Natural products have shown great potential in the research and development of antitumor drugs. However， their clinical application is severely limited by inherent drawbacks such as poor water solubility， low stability， and low bioavailability. Cell membrane biomimetic nanoparticles， as a novel drug delivery system， have provided new strategies to overcome this bottleneck. This review systematically summarizes the preparation methods （e.g.， membrane extrusion， ultrasonic fusion， and microfluidic electroporation） and characterization techniques （e.g.， particle size， Zeta potential， and membrane surface protein detection） of cell membrane biomimetic nanoparticles， with a focus on the application of these derived from various sources in delivering antitumor natural products. Cell membrane biomimetic nanoparticles are endowed with unique biological functions， including low immunogenicity conferred by stem cell membranes， prolonged systemic circulation enabled by red blood cell membranes， and homologous targeting facilitated by tumor cell membranes. Despite these advancements， the technology still faces challenges such as difficulties in large-scale production， high costs， and limited characterization methods. Future research needs to further optimize the relevant processes to promote the clinical translation of cell membrane-biomimetic nanoparticles， thereby offering an efficient and safe novel delivery approach for antitumor therapy using natural products.

ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

Objective To analyze the spatiotemporal distribution characteristics of and trends in the disease burden of dengue fever in China from 2005 to 2024, so as to provide insights into formulation of dengue fever control strategies. Methods Data pertaining to dengue fever cases in China from 2005 to 2024 were retrieved from the Infectious Disease Reporting Information System of Chinese Center for Disease Control and Prevention, and city population, gross domestic product (GDP), GDP per capita, and consumer price index in China were captured from the China Statistical Yearbook, National Bureau of Statistics of China, the China City Statistical Yearbook, and bureaus of statistics in each city. The disability-adjusted life years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs) due to dengue fever were calculated in China from 2005 to 2024. The direct and indirect economic burdens of dengue fever were estimated to calculate the total economic burden. The trends in the disease burden of dengue fever were estimated in China from 2005 to 2024 using a Joinpoint regression model with the software Joinpoint 4.9.0.0, and the average annual percent change (AAPC) and its 95% confidence interval (CI) were calculated. In addition, the DALYs rate and economic burden of dengue fever in China were subjected to global and local spatial autocorrelation analyses using the software ArcGIS 10.8. Results The gross DALYs due to dengue fever were 5 558 person-years in China from 2005 to 2024, and the DALYs of dengue fever increased from 36 person-years in 2005 to 899 person-years in 2024, with an increase of 23.97 folds. The average annual DALYs rate of dengue fever was 0.02 person-years/10⁵ in China during the 20-year study period from 2005 to 2024, and the DALYs rate peaked in 2014 (0.13 person-years/10⁵) and reduced during the COVID-19 pandemic from 2020 to 2022. YLDs were the main contributor of DALYs due to dengue fever in China from 2005 to 2024, with a total of 5 354 person-years, accounting for 96.33% (5 354 person-years/5 558 person-years) of the gross DALYs. The gross DALYs of dengue fever were 2 982 person-years among men (53.66%) and 2 575 person-years among women (46.34%) in China from 2005 to 2024, and high DALYs of dengue fever were measured among residents at ages of 15 to 30 years (1 639 person-years), 30 to 45 years (1 857 person-years), and 45 to 60 years (1 204 person-years), respectively, accounting for 84.56% (4 700 person-years/5 558 person-years) of total DALYs due to dengue fever in China. The total economic burden of dengue fever was estimated to be 612 million Yuan in China from 2005 to 2024, with an average annual economic burden of 30.584 million Yuan. The economic burden of dengue fever increased from 196 000 Yuan in 2005 to 121 million Yuan in 2024 in China, with an increase of 616.35 folds, and the per capita economic burden increased from 3 322.21 Yuan in 2005 to 4 940.01 Yuan in 2024, with an increase of 48.70%. Dengue fever cases were reported in 274 cities (counties) across 31 provinces (autonomous regions, municipalities) in China from 2005 to 2024, with relatively higher DALYs in Guangdong Province and Yunnan Province. Spatial autocorrelation analysis revealed that the disease burden of dengue fever appeared positive aggregation in Chinese cities (counties) from 2005 to 2024 (global Moran’s I = 0.045, Z = 2.24, P < 0.05), with high-high clusters mainly concentrated in the Pearl River Delta region in Guangdong Province and Xishuangbanna Dai Autonomous Prefecture and Pu’er City in Yunnan Province, and the total economic burden (global Moran’s I = 0.032, Z = 9.55, P < 0.001), per capita economic burden (global Moran’s I = 0.208, Z = 27.34, P < 0.001), and the proportion of total economic burdens in GDP in 2024 (global Moran’s I = 0.017, Z = 5.91, P < 0.001) all presented positive aggregation, with relatively higher total economic burdens mainly concentrated in Guangdong Province and Yunnan Province. Joinpoint regression analysis showed that the gross DALYs rates of dengue fever appeared an overall tendency towards a rise in China from 2005 to 2024 (AAPC = 16.24%, P = 0.029), and the DALYs rate presented an overall tendency towards a rise among both men (AAPC = 14.75%, P = 0.028) and women (AAPC = 14.93%, P = 0.037) during the study period. The per capita direct economic burden appeared an overall tendency towards a rise among dengue fever patients in China from 2005 to 2024 (AAPC = 2.16%, P = 0.012); however, there was no significant difference in the trends in the per capita indirect economic burden (AAPC = 0.46%, P = 0.470). In addition, the DALYs rate of dengue fever appeared a tendency towards a rise in 84.67% (232/274) of cities (counties) in China from 2005 to 2024, and the per capita economic burden appeared a tendency towards a rise in 85.40% (234/274) of cities (counties), while the DALYs rate and per capita economic burden of dengue fever appeared a tendency towards a rise in 77.01% (211/274) of cities (counties). Conclusions The disease burden of dengue fever significantly increased in China from 2005 to 2024. It is recommended to reinforce integrated dengue fever control in high-risk areas and among high-risk populations, and to improve the surveillance of imported dengue fever cases and vector control.

