Objective: To review the relationship between 24 hour movement behaviors and physical and mental health among college students, in order to provide evidence to support health promotion and further research in universities. Methods: Following the Joanna Briggs Institude(JBI) scoping review guidelines, relevant studies published in databases from inception date to December 26, 2025 were searched, including PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI) and Wanfang Data. For studies meeting the inclusion and exclusion criteria, a descriptive analysis was conducted to summarize the measurement tools used, adherence rates with guidelines, and the relationship between physical and mental health. Results: A total of 30 studies were included. Measurement tools exhibited a high heterogeneity, with questionnaires being the primary method. The rate of full adherence with 24 hour movement behaviors among college students was less than 30%. Moderate to vigorous physical activity and high quality sleep were associated with improvements in physical fitness, cardiopulmonary function, and mental health, while prolonged sitting was negatively associated with obesity and depression. Equivalent time substitution analysis indicated that increasing moderate to vigorous physical activity and reducing prolonged sitting could significantly improve health outcomes. Conclusions The adherence rate for 24 hour movement behaviors among college students is low and it is closely associated with physical and mental health. Future studies should standardize measurement tools, and implement targeted interventions based on the optimization of daily activity patterns.

Background: Malignant pleural effusion (MPE), a common complication of advanced cancers, is associated with poor prognosis and reduced quality of life. Although host–tumor interactions are known to drive MPE development, the associated immune dynamics during disease progression remain unclear. Using a Lewis lung carcinoma-induced MPE model in C57BL/6JNidfc mice, we systematically evaluated general parameters and immune cell changes at two-day intervals throughout disease progression. Results: The day of Lewis lung carcinoma cell injection into the pleural space was designated as day 0. By day 10 post-injection (p.i.), MPE-bearing mice exhibited ~ 10% body weight loss, marking the experimental endpoint. Pleural tumor mass and pleural effusion volume were minimal up to day 4 p.i. but increased sharply from day 6 onward.CD45⁺ immune cell counts rose over time, and days 6, 8, and 10 p.i. marked key stages of MPE progression. On day 6, B cells, T cells, and natural killer cells, but not macrophages and neutrophils, increased significantly compared to earlier timepoints. By day 8, all immune cell subsets except T cells exceeded day 6 levels, and at day 10, natural killer cell numbers declined while others continued to increase. Besides, the numbers of CD8⁺ T cells, Th1 cells, regulatory T cells, and M2 macrophages progressively increased from day 6 to 10. Based on these data, days 6 and 10 were defined as early and advanced MPE stages, respectively, with distinct immune phenotypes. In advanced MPE, CD8⁺ T cells displayed reduced IFN-γ, TNF-α, Granzyme B, Perforin, FasL, and Ki-67, but upregulated PD-1 and CTLA-4 relative to early stage. Similarly, Th1 cells showed decreased IFN-γ, TNF-α, and IL-2 production along with reduced Ki-67 expression. Advanced-stage M2 macrophages exhibited lower MHC-II levels and impaired phagocytosis, but higher PD-L1 and IL-10 production, while neutrophils showed reduced TNF-α release and phagocytic activity. Conclusions Our findings characterize the temporal immune dynamics associated with MPE progression in a mouse model, revealing a transition from an early immunostimulatory state to a late immunosuppressive state. This study enhances our understanding of MPE immunopathogenesis and provides a foundation for developing precise, stagespecific therapeutic strategies.

