes (Pexo). Finally, human umbilical vein endothelial cells (HUVECs) were transfected with miR-let-7a mimics and treated with Pexo to observe the effect of miR-let-7a in Pexo on tube formation. RESULTS: Animal experiments showed that after treating with zedoarondiol, the neovascularization density in plaques of AS mice was significantly reduced, THBS-1 and CD36 increased, the platelet activation rate was markedly reduced, and the miR-let-7a level in Pexo was reduced (P<0.01). In vitro experiments, the platelet activation rate and miR-let-7a levels in Pexo were significantly reduced after zedoarondiol's intervention. Cell experiments showed that after Pexo's intervention, the tube length increased, and the transfection of miR-let-7a minics further increased the tube length of cells, while reducing the expressions of THBS-1 and CD36. CONCLUSION Zedoarondiol has the effect of inhibiting neovascularization within plaque in AS mice, and its mechanism may be potentially related to inhibiting platelet activation and reducing the Pexo-derived miRNA-let-7a level.
Animals ; MicroRNAs/genetics* ; Exosomes/drug effects* ; Plaque, Atherosclerotic/genetics* ; Neovascularization, Pathologic/genetics* ; Human Umbilical Vein Endothelial Cells/metabolism* ; Humans ; Blood Platelets/drug effects* ; Apolipoproteins E/deficiency* ; Thrombospondin 1/metabolism* ; CD36 Antigens/metabolism* ; Platelet Activation/drug effects* ; Male ; Mice ; Mice, Inbred C57BL

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Human respiratory syncytial virus (HRSV) can infect individuals of all ages, with children under five years old, the elderly, and immunocompromised persons as the main high-risk groups. Although older children and adults often exhibit mild or no symptoms, they may still be potential carriers of the virus. Therefore, employing efficient, accurate, and rapid detection methods to timely identify infection sources and quickly halt transmission is an important means to curb the potential spread of the epidemic. However, the clinical manifestations of HRSV infection are difficult to distinguish from acute respiratory infections caused by other respiratory viruses, and the identification relies on the results of pathogen testing. This article summarizes four commonly used detection methods for HRSV based on detection principles: antigen detection, nucleic acid testing, antibody detection, and virus isolation. The advantages, disadvantages, principles, and applicable scenarios of these four methods are summarized and compared. Furthermore, the research progress and prospects of HRSV detection methods are reviewed.

Human respiratory syncytial virus (HRSV) can infect individuals of all ages, with children under five years old, the elderly, and immunocompromised persons as the main high-risk groups. Although older children and adults often exhibit mild or no symptoms, they may still be potential carriers of the virus. Therefore, employing efficient, accurate, and rapid detection methods to timely identify infection sources and quickly halt transmission is an important means to curb the potential spread of the epidemic. However, the clinical manifestations of HRSV infection are difficult to distinguish from acute respiratory infections caused by other respiratory viruses, and the identification relies on the results of pathogen testing. This article summarizes four commonly used detection methods for HRSV based on detection principles: antigen detection, nucleic acid testing, antibody detection, and virus isolation. The advantages, disadvantages, principles, and applicable scenarios of these four methods are summarized and compared. Furthermore, the research progress and prospects of HRSV detection methods are reviewed.

Aim To investigate the effect of Celastrol on the expression of Ezrin in tissues and HaCaT cells of DNCB sensitisation-induced atopic dermatitis(AD)mice.Methods BALB/c mice were taken and ran-domly divided into the control,DNCB group,Celastrol 25 μg,50 μg,75 μg treatment group,and Dex group,with 8 mice in each group;HaCaT cells were induced with TNF-α and treated with 1 μmol·L-1 Celastrol and Ezrin siRNA.The thickness of the skin on the ear and back of mice was measured by a thickness gauge,and the spleen and lymph nodes of mice were taken to observe the changes.HE and toluidine blue staining were used to observe the inflammatory cells and mast cell infiltration in mice.Flow cytometry was used to detect the levels of IL-4 and TNF-α in the lymph nodes of mice,and enzyme-linked immunosorbent was used to determine the levels of IL-4,TNF-α and IgE in serum of mice,and the expression of IL-4,IL-5 and IL-13 in the supernatant of HaCaT cells.Western blot was used to detect the expression of P-Ezrin and Ezrin in skin tissues.Results Celastrol significantly inhibited the swelling of ear and back skin tissues,reduced the de-granulation of inflammatory cells and mast cells,low-ered serum IgE and serum and lymph node levels of IL-4 and TNF-α,and reduced the activation of Ezrin in mice,and the expression of IL-4,IL-5 and IL-13 in the supernatant of HaCaT cells was restored by the treat-ment with Ezrin siRNA.Conclusion Celastrol amel-iorates AD,which may be achieved by modulating Ezrin activation.

Objective:To establish and validate a gallstones classification method based on deep learning.Methods:A total of 618 gallstones samples were collected from East Hospital Affiliated to Tongji University, and 1 023 high-definition cross-sectional gallstones profile images were captured to construct a cross-sectional gallstones profile image dataset. Based on the traditional eight-category gallstones classification method, a lightweight network model, MobileNet V3, was trained using deep learning and transfer learning methods. The classification performance of MobileNet was evaluated using a confusion matrix with metrics such as accuracy rate, precision rate, F1 score, and recall rate. The MobileNet V3 was improved and further validated using accuracy and loss values.Results:The accuracy rate (94.17%), precision rate (94.03%), F1 score (92.96%) and recall rate (92.99%) of the improved MobileNet V3 model were better than other networks. The improved MobileNet V3 model achieved the highest accuracy rate (94.17%) in gallstones profile classification and was validated by the test set. The confusion matrix showed a weighted average of accuracy rate (92.0%), precision rate (92.6%), and F1 score (92.2%) for each category of gallstones.Conclusions:Based on deep learning, a high-accuracy gallstones classification method is proposed, which provides a new idea for the intelligent identification of gallstones.