The American Heart Association (AHA) and the American College of Cardiology (ACC), in collaboration with multiple professional organizations, jointly released the "Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults" in August 2025. Based on the latest evidence-based medical findings from February 2015 to January 2025, the guideline proposes an individualized treatment strategy grounded in total cardiovascular disease risk stratification, incorporates the novel PREVENT risk assessment model, lowers the medication initiation threshold and control targets for high-risk populations, and provides specific management recommendations for special populations. This article provides an interpretation of these updates and conducts a comparative analysis with the current status of hypertension prevention and treatment in China as well as Chinese guidelines, aiming to offer reference for hypertension control practices in China.

The American Heart Association (AHA) officially released the "2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association" on January 27, 2025. This report systematically compiles the latest statistics on major cardiovascular diseases worldwide, while simultaneously integrating relevant outcome indicators, including quality of care, procedures, and economic costs, and updating the global prevalence patterns and evolving trends of diverse risk factors impacting cardiovascular health, providing essential guidance for the prevention, diagnosis, and treatment of cardiovascular diseases. Synthesizing insights from this pivotal report and other relevant studies, this article highlights key findings concerning the global prevalence and mortality of heart diseases, associated risk factors, and emerging diagnostic and therapeutic technologies.

BACKGROUND:Patellar tendonitis can present as tendon degeneration that fails to heal due to tissue overload and incomplete recovery.Patellar tendonitis is a predisposition to high jumping and its pathogenesis has not been clearly defined.OBJECTIVE:To explore the stress-strain relationship of patellar tendon in the take-off technique of high jump through the finite element model with accurate human anatomical structure,so as to provide ideas for the prevention and rehabilitation of patellar tendinitis.METHODS:Based on the CT and MRI imaging data of the lower extremity(including the knee and ankle)of one subject(22 years old,183 cm height,70 kg body mass),a three-dimensional finite element model of the lower extremity was reconstructed using medical imaging software,reverse engineering software and modeling software.The plantar pressure of the take-off leg was collected in eight subjects by gait testing system,and the technical action of high jump take-off was collected by motion capture system.The captured data were imported into human sports biomechanics software for analysis,and kinematic and kinetic data were obtained as the boundary conditions of finite element model for finite element simulation analysis.RESULTS AND CONCLUSION:The force borne by the patellar tendon reached 3.29 times of its own body mass when the subjects took off.In the take-off stage,the peak values of normal equivalent stress,strain and shear stress of the patellar tendon were 127.76 MPa,0.81 and 37.69 MPa,respectively,which were in the nonlinear region of the stress-strain curve,and the peak values were distributed in the proximal and posterior parts of patellar tendon.To conclude,the high patellar tendon force,strain and shear stress caused by the load of 3.29 times its own body mass during take-off are related to the induction of patellar tendinitis.