Although global brain science research has progressed rapidly in recent decades, several fundamental questions in neuroscience remain unresolved. In particular, the physical mechanism underlying neural signal transmission remains controversial, and the carriers responsible for neural information storage and retrieval have not yet been fully clarified. These unresolved issues motivate us to re-examine the processes of neural information generation, transmission, integration, storage, and retrieval from multiple perspectives. A key observation is that neural electromagnetic activities are closely associated with time. Their duration, temporal structure, and dynamic evolution play crucial roles in neural information processing. In this work, we analyze neural electromagnetic activities from the perspective of temporal scales (referred to here as the “time course”). By reviewing and integrating findings from previous studies, we examine the characteristic time requirements and dynamic features of neural processes occurring at different stages of information processing. These stages include neural signal generation, signal transmission along axons, synaptic integration, synaptic plasticity, and memory formation and retrieval. Based on this temporal analysis, we outline a framework describing neural electromagnetic activities across a wide range of time scales, spanning from microseconds to minutes, hours, or even longer periods associated with long-term memory, which suggests that neural information processing involves multiple physical processes operating at different time levels. Rapid electromagnetic events may occur on microsecond scales, whereas electrophysiological phenomena such as action potentials typically last on the order of milliseconds. Longer time scales are associated with synaptic plasticity and memory-related processes. From this perspective, we propose that the physical carrier of neural information may be transient electromagnetic pulses with durations on the microsecond scale. In this framework, action potentials can be interpreted as the macroscopic electrophysiological manifestation of underlying electromagnetic processes triggered by ionic currents across neuronal membranes. Rather than being the fundamental neural signal itself, the action potential may represent a measurable membrane-level response associated with the successful activation of these electromagnetic events. Moreover, we discuss a possible mechanism for long-term memory storage. Considering the apparent temporal contradiction between the millisecond-scale excitation of neurons and the long-term persistence of memories, we believe that long-term memory information may be stored within neural network topologies formed by electrical synapse coupling. Such structures, referred to as electrically coupled memory networks (ECMNs), may enable neurons within the same network to respond rapidly and synchronously to stimuli, thereby facilitating efficient memory retrieval. Overall, this study emphasizes the importance of considering the temporal organization of neural electromagnetic activities when interpreting neural signaling mechanisms. It may provide new insights into the physical nature of neural information carriers and the mechanisms of memory storage and retrieval. Furthermore, highlighting the potential role of electromagnetic interactions in neural activity may contribute to the development of new theoretical frameworks and experimental approaches in neuroscience. Such perspectives may also offer valuable references for future research on neural coding, brain function mechanisms, and neuromodulation technologies.

ObjectiveThe present study aimed to investigate alterations in white matter microstructure and spontaneous neural activity in male college smokers, and to further explore their associations with nicotine dependence. Given that adolescence and early adulthood represent critical periods for brain maturation, particularly for white matter development, understanding the neural correlates of smoking behavior during this stage is of substantial importance for both neuroscience and public health. MethodsA total of 115 male undergraduate students were initially recruited for this study. After quality control and exclusion procedures, 52 male college smokers and 42 demographically matched healthy non-smokers were included in the final analysis. All participants underwent multimodal magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI). White matter fiber tracts were reconstructed using the automated fiber quantification (AFQ) method, which enables precise identification and quantification of major fiber bundles. Eighteen major white matter tracts were segmented for each participant. Along the core trajectory of each tract, 100 equidistant nodes were sampled. Fractional anisotropy (FA) was calculated at each node to assess white matter microstructural integrity, while amplitude of low-frequency fluctuation (ALFF) was computed to evaluate spontaneous neural activity within white matter tracts. Between-group differences in FA and ALFF were assessed using two-sample t-tests, with appropriate corrections applied for multiple comparisons. Furthermore, Pearson correlation analyses were conducted to examine the relationships between imaging-derived metrics (FA and ALFF values in regions showing significant group differences) and nicotine dependence severity, as measured by the Fagerström test for nicotine dependence (FTND). ResultsCompared with healthy non-smokers, male college smokers exhibited significantly increased FA values in several white matter tracts, including the left thalamic radiation, right corticospinal tract, forceps major of the corpus callosum, left uncinate fasciculus, and right arcuate fasciculus. These findings suggest altered microstructural organization or increased directional coherence within these pathways. In addition, smokers demonstrated significantly elevated ALFF values in the forceps major, right uncinate fasciculus, and left arcuate fasciculus, indicating enhanced spontaneous neural activity in these white matter regions. Correlation analyses revealed that FA values in the left thalamic radiation and right corticospinal tract were negatively correlated with FTND scores, suggesting that higher levels of nicotine dependence were associated with reduced microstructural integrity or altered fiber organization in these regions. In contrast, ALFF values in the forceps major and right uncinate fasciculus were positively correlated with FTND scores, indicating that greater nicotine dependence was associated with increased spontaneous neural activity in specific white matter pathways. ConclusionThe present study provides evidence that male college smokers exhibit distinct alterations in both white matter microstructure and functional activity. These abnormalities are not uniformly distributed but rather localized to specific fiber tracts implicated in sensorimotor processing, interhemispheric communication, and higher-order cognitive and emotional regulation. Importantly, the observed associations between imaging metrics and nicotine dependence severity suggest that these structural and functional alterations may reflect neurobiological mechanisms underlying addiction. The combination of AFQ-based tract profiling and multimodal MRI offers a sensitive approach for detecting subtle changes along white matter pathways, highlighting its potential utility in identifying neuroimaging biomarkers of nicotine dependence. Overall, these findings indicate that smoking during early adulthood may disrupt ongoing white matter maturation, potentially leading to long-term consequences for brain function. This study provides novel insights into the neural basis of nicotine dependence and underscores the importance of early intervention and prevention strategies targeting young smokers.

