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: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

This study investigates the effect of glycyrrhetinic acid(GA) combined with doxorubicin(DOX) on apoptosis in HepG2 cells and its possible mechanisms. HepG2 cells were cultured in vitro, and cell viability was assessed using the cell counting kit-8(CCK-8) method. Flow cytometry was used to measure apoptosis levels in HepG2 cells. The cells were divided into the following groups: control group(0 μmol·L~(-1)), DOX group(2 μmol·L~(-1)), GA group(150 μmol·L~(-1)), and DOX + GA combination group(2 μmol·L~(-1) DOX + 150 μmol·L~(-1) GA), with treatments given for 24 hours. The colocalization level between the endoplasmic reticulum(ER) and mitochondria was assessed by colocalization fluorescence imaging. Fluorescence probes were used to measure the Ca~(2+) content in the ER and mitochondria. The qRT-PCR and Western blot were used to determine the mRNA and protein expression of sirtuin-3(SIRT3). Co-immunoprecipitation(CO-IP) was applied to investigate the interactions between voltage-dependent anion channel 1(VDAC1) and SIRT3, as well as between VDAC1, glucose-regulated protein 75(GRP75), and inositol 1,4,5-trisphosphate receptor(IP3R). The results showed that the combination of DOX and GA promoted apoptosis in HepG2 liver cancer cells. The colocalization level between the ER and mitochondria was significantly reduced, the Ca~(2+) content in the ER was significantly increased, and the Ca~(2+) content in the mitochondria was significantly decreased. The relative expression of VDAC1, GRP75, and IP3R was significantly reduced, and interactions between VDAC1, GRP75, and IP3R were observed. SIRT3 mRNA and protein expression levels were significantly increased, and an interaction between SIRT3 and VDAC1 was detected. The acetylation level of VDAC1 was significantly decreased. In conclusion, GA combined with DOX induces apoptosis in HepG2 cells by mediating the deacetylation of VDAC1 through SIRT3, weakening the interactions among VDAC1, GRP75, and IP3R. This regulates the formation of endoplasmic reticulum-mitochondrial membrane domains(ERMMDs), affects Ca~(2+) transport between the ER and mitochondria, and ultimately triggers cell apoptosis.
Humans ; Apoptosis/drug effects* ; Hep G2 Cells ; Glycyrrhetinic Acid/pharmacology* ; Doxorubicin/pharmacology* ; Liver Neoplasms/genetics* ; Carcinoma, Hepatocellular/physiopathology* ; Mitochondria/metabolism* ; Endoplasmic Reticulum/metabolism* ; Cell Survival/drug effects* ; Membrane Proteins/genetics*

OBJECTIVE: To evaluate the partial biomechanical properties of the posterior occipital muscles (rectus capitis posterior major, rectus capitis posterior minor, and obliquus capitis inferior) in patients with cervical vertigo. METHODS: A total of 30 patients with cervical vertigo admitted from April 2024 to September 2024 were included in the vertigo group, and 30 age-and gender-matched healthy subjects were recruited as the normal group. In the vertigo group, there were 21 females and 9 males, with an average age of (24.00±2.25) years;in the normal group, there were 22 females and 8 males, with an average age of (23.00±3.00) years. Shear wave elastography was used to measure the thickness and stiffness of the posterior occipital muscles in both groups. RESULTS: In the vertigo group, there were no statistically significant differences in the Young's modulus values (E) of stiffness of the posterior occipital muscles (rectus capitis posterior major, rectus capitis posterior minor, obliquus capitis inferior) between the left and right sides(P>0.05). The Young's modulus values(E) of stiffness of the right posterior occipital muscles (rectus capitis posterior major, rectus capitis posterior minor, obliquus capitis inferior) in the cervical vertigo group were (39.66±8.21) kPa, (45.61±5.85) kPa, and (43.73±5.22) kPa, respectively, which were significantly higher than those in the normal group 33.97(17.76) kPa, 41.38(8.99) kPa, 38.27(12.58) kPa, with statistically significant differences (P<0.05). In the vertigo group, the Young's modulus values(E) of stiffness of the left rectus capitis posterior major and left obliquus capitis inferior were (40.41±9.13) kPa and (42.11±6.20) kPa, respectively, which were significantly greater than those in the normal group (33.30±11.31) kPa, 38.94(14.62) kPa, with statistically significant differences(P<0.05);however, there was no statistically significant difference in the left rectus capitis posterior minor between the two groups(P>0.05). In the vertigo group, there were no statistically significant differences in the stiffness of the posterior occipital muscles (rectus capitis posterior major, rectus capitis posterior minor, obliquus capitis inferior) between the left and right sides(P>0.05). Additionally, there were no statistically significant differences in the thickness of the bilateral posterior occipital muscles between the vertigo group and the normal group (P>0.05). CONCLUSION The posterior occipital muscles of patients with cervical vertigo are stiffer than those of healthy individuals, while there is no significant difference in muscle thickness between the two groups.
Humans ; Female ; Male ; Elasticity Imaging Techniques/methods* ; Adult ; Vertigo/physiopathology* ; Neck Muscles/physiopathology* ; Young Adult

OBJECTIVE: To investigate the metabolic characteristics of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) in myeloid leukemia by in vitro culture of myeloid leukemia cells and construction of tumor-bearing nude mouse model. METHODS: U937, THP-1, HL60 and K562 cells were cultured in vitro. The cells in logarithmic growth phase (l×10 ⁵ cells/well) were added with ¹⁸F-FDG, and the uptake rate of ¹⁸F-FDG was measured at 15, 30, 60 and 120 min after addation, respectively. The four kinds of cells were inoculated subcutaneously into the hind limbs of nude mice to establish a tumor-bearing nude mouse model. When the tumor size was about 500 mm³, ¹⁸F-FDG was injected through the tail vein of the mice, and positron emission tomography/computed tomography was performed at 60 min after injection. The morphology of tumor-bearing cells was observed by hematoxylin-eosin (HE) staining in serial pathological sections. RESULTS: After co-incubation with ¹⁸F-FDG, the ¹⁸F-FDG uptake rates of U937 cells were significantly higher than THP-1, HL60 and K562 cells at 4 time points (all P <0.05), and THP-1 cells were higher than K562 cells (all P <0.05). The uptake rate of ¹⁸F-FDG by leukemia cells was rapid in the first 60 min, then tended to be stable. Pathological analysis showed that subcutaneous inoculation of U937, THP-1, HL60 and K562 cells could successfully establish tumor-bearing nude mouse models of myeloid leukemia. The ¹⁸F-FDG uptake value in U937 tumor-bearing nude mice was significantly higher than THP-1, HL60 and K562 tumor-bearing nude mice (all P <0.01). The ¹⁸F-FDG uptake values in THP-1 and HL60 tumor-bearing nude mice were significantly higher than that in K562 tumor-bearing nude mice (both P <0.01). CONCLUSION The tumor-bearing nude mouse model of myeloid leukemia can be successfully constructed by subcutaneous inoculation. The ¹⁸F-FDG uptake rate of acute myeloid leukemia (AML) cells is higher in cells cultured in vitro and tumor-bearing nude mouse model. ¹⁸F-FDG may have better clinical application value for AML.
Animals ; Fluorodeoxyglucose F18/metabolism* ; Mice, Nude ; Mice ; Humans ; Leukemia, Myeloid/diagnostic imaging* ; HL-60 Cells ; K562 Cells ; Cell Line, Tumor ; U937 Cells