BACKGROUND:Patellar tendonitis can present as tendon degeneration that fails to heal due to tissue overload and incomplete recovery.Patellar tendonitis is a predisposition to high jumping and its pathogenesis has not been clearly defined.OBJECTIVE:To explore the stress-strain relationship of patellar tendon in the take-off technique of high jump through the finite element model with accurate human anatomical structure,so as to provide ideas for the prevention and rehabilitation of patellar tendinitis.METHODS:Based on the CT and MRI imaging data of the lower extremity(including the knee and ankle)of one subject(22 years old,183 cm height,70 kg body mass),a three-dimensional finite element model of the lower extremity was reconstructed using medical imaging software,reverse engineering software and modeling software.The plantar pressure of the take-off leg was collected in eight subjects by gait testing system,and the technical action of high jump take-off was collected by motion capture system.The captured data were imported into human sports biomechanics software for analysis,and kinematic and kinetic data were obtained as the boundary conditions of finite element model for finite element simulation analysis.RESULTS AND CONCLUSION:The force borne by the patellar tendon reached 3.29 times of its own body mass when the subjects took off.In the take-off stage,the peak values of normal equivalent stress,strain and shear stress of the patellar tendon were 127.76 MPa,0.81 and 37.69 MPa,respectively,which were in the nonlinear region of the stress-strain curve,and the peak values were distributed in the proximal and posterior parts of patellar tendon.To conclude,the high patellar tendon force,strain and shear stress caused by the load of 3.29 times its own body mass during take-off are related to the induction of patellar tendinitis.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Effective symptom management can alleviate the physical and psychological distress experienced by patients with cancer, improve quality of life, and contribute to treatment adherence and improve clinical outcomes. However, most existing guidelines are developed for healthcare professionals, and patients and the public have limited access to standardized and comprehensible guidance on symptom management. To address this gap and to facilitate effective communication and shared decision-making, this study proposes the development of a patient version of the cancer symptom management guideline. The development process will adhere to the methodological framework recommended by the Guidelines International Network and the World Health Organization. The GRADE approach will be employed to assess the certainty of evidence and to formulate recommendations. In addition, the process will be informed by the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument and the Reporting Items for Practice Guidelines in Healthcare-Public or Patient Versions of Guidelines (RIGHT-PVG). This protocol outlines the establishment of the guideline working group, the identification and prioritization of key questions, evidence retrieval and appraisal, and the formulation of recommendations, with the aim of ensuring methodological rigor and transparency in the development of the patient guideline and providing methodological reference for similar guideline initiatives.

The Expert Consensus on the Deployment of DeepSeek in Medical Institutions serves as a detailed guideline for the deployment of DeepSeek in medical institutions. It was developed by experts in the fields of healthcare， hospital management， medical information， health policy， law， and medical ethics from nearly 30 leading domestic medical and academic research institutions， based on relevant domestic and international laws and regulations as well as the practices of medical institutions. It aims to provide medical institutions with a scientific， standardized， and secure deployment guideline to ensure that the application of artificial intelligence （AI） technologies in healthcare， including but not limited to DeepSeek， conforms to the unique characteristics of the healthcare industry and effectively promotes the improvement of medical service levels. From the three aspects of pre-deployment evaluation， deployment implementation， and post-deployment management and monitoring， the key factors that medical institutions should consider when introducing DeepSeek were elaborated in detail， including medical demand compatibility， technical capabilities and infrastructure， legal and ethical risks， data preparation and management， model selection and optimization， system integration and training， performance monitoring and continuous optimization， risk management and emergency response， as well as compliance review and evaluation. This provides a comprehensive deployment framework for medical institutions to ensure the safety and effectiveness of technology applications.

Objective:To explore the association between the systemic immune-inflammation index (SII) and obesity metabolic phenotypes, as well as metabolic features in children and adolescents.Methods:A cross-sectional study was conducted using the random cluster sampling method from March 2023 to May 2024. Children and adolescents aged 9-17 years in Guangzhou were surveyed through questionnaires, physical measurements, and blood tests. According to BMI and metabolic status, participants were classified into normal-weight groups [metabolically healthy normal weight (MHNW) and metabolically unhealthy normal weight (MUNW)] and overweight/obese groups [metabolically healthy overweight/obese (MHO/O) and metabolically unhealthy overweight/obese (MUO/O)]. After natural log-transformation of SII values (lnSII), multinomial logistic regression was used to assess the association between SII and obesity metabolic phenotypes, while binary logistic regression was applied to assess the relationship between SII and metabolic phenotypes in the overweight/obese subgroup. Linear regression model and restricted cubic spline (RCS) were employed to examine the relationship between SII and metabolic features among the entire population.Results:A total of 3 749 participants were included. After adjusting for covariates, for every unit increase in lnSII, the risk of MHO/O and MUO/O increased by 93% ( OR=1.93, 95% CI: 1.56-2.40, P<0.001) and 156% ( OR=2.56, 95% CI: 2.02-3.25, P<0.001), respectively. In the overweight/obesity subgroup, for every unit increase in lnSII, the risk of MUO/O increased by 37% ( OR=1.37, 95% CI: 1.01-1.87, P=0.045). Linear regression model and RCS showed that lnSII was positively correlated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) (SBP: β=1.39, 95% CI: 0.67-2.11, P<0.001; DBP: β=1.27, 95% CI: 0.79-1.75, P<0.001). lnSII also had a non-linear relationship with triglyceride ( Pnonlinear=0.032) and high-density lipoprotein cholesterol ( Pnonlinear=0.002). Conclusion:Elevated SII levels are associated with unfavorable obesity metabolic phenotypes, higher blood pressure, and altered lipid profiles in children and adolescents. SII may be a potential driving factor for metabolic heterogeneity in children and adolescents.