Objective:To establish a mouse model of liver cancer resistant to PD-1 monoclonal antibody and analyze the changes in its metabolomics to explore the potential mechanism of drug resistance.Methods:BALB/c mice were randomly divided into control and treatment groups after being loaded with tumor,and a normal group was additionally set up.The normal and control groups were injected with saline,and the treatment group was injected with PD-1 monoclonal antibody,after which the mice in the treatment group were screened for drug resistant and response groups.Observed the drug-resistant situation,body mass,tumor growth and survival rate of mice in each group,calculate the spleen index.The pathological features of tumor tissues were observed by HE staining method.Serum metabolites were detected by non-targeted metabolomics.Finally,a bivariate Pearson correlation analysis was conducted between the differential serum metabolites and tumor size.Results:The tumor-bearing mouse model with PD-1 monoclonal antibody resistance was successfully established,and the drug resistance rate of the mice was 50％.Compared with the normal and response groups,mice in the resistant group showed an increase in body weight,a significant increase in tumor volume,a decrease in survival rate,and a significant increase in splenic index.There was less lymphocyte infiltration in the tumor tissue.Metabolomics analysis showed that the serum levels of glutamic acid and aspartic acid increased and malic acid decreased in the resistant mice compared with the response group,and these changes were closely related to the arginine biosynthesis pathway.Conclusions:The tumor-bearing mouse model with PD-1 monoclonal antibody resistance was successfully established.The changes in its peripheral serum metabolomics mainly involve arginine metabolism and the related changes of aspartate,malate and glutamate.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective Radial artery occlusion(RAO)is one of the common complications following coronary intervention via the traditional radial artery approach.This study aims to evaluate the clinical effectiveness of retrograde recanalization of occluded radial arteries through the distal transradial approach(dTRA)approach.Methods A total of 35 patients with RAO admitted to the cardiovascular department of the Anhui Chest hospital between December 2022 and April 2024,who were scheduled to undergo coronary intervention and had attempted recanalization of RAO via dTRA approach were selected.The primary result was the success rate of recanalizing RAO via dTRA.The secondary results included factors influencing the failure of recanalization via dTRA,postoperative puncture complications,and the patency rate at the 3-month follow-up.Results This study divided the patients into a successful group(29 cases,82.9％)and a failed group(6 cases,17.1％)based on whether the distal radial artery was successfully opened and occluded.The proportion of smoking(100.00％vs.17.24％,P=0.040),history of diabetes(100.00％vs.10.34％,P=0.025),and chronic total occlusion of coronary artery(83.33％vs.17.24％,P=0.030)in the failure group were higher than those in the success group,and the difference was statistically significant.The application rate of balloon tracking assisted technology in the failed group(16.67％vs.58.62％,P=0.045),and the diameter of the radial artery at 3 days after surgery[(1.63±0.13)mm vs.(2.13±0.32)mm,P=0.021]and the peak radial artery blood flow velocity at 3 days postoperatively[(0.10±0.78)m/s vs.(0.50±0.13)m/s,P＜0.001]were all lower in the successful group,and the differences were statistically significant.Logistic regression analysis 3 days after surgery showed that chronic complete occlusion of the coronary artery was an independent risk factor for surgical opening failure(OR 0.042,95％CI 0.004-0.438,P=0.008).After 3 months of follow-up,the patency rate of the successful group was 55.2％.Conclusions Retrograde recanalization of RAO via dTRA is safe and feasible,but its long-term patency rate is not high.

