Objective To analyze the clinical significance of subjective visual vertical (SVV) tests at different head deflection angles in assessing utricle function in patients with benign paroxysmal positional vertigo (BPPV). Methods A total of 61 BPPV patients who were treated at the Hearing Impairment and Vertigo Diagnosis and Treatment Center of Otolaryngology Head and Neck Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine from August 2022 to May 2023 were retrospectively included, and 29 healthy adults were selected as controls. SVV tests were performed on all research subjects at different head deflection angles: upright head (0°), left head 45° (L45°), right head 45° (R45°). The test results between the two groups were compared. Results SVV absolute value at R45° in BPPV group was lower than that in the control group (P＝0.003); there was no significant difference in SVV values at 0° and L45° between the two groups. There was no statistical difference in SVV values at different head deflection angles between the control group and the left BPPV group. SVV absolute value at R45° in right BPPV group was lower than that in the control group (P＜0.001); there was no statistical difference in SVV values at 0° and L45° between the two groups. Conclusions SVV test can provide subjective information about the utricle, and SVV tests at different head deflection angles can fine-tune evaluate the function of the utricle in BPPV patients.

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.

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.

To investigate the effect of baicalin (BAI)-loaded cross-linked lipoic acid nanocapsules (BAI@cLANCs) against hydrogen peroxide (H2O2)-induced senescence in human umbilical vein endothelial cells (HUVECs), this study examined the toxicity of BAI@cLANCs on HUVECs by MTT method. The cell nuclear staining, SA-β-gal staining, and MTT methods were used to assess the optimal concentration of H2O2-induced senescence in HUVECs. The cellular uptake of BAI@cLANCs was evaluated using fluorescence microscopy imaging and flow cytometry. The proportion of cellular senescence was determined by SA-β-gal staining. The level of reactive oxygen species (ROS) in senescent cells was detected by fluorescence microscopy imaging and multifunctional microplate reader. The content of malondialdehyde (MDA) in cells was detected by lipid oxidation detection kit, and the cell cycle was analyzed by flow cytometry with propidium iodide staining. The results showed that BAI@cLANCs had no significant effect on the growth of HUVECs in the range of BAI at 2.80−112 mmol/L. 200 μmol/L and 25 minutes were the ideal conditions for H2O2-induced senescence of HUVECs. cLANCs as drug delivery carriers significantly enhanced the uptake efficiency of BAI in HUVECs. Compared with the normal group, the H2O2 model group showed decreased cell viability, increased positive SA-β-gal staining rate, increased ROS and MDA content, as well as increased percentage of cells blocked in S phase and decreased cells entering G2/M phase. Compared with the H2O2 model group, BAI, cLANCs, BAI + cLANCs, and BAI@cLANCs groups showed increased cell viability, decreased positive SA-β-gal staining rate, decreased ROS and MDA content, decreased percentage of S-phase cells, and increased cells entering G2/M phase, with the best anti-aging effect in the BAI@cLANCs group. In summary, the results above showed that both BAI and cLANCs have anti-aging properties. With cLANCs as drug carriers, the anti-aging benefits of BAI@cLANCs are synergistic and can effectively delay H2O2-induced senescence of HUVECs.

Objective: To investigate the association between medium to long term PM 2.5 exposure around school areas and overweight/obesity among primary and secondary school students in Guangxi, providing data support and theoretical foundations for scientifically addressing overweight and obesity in primary and secondary school students. Methods: From September to November 2023, a stratified cluster random sampling method was employed to select 251 183 students aged 7-18 years (grade 1 to grade 12) from 14 prefecture level cities (111 districts and counties) in Guangxi. PM 2.5 mass concentration data were obtained from the Tracking Air Pollution in China (TAP) dataset. Preliminary comparative analysis was conducted using the Mann-Whitney U test, while binary Logistic regression models were applied to quantify the relationship between PM 2.5 exposure and overweight/obesity. Restricted cubic spline analysis was further utilized to examine the nonlinear association between PM 2.5 concentration and overweight/obesity risk. Results: The detection rate of overweight/obesity among Guangxi students in 2023 was 19.5%. The median PM 2.5 concentration in the year prior to the study was higher in the overweight/obesity group (23.22 μg/m 3) compared to the non overweight/obesity group (22.63 μg/m 3) ( Z=-15.66, P <0.01), and consistent trends were observed across gender (male/female) and educational stage (primary/junior/senior high school) subgroups (all P <0.01). Binary Logistic regression revealed that for every 10 μg/m 3 increase in the annual average PM 2.5 concentration, the risk of overweight/obesity increased by 12% ( OR=1.12, 95%CI=1.09- 1.15 , P <0.01). Restricted cubic spline analysis indicated a nonlinear relationship between monthly PM 2.5 levels and overweight/obesity risk ( P trend <0.01). Below 22.68 μg/m 3, PM 2.5 exposure showed no significant association with obesity risk; above the threshold, the risk increased with rising PM 2.5 levels. Conclusion Medium to long term PM 2.5 exposure around school environments is significantly associated with overweight/obesity among primary and secondary school students.

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.

The early response of plant auxin gene family Aux/IAA (auxin/indole-3-acetic acid) and its interaction with auxin response factor (ARF) are important pattern to regulate plant growth and development. This work identified 28 StoIAA and 24 StoARF members based on the whole genome data of the medicinal plant Senna tora L., which were classified into 10 and 8 subfamilies, respectively. Phylogenetic tree and collinearity analysis showed that S. tora has close evolutionary relationship with the IAA and ARF homologous genes of Glycine max, Medicago truncatula, and the segment duplication events dominate the expansion of StoIAA and StoARF. Gene structure analysis showed that the vast majority of StoIAA and StoARF contain characteristic conserved domain. Transcriptome data showed that StoIAAs and StoARFs were expressed in leaves, roots and seeds, some members had tissue specific expression. The StoIAA and StoARF promoter region most contain functional elements related to stress response, growth and development, hormone induction and secondary metabolism. In addition, gene expression analysis showed that many StoIAAs and StoARFs can quickly respond to drought and salt stress and exhibited same expression patterns under both stress condition. The yeast two-hybrid experiment confirmed that StoARF8 and StoARF10 exhibit varying degrees of interaction with multiple StoIAA proteins, respectively. The above results provide a basis for further biological functional analysis of the Aux/IAA and ARF gene family of S. tora.