With the integration of 5G communication technology and robotic surgical systems, remote robot-assisted thoracic surgery is overcoming geographical barriers, offering an innovative approach to addressing the uneven distribution of medical resources. This study conducted a systematic literature review—using databases such as PubMed and CNKI, with the search period extending up to 2025—incorporating clinical studies, case reports, and review articles to comprehensively evaluate the clinical efficacy and safety of 5G-enabled remote robot-assisted thoracic surgery (5G-RRATS). The analysis also examined current technological limitations and potential future development trajectories. Existing evidence indicates that, given adequate technical support, 5G-RRATS can achieve perioperative outcomes comparable to those of conventional local robotic surgeries across procedures including pulmonary wedge resection, lobectomy, and esophagectomy. Furthermore, it demonstrates potential advantages in minimizing surgical incisions and reducing intraoperative blood loss. Nevertheless, challenges related to network stability, latency control, interdisciplinary collaboration between medical and engineering teams, and legal, regulatory, and ethical considerations continue to hinder widespread clinical adoption. Looking ahead, the emergence of a "one-to-many" remote surgical model, combined with the integration of artificial intelligence and augmented reality technologies, as well as advancements in low-orbit satellite communications, may enable 5G-RRATS to further advance precision and efficiency in thoracic surgery, thereby facilitating equitable access to high-quality care for a broader patient population.

ObjectiveTo construct an actor-network for integrating physical activity into rehabilitation services based on the World Health Organization Rehabilitation in Health Service System framework and actor-network theory (ANT). MethodsContent analysis was employed using the six building blocks of health service systems as the theoretical framework. Actors related to rehabilitation services were extracted and categorized into a rehabilitation actor pool, while a physical activity actor pool was formed based on four major physical activity scenarios. Actors from both pools were integrated, deduplicated and classified to form a final list of integrated actors. Using ANT, the construction process of the actor network integrating physical activity into rehabilitation was analyzed through the four stages of translation: problematization, interessment, enrollment and mobilization. ResultsA dynamic integration network was constructed, comprising human actors (patients, rehabilitation professionals, researchers, sports coaches, government departments, medical institutions, community organizations and industry media, etc.) and non-human actors (assistive devices, sports infrastructure, smart equipment, information systems, online exercise guidance systems, laws and regulations, strategic documents, and exercise prescriptions, etc.). The study identified maximizing rehabilitation outcomes as the mandatory passage point and elaborated on the critical role of government departments as focal actors in coordinating various stakeholders. ConclusionThe integration of physical activity into rehabilitation services is a dynamic network constructed by diverse actors through a process of translation. ANT provides an operational theoretical framework for cross-departmental governance of rehabilitation policies in China, promotes the spatial expansion of the rehabilitation field, and drives its transformation toward a networked and ecological system. The government needs to play a leading role in facilitating role reconstruction and synergy among heterogeneous actors in both the sports and rehabilitation sectors through mechanism design, to create a bidirectional empowerment mechanism that fosters mutual progress and ensures the sustainable development of integrated services.

Abstract To improve adolescent health behavior, the study summarizes and analyzes the performance, pathways of transmission, and influencing factors of the intergenerational transmission of adolescent health behaviors from the perspective of intergenerational transmission. The study emphasizes the need to deepen research on the intergenerational transmission of adolescent health behaviors, promote multidisciplinary and cross team collaboration, and shift adolescent health care from individual focused care to a holistic approach that prioritizes family and community culture. Simultaneously, an action framework should be established to block the intergenerational transmission of health risk behaviors, with a focus on childhood and adolescence. Additionally, parent-child participatory health education and health promotion activities should be carried out under a tripartite coordinated intervention model involving the community, school, and family, collectively fostering the development of healthy behaviors among adolescents.