To explore the understanding of the target population regarding the Get Active Questionnaire for Pregnancy(GAQ-P)and the Companion Health Care Provider Consultation Form for Prenatal Physical Activity(cHCP-CF-PPA)in the Chinese context,and to verify the consistency of the Chinese version of the prenatal physical activity dual screening questionnaire with the original version in terms of language expression,27 pregnant women and 12 healthcare providers were selected from Obstetrics and Gynecology Hospital,Fudan University during Aug and Oct 2023,and were interviewed using purposive sampling.Two rounds of cognitive interviews were conducted.The first round revealed that some respondents experienced ambiguities in understanding the meanings of 5 items in the questionnaire.Following modifications,the second round indicated that the revised items were consistent in meaning with the original questionnaire.Cognitive interviews can facilitate the adaptation of the prenatal physical activity dual screening questionnaire to the Chinese cultural context,improve the understanding of the questionnaire items among the target population,and promote the localization of the screening tool.

Objective:To establish a mouse model of liver cancer resistant to PD-1 monoclonal antibody and analyze the changes in its metabolomics to explore the potential mechanism of drug resistance.Methods:BALB/c mice were randomly divided into control and treatment groups after being loaded with tumor,and a normal group was additionally set up.The normal and control groups were injected with saline,and the treatment group was injected with PD-1 monoclonal antibody,after which the mice in the treatment group were screened for drug resistant and response groups.Observed the drug-resistant situation,body mass,tumor growth and survival rate of mice in each group,calculate the spleen index.The pathological features of tumor tissues were observed by HE staining method.Serum metabolites were detected by non-targeted metabolomics.Finally,a bivariate Pearson correlation analysis was conducted between the differential serum metabolites and tumor size.Results:The tumor-bearing mouse model with PD-1 monoclonal antibody resistance was successfully established,and the drug resistance rate of the mice was 50％.Compared with the normal and response groups,mice in the resistant group showed an increase in body weight,a significant increase in tumor volume,a decrease in survival rate,and a significant increase in splenic index.There was less lymphocyte infiltration in the tumor tissue.Metabolomics analysis showed that the serum levels of glutamic acid and aspartic acid increased and malic acid decreased in the resistant mice compared with the response group,and these changes were closely related to the arginine biosynthesis pathway.Conclusions:The tumor-bearing mouse model with PD-1 monoclonal antibody resistance was successfully established.The changes in its peripheral serum metabolomics mainly involve arginine metabolism and the related changes of aspartate,malate and glutamate.

To explore the understanding of the target population regarding the Get Active Questionnaire for Pregnancy(GAQ-P)and the Companion Health Care Provider Consultation Form for Prenatal Physical Activity(cHCP-CF-PPA)in the Chinese context,and to verify the consistency of the Chinese version of the prenatal physical activity dual screening questionnaire with the original version in terms of language expression,27 pregnant women and 12 healthcare providers were selected from Obstetrics and Gynecology Hospital,Fudan University during Aug and Oct 2023,and were interviewed using purposive sampling.Two rounds of cognitive interviews were conducted.The first round revealed that some respondents experienced ambiguities in understanding the meanings of 5 items in the questionnaire.Following modifications,the second round indicated that the revised items were consistent in meaning with the original questionnaire.Cognitive interviews can facilitate the adaptation of the prenatal physical activity dual screening questionnaire to the Chinese cultural context,improve the understanding of the questionnaire items among the target population,and promote the localization of the screening tool.