AIM: To screen and analyze the influencing factors of myopia among primary and secondary school students and establish a predictive model to provide ideas for the prevention and control measures of myopia among children and adolescents.METHODS:A total of 1 759 primary and secondary school students from 2 primary schools, 2 junior high schools, 2 senior high schools and 1 vocational high school in the urban area of Qingdao were sampled by means of stratified cluster sampling in September 2023. Vision screening and a questionnaire survey on influencing factors were carried out based on machine learning algorithms. The screening and determination were mainly conducted in accordance with the Standard Logarithmic Visual Acuity Chart(GB/T11533-2011)and the Specifications for Screening Myopia in Children and Adolescents. The influencing factors of myopia were analyzed and a prediction model was developed based on the machine learning algorithms LASSO in combination with XGBoost, and visualization was achieved through an interactive Nomogram. Statistical analysis was performed using R statistical software version 4.3.3.RESULTS:The screening prevalence of myopia among primary and secondary school students in the urban area of Qingdao was 70.61%(1 242 cases). The optimal predictive variables for screening were grade, gender, whether parents were myopic, daily indoor sedentary time, appropriate distance between eyes and books during reading and writing, daily sleep time, distance between eyes and TV screen when watching TV/playing video games exceeding 3 meters, the playground during breaks, total duration of tutorial classes, how often eyes are rested during near work, daily computer usage time, and average daily homework time after school, totaling 12 influencing factors. The AUCs of the training set and test set were 0.770(95%CI:0.751-0.789)and 0.732(95%CI:0.714-0.750), respectively.CONCLUSION: A machine learning-based prediction model was developed and validated to predict the risk of myopia onset in primary and secondary school students, accompanied by effective visualization techniques.

With the rapid development of artificial intelligence （AI） technology， the ethical governance of AI has gained increasing attention. The Recommendation on the Ethics of Artificial Intelligence was issued by the United Nations Educational， Scientific and Cultural Organization in 2021， which clarified several principles for the ethical governance of AI. In the field of rehabilitation medicine， the research and application of AI technology have significantly improved patients’ quality of life and survival. However， due to the specificity of the service population in rehabilitation medicine， which is mostly for the sick， injured， disabled， and elderly， a series of complex ethical issues have also arisen. This paper analyzed in detail the ethical issues and challenges encountered in the research and application of AI technology in the field of rehabilitation medicine from various aspects， such as informed consent， security of privacy and data， patients’ physical and mental rehabilitation， compliance regulation， protection of specific groups， and promotion of equity. According to the principles of the Recommendation on the Ethics of Artificial Intelligence and others， response strategies were proposed， including multi-party collaboration and interdisciplinary cooperation， improving and refining relevant laws and regulations， strengthening ethical education across society， establishing accountability mechanisms， increasing investment， promoting equity， and other measures， to promote the healthy development of research and application of AI technology in the field of rehabilitation， as well as benefit humanity.