OBJECTIVE To identify Bupleurum chinense pieces and its counterfeits based on the characteristic chromatogram and UPLC-Q-TOF/MS technology.METHODS Thin layer chromatography was used to identify Bupleurum chinense and its counterfeits collected from different origins and different producing areas.Then,the chemical constituents of Bupleurum chinense and its counter-feits were compared according to the established HPLC characteristic chromatogram,and the representative differential markers of Bup-leurum chinense counterfeits were further identified by UPLC-Q-TOF/MS technology.RESULTS Thin layer chromatography showed that different original Bupleurum chinense pieces had saikosaponin A and saikosaponin D,which could be distinguished according to the intensity and position of fluorescent spots.There were 14 and 16 common peaks in the specific chromatogram of Bupleurum chinense and its vinegar-processed products respectively,and 6 components were identified.The chemical components of Bupleurum chinense pieces from different producing areas were similar,and the established method could better reflect the characteristics of Bupleurum chinense.Further comparison of the specific chromatogram of Bupleurum chinense and its counterfeits showed that the composition of B.marginatum Wall.was close to that of the authentic Bupleurum chinense.,B.bicaule Helm and B.longiradiatum Turcz.had their own characteristic peaks with high response values.The content of saponins in B.marginatum var.stenophyllum was significantly high-er.A total of 69 Bupleurum compounds were identified by UPLC-Q-TOF/MS mainly triterpenoid saponins,followed by flavonoids,a few chromones,phenylpropanoids and alkynes.The results of cluster analysis showed that the interspecific differences of Bupleurum chinense were obvious,and different Bupleurum counterfeits had representative differential markers.CONCLUSION The established characteristic chromatogram and UPLC-Q-TOF/MS method can be used for the chemical identification of Bupleurum chinense and its counterfeits,which provide a basis for the comprehensive and rational development and utilization of Bupleurum resources.

Objective:To explore the current state of thriving at work among orthopedic nurses and analyze its influencing factors, so as to provide a basis for nursing administrators to promote the development of orthopedic nurses.Methods:Convenience sampling was used to select 469 orthopedic nurses from the Honghui Hospital, Xi'an Jiaotong University from January to February 2025 as research subjects. The General Information Questionnaire, Thriving at Work Scale (TWS), Leisure Crafting Scale (LCS), and General Self-Efficacy Scale (GSES) were used to investigate general information, thriving at work, leisure crafting, and self-efficacy. Pearson correlation analysis was employed to investigate the correlation between orthopedic nurses' thriving at work, leisure crafting, and general efficacy. Multiple linear regression was used to analyze the factors influencing the thriving at work of orthopedic nurses.Results:A total of 469 questionnaires were distributed, with 442 valid responses collected, with a valid response rate of 94.24%. The TWS score of orthopedic nurses was (35.70±5.20). Pearson correlation analysis revealed that scores on TWS were positively correlated with scores on both LCS and GSES ( r=0.593, 0.569; P<0.01). Multiple linear regression analysis revealed that being an only child, liking nursing work, interest in the nursing profession, leisure crafting, and general self-efficacy were factors influencing the thriving at work of orthopedic nurses ( P<0.05) . Conclusions:The thriving at work of orthopedic nurses is at a moderate level. Orthopedic nurses who are only children, the better the leisure crafting and general self-efficacy of orthopedic nurses have the higher their thriving at work. Orthopedic nursing managers should provide a supportive environment to enhance orthopedic nurses' intrinsic motivation, thereby promoting their thriving at work.