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.

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.

Object To study the efficacy and potential mechanism of Tongbianling capsule in constipation. Methods The effects of Tongbianling capsule on intestinal motility in normal mice and carbon powder propulsion rate in small intestine of constipation model mice after were observed administration. The potential targets and key pathways of Tongbianling capsule in treating constipation were identified through network pharmacology. To verify the mechanism, the expression of p-PI3K/PI3K, p-AKT/AKT and CASP3 proteins in mouse colon tissue was detected by the western blot. Results The time for mice to excrete the first black stool was shortened and the number of fecal particles was increased in Tongbianling capsule administration group, and the carbon powder propulsion rate of mice in each Tongbianling capsule administration group was increased. The results of network pharmacology showed that treatment of constipation by Tongbianling capsule may be related to signaling pathways such as PI3K-Akt signaling pathway and 5-HT. The protein expression of p-PI3K/PI3K, p-AKT/AKT, and CASP3 in mouse colon tissue could be significantly downregulated in administration group. Conclusion Tongbianling capsule could effectively promote intestinal peristalsis in mice, increase the frequency of defecation, and effectively treat constipation. The mechanism of its action may be related to the direct or indirect regulation of intestinal motility by the PI3K-Akt signaling pathway.

Objective To characterize the properties of Hepa1-6-derived microparticles (Hepa1-6-MPs), investigate their stimulatory effects on dendritic cells (DCs) and their cellular uptake pathways, and explore the specific cytotoxic effects of CD8⁺ T cells induced by Hepa1-6-MP-loaded DCs on hepatoma cell lines, with the aim of developing a novel immunotherapeutic model for hepatocellular carcinoma (HCC). Methods The isolated Hepa1-6-MPs were identified using Western blotting, transmission electron microscopy (TEM) and dynamic light scattering (DLS). Flow cytometry was used to assess the uptake pathways of Hepa1-6-MPs by DCs. Subsequently, enzyme-linked immunosorbent assay (ELISA) was used to measure the effects of Hepa1-6-MP-loaded DCs on the release levels of tumor necrosis factor α(TNF-α) and interferon γ(IFN-γ) into the supernatant of CD8⁺ T cells. Lactate dehydrogenase (LDH) tests were conducted to evaluate the cytotoxic effects of CD8⁺ T cells stimulated by Hepa1-6-MP-loaded DCs on hepatoma cells. Results The morphology, size and protein markers of Hepa1-6-MPs met the established criteria. Hepa1-6-MPs enhanced the expression of DC maturation markers CD80 and CD86, and were internalized by DCs via clathrin-mediated endocytosis and phagocytosis pathways. Subsequently, Hepa1-6-MP-loaded DCs stimulated CD8⁺ T cells to release high levels of TNF-α and IFN-γ, which induced their specific cytotoxicity against HCC cells. Conclusion These findings suggest that Hepa1-6-MP-loaded DCs may be a promising HCC immunotherapeutic tool.
Carcinoma, Hepatocellular/therapy* ; Liver Neoplasms/therapy* ; Dendritic Cells/immunology* ; Humans ; Cancer Vaccines/immunology* ; CD8-Positive T-Lymphocytes/immunology* ; Cell Line, Tumor ; Tumor Necrosis Factor-alpha/immunology* ; Interferon-gamma/immunology* ; Cell-Derived Microparticles/immunology* ; Animals

Acute pancreatitis (AP) is a life-threatening gastrointestinal disorder for which no effective pharmacological treatments are currently available. One of the pharmacological targets that merits further research is the neurokinin 1 receptor (NK1R), which is found on pancreatic acinar cells and responds to the neuropeptide substance P (SP) that participates in AP. Although a few studies have stated the involvement of SP/NK1R in neurogenic inflammation in AP development, the regulatory mechanism remains unclear. In this study, we found that following activation of NK1R by SP, β-arrestin1, a scaffold protein of NK1R, down-regulated transcription of Adss, Adsl, and Ampd in the purine nucleotide cycle, thereby inhibiting mitochondrial function through fumarate depletion. Interestingly, we identified magnolol as a new and natural NK1R inhibitor with a non-nitrogenous biphenyl core structure. It exhibited a beneficial effect on AP by restoring purine nucleotide cycle metabolic enzymes and fumarate levels. Our study not only provides new therapeutic strategies, leading compounds, and drug translation possibilities for AP, but also provides important clues for the study of downstream mechanisms driven by SP in other diseases.

ObjectiveTo investigate the characteristics and distribution of traditional Chinese medicine （TCM） syndromes in the patients with esophageal squamous cell carcinoma （ESCC）. MethodsAn electronic questionnaire was developed to collect the general data and four examination information of ESCC patients treated in 10 areas with high incidence of esophageal cancer in China from June 2020 to March 2021. Multiple analyses including frequency analysis， factor analysis， and hierarchical cluster analysis were performed to analyze the potential syndrome elements， disease location， and common syndromes of ESCC. ResultsA total of 2 027 patients with ESCC were included. Statistical analysis was performed on 113 symptoms， physical signs， 33 tongue manifestation variables， and 23 pulse manifestation variables of the patients’ four examination information. Factor analysis was performed on 55 variables with frequency>10%， extracting 19 common factors. According to clinical experience and expert opinions， the main lesions of patients with ESCC were in the spleen and stomach， and the main syndrome elements were Qi stagnation， blood stasis， phlegm， dampness， and Qi deficiency， with the syndrome element combination of phlegm obstruction + Qi stagnation + blood stasis being the most common. The syndromes can be classified into four categories of liver-stomach disharmony + combined phlegm and Qi obstruction， kidney-spleen dysfunction + combined phlegm and stasis， spleen-kidney Yang deficiency + obstinate phlegm and blood stasis， and liver-kidney Yin deficiency + obstinate phlegm and blood stasis. The main syndrome of ESCC was liver-stomach disharmony + combined phlegm and Qi obstruction in the early stage， liver-spleen dysfunction + combined phlegm and stasis in the middle stage， and spleen-kidney Yang deficiency + obstinate phlegm and blood stasis in the late stage. ConclusionESCC mainly has main pathological features of internal deficiency and external excess and combined deficiency and excess， with the key syndrome elements being phlegm obstruction， Qi stagnation， and blood stasis. The main disease locations are in the spleen and stomach， involving the liver， kidney， chest and diaphragm， heart， and lung. The main syndrome is liver-stomach disharmony + combined phlegm and Qi obstruction. In clinical practice， it is necessary to grasp the pathogenesis dynamics of the disease and use prescriptions according to patients’ syndromes.

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 compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.