ObjectiveTo systematically analyze the global policy framework, standard systems and application technology models of digital rehabilitation within the framework of the World Health Organization (WHO) Global Digital Health Strategy and propose policy recommendations for the future development of digital rehabilitation. MethodsBased on the policies on digital health and rehabilitation development issued by the WHO, focusing on the Global Digital Health Strategy, Rehabilitation 2030 Initiative, Rehabilitation in Health Systems, Rehabilitation in Health Systems: A Guide for Action, and World Report on Disability, a systematic review was conducted, to explore the policy architecture and core content of digital rehabilitation, the standard system for digitalizing rehabilitation, and key technological models for the development of digital rehabilitation. ResultsIn the context of global health and digital transformation, the development of digital rehabilitation services was an essential component of the global digital health strategy. Building a comprehensive policy framework and content system for digital rehabilitation was critical for strengthening rehabilitation data governance, enhancing data utilization efficiency, and ensuring data privacy and security. Empowering rehabilitation with digital technology was vital for improving the standardization, effectiveness, coverage, quality and safety of rehabilitation services. International digital rehabilitation policies primarily involved the following areas: policy and governance, digital standard systems, data privacy, security and ethics, digital talent cultivation and capacity building, and monitoring, evaluation and continuous improvement of digitally empowered rehabilitation services. The standard system for rehabilitation digitization covered the three major reference classifications of the WHO Family of International Classifications, including International Classification of Diseases Eleventh Revision (ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI), especially ICF. It also included international data interoperability standards, data security and privacy protection standards, data quality and certification standards, and health information standards, etc. The application technology models of digital rehabilitation primarily included data-driven service models, artificial intelligence -enabled models, and remote rehabilitation models combined with virtual reality, augmented reality technologies, and Internet of Things support. ConclusionThe establishment and implementation of comprehensive policies, standards and technological models for digital rehabilitation are crucial for driving the digital transformation and development of global rehabilitation services. Under the framework of the WHO Global Digital Health Strategy, it is necessary to build adaptive digital rehabilitation policy frameworks, and enhance digital governance capabilities and levels, establishing and improving digital rehabilitation standard systems, and promoting the interoperability and integration of rehabilitation data with other health big data. Meanwhile, it is essential to actively develop data-driven technological models for rehabilitation services to comprehensively improve the accessibility, availability, quality and safety of rehabilitation services.

To summarize Professor GAO Shuzhong's clinical experience in treating idiopathic tinnitus with a combination of acupuncture and Chinese meteria medica. It is believed that idiopathic tinnitus is mostly caused by weak lungs and spleen， kidney essence deficiency， liver constraint transforming into fire， and binding constraint of heart qi. Treatment advocates the combination of acupuncture and Chinese meteria medica in clinical practice. Acupuncture treatment mainly focus on the method of opening the orifices by syndrome identification in combination with Ermen （TE 21）， Tinggong （SI 19）， Tinghui （GB 2）， Shenmai （BL 62） to regulate qi and blood， and supporting with Baihui （GV 20）， Yintang （EX-HN 3）， Taichong （LR 3）， and Yanglingquan （GB 34） to soothe the liver， resolve constraint， and calm the mind. Oral administration of Chinese medicinal prescription usually includes modified Yiqi Congming Decoction （益气聪明汤） and Tongqi Powder （通气散）， and the external administration of Chinese medicinal prescription can apply self-prescribed Wenqing Powder （温清散） to navel moxibustion.

Objective To explore the accuracy of artificial intelligence-based diagnosis of ovarian malignant tumors and the identification of benign and malignant tumors under transabdominal scanning and transvaginal scanning methods. Methods A dataset of transabdominal and transvaginal two-dimensional ultrasound images was used and the images were preprocessed to enhance quality. The region of interest was segmented and divided into a training set and a test set. A convolutional neural network (CNN) was trained on the images in the training set, and the accuracy of the model on the test set was calculated. Results Transvaginal scanning was 14% more accurate in diagnosing malignant ovarian tumors than transabdo-minal scanning on the test set. For identifying the benign and malignant ovarian tumors containing cystic components, a mixture of transvaginal and transabdominal scanning increased the accuracy by 9.7% over transabdominal scanning alone. Conclusion CNN can identify ovarian malignant tumors under both scanning methods, but the accuracy of transvaginal scanning is higher than that of transabdominal scanning, and the CNN model has a higher accuracy in identifying benign and malignant ovarian tumors under transvaginal scanning.