ObjectiveTo investigate the contamination status and drug resistance of diarrheagenic Escherichia coli (DEC) in commercially available frozen poultry and livestock meat and their products in Zhangzhou City, and to provide a reference basis for the prevention and control of foodborne diseases caused by DEC. MethodsIn 2024, a total of 240 samples of frozen poultry and livestock meat and their products were collected from supermarkets, farmers’ markets, and online stores. Isolation, identification, and drug resistance analyses of DEC were conducted. ResultsAmong the 240 samples, 42 samples tested positive for DEC, with a detection rate of 17.50%. Specifically, the DEC detection rate in frozen poultry meat and its products （21.15%） was significantly higher than that in frozen livestock meat and its products （10.71%） (χ²=4.122, P=0.042); the detection rate in the first and fourth quarters （26.67%） was higher than that in the second and third quarters （8.33%） (χ²=13.968, P=0.003). A total of 44 DEC strains were isolated, among which enteroaggregative Escherichia coli (EAEC) was the dominant pathotype, accounting for 90.91%, and mainly carried the astA gene, followed by the pic gene. At a distance level of 10 000 in matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) cluster analysis, the 44 DEC strains were divided into 2 major clusters, with 29 strains in Cluster Ⅰ and 15 strains in Cluster Ⅱ； at a distance level of 200, the strains could be classified into 12 types, with type c being the dominant one (27.27%). Among the 44 DEC strains, 37 DEC strains showed different degrees of resistance to 11 categories of antibiotics, 36 strains were resistant to 3 or more classes of antibiotics, with multidrug-resistant strains accounting for 81.82%, and 21 strains were resistant to more than 7 types of antibiotics. ConclusionThe detection rate of DEC in commercially available frozen poultry and livestock meat and their products in Zhangzhou City is relatively high, and the prevalence of multidrug resistance is severe. These findings should warrant the attention of relevant authorities, to strengthen food safety supervision and the management of antibiotic use, and enhance public health education to reduce the risk of foodborne diseases caused by DEC contamination.

ObjectiveTo establish a semi-automated workflow for hyoid motion tracking based on submental ultrasound videos, evaluate the reliability of manual measurements and the validity of semi-automated tracking, and compare the analytical efficiency. MethodsA total of 46 healthy adults were recruited at Xiamen Fifth Hospital from October, 2024 to August, 2025, and performed dry swallows and swallows of 5 mL and 10 mL moderately-thick liquid boluses. A total of 414 sagittal-plane submental ultrasound videos were acquired. After video quality screening and tracking quality-control procedures, 286 videos were included for paired analysis. A semi-automated tracking pipeline was constructed combining the channel-and-spatial-reliability tracker and You Only Look Once version 8 small (YOLO v8s). Intra-class correlation coefficients [ICC(A,1)] based on the absolute-agreement single-measurement model were calculated for test-retest and inter-rater manual measurements. Using the manual repeated-measurement results as the reference standard, Pearson correlation coefficient (r), ICC(A,1) and concordance correlation coefficient (CCC) were computed for the semi-automated measurements. A total of 5 000 subject-clustered bootstrap resamples were performed to calculate 95% confidence intervals. Cumulative analysis time of manual and semi-automated workflows was compared. ResultsFor the independent test set of YOLOv8s, precision and recall reached 0.940 and 0.956. The mean average precision was 0.974 at an intersection-over-union threshold of 0.5, and 0.609 averaged across thresholds ranging from 0.5 to 0.95. For manual measurements of maximum horizontal and vertical hyoid displacement, the test-retest ICC(A,1) values were 0.925 and 0.912, respectively; the inter-rater ICC(A,1) values were 0.815 and 0.775. For semi-automated versus manual reference measurements, r = 0.865，ICC(A,1) = 0.843，CCC = 0.843 in maximum horizontal displacement; and r = 0.754，ICC(A,1) = 0.746，CCC = 0.745 in maximum vertical displacement. Mean biases were -0.772 mm and -0.229 mm, with 95% limits of agreement of -4.567 to 3.023 mm and -2.918 to 2.460 mm for horizontal and vertical directions. Cumulative manual analysis time was 526 minutes for rater A and 713 minutes for rater B. The semi-automated workflow consumed 155 minutes, in which 40 minutes for computational processing and 115 minutes for manual review. ConclusionManual measurement of maximum hyoid displacement is reliable using qualified submental ultrasound videos. Semi-automated tracking achieves acceptable agreement with manual reference and greatly reduces analysis workload.

Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.

Objective:To investigate the relationship between triglyceride glucose (TyG) index and major adverse cardiovascular events (maces) within 30 days after discharge in patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI) and its predictive value.Methods:A single center retrospective study was conducted to select AMI patients with PCI in the chest pain center of the Fourth Hospital of Changsha from January 2023 to January 2024 by a convenience sampling method. The clinical data and follow-up information of the patients were collected. The relationship between TyG index and Maces and its predictive value were tested by correlation analysis and logistic regression model.Results:A total of 110 patients met the inclusion and exclusion criteria, including 88 males and 22 females, aged (61.46 ± 12.42) years old. Spearman correlation analysis showed that TyG index was positively correlated with maces 30 days after discharge ( r = 0.421, P<0.001). Logistic regression analysis showed that TyG index was a risk factor for maces in AMI patients 30 days after discharge ( OR = 9.033, 95% CI 2.835-8.788, P<0.001). Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) was 0.854 (95% CI 0.765-0.943, P<0.001). Conclusions:TyG index has a significant positive correlation with maces within 30 days after discharge, which is an independent risk factor for maces within 30 days after discharge. Risk stratification by TyG index is more conducive to the management of clinical postoperative nursing and nursing education after discharge.

Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.