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.

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.

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.

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.

ObjectiveTo establish the quality standards for Sennae Folium（Cassia angustifolia） dispensing granules based on standard decoctions. MethodsHigh performance liquid chromatography（HPLC） specific chromatograms were established for 15 batches of Sennae Folium（C. angustifolia） standard decoctions and 10 of Sennae Folium（C. angustifolia） dispensing granules from different manufacturers， and the similarity evaluation， hierarchical cluster analysis（HCA） and principal component analysis（PCA） were performed. Linear calibration with two reference substances（LCTRS） and quantitative analysis of multi-components by single-marker（QAMS） were established for the common peaks in the specific chromatograms to determine the contents of main components in the decoction pieces， standard decoctions and dispensing granules， and to calculate their transfer rates from decoction pieces to standard decoctions and dispensing granules. ResultsThe similarities of specific chromatograms of 15 batches of Sennae Folium（C. angustifolia） standard decoctions and 10 batches of Sennae Folium（C. angustifolia） dispensing granules were all greater than 0.95， and a total of 8 characteristic peaks were calibrated， and five of them were identified， including kaempferol-3，7-O-diglucoside， apigenin-6，8-di-C-glucoside， quercetin-3-O-gentianoside， sennoside B and sennoside A. HCA and PCA results showed that there were certain differences in the composition of different batches of standard decoctions， but no clustering was observed in the production area. As the standard decoctions， the extract rate of 15 batches of samples was 26.54%-45.38%， the contents of kaempferol-3，7-O-diglucoside， apigenin-6，8-di-C-glucoside， quercetin-3-O-gentianoside， sennoside B and sennoside A were 12.16-19.26， 2.57-4.94， 3.27-5.11， 6.75-11.39， 4.69-7.79 mg·g^-1， and their transfer rates from decoction pieces to standard decoctions were 45.41%-79.02%， 29.12%-55.07%， 40.52%-67.90%， 24.72%-49.12%， 27.54%-49.34%， respectively. The extract rates of Sennae Folium（C. angustifolia） dispensing granules（C8-C10） were 38.10%-39.50%， the transfer rates of the above five components from decoction pieces to dispensing granules were 72.85%-73.58%， 53.43%-53.94%， 40.19%-40.74%， 24.62%-25.00%， 28.65%-29.11%， respectively， which were generally consistent with the transfer rates from decoction pieces to standard decoctions. ConclusionCompared with the relative retention time method， LCTRS has higher prediction accuracy and is more suitable for chromatographic columns. The established quality control standard of Sennae Folium（C. angustifolia） dispensing granules based on standard decoction is reasonable and reliable， and all indicators of samples from different manufacturers are within the range specified based on the standard decoction， which can provide reference for the quality control and process research of this dispensing granules.