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.

A method for determining volatile organic compounds(VOCs)in environmental air samples using thermal desorption-gas chromatography/mass spectrometry(TD-GC/MS)was developed.The qualitative and quantitative analyses of 103 kinds of VOC species were achieved under the optimal conditions such as cold trap desorption temperature and time during the thermal desorption process,combined with full-scan data acquisition using an electron impact ionization source.With a sampling volume of 3.0 L,the method exhibited detection limits of 0.1-0.5 μg/m3 and quantitation limits of 0.4-2.0 μg/m3.At spiked concentrations of 1.0 μg/m3 and 10.0 μg/m3,the recoveries ranged from 60.5%to 118.0%,with relative standard deviations varying from 2.37%to 18.70%.Furthermore,all target compounds showed correlation coefficients(R2)exceeding 0.997 across their respective concentration ranges,demonstrating that the method had high accuracy and reliability.Using this method,a study was conducted to investigate the spatial variations and source apportionment of VOCs across urban,suburban,and rural sites in Anyang,a representative industrial city,during the summer season.The results revealed significant differences in VOC concentrations among the three regions.The urban site recorded the highest concentration at 40.1 μg/m3,followed by the rural site at 23.5 μg/m3,while the suburban site had the lowest concentration of 9.74 μg/m3.With regard to compositional characteristics,alkanes were the dominant components in the urban and rural areas,whereas oxygenated VOCs were predominant in the suburban site.The ozone formation potential(OFP)also varied significantly across regions:96.0 μg/m3 in urban areas,72.0 μg/m3 in rural areas,and only 27.6 μg/m3 in suburban areas.Alkenes were identified as the primary contributors to the total ozone formation potential(TOFP)in all regions,highlighting their critical role in atmospheric oxidation processes.Source apportionment analysis using the positive matrix factorization(PMF)model identified combustion sources,natural sources,chemical industry emissions,industrial emissions,solvent use,and vehicle emissions as the major sources of VOCs in Anyang during summer.Notably,chemical industry emissions and combustion sources were dominant in urban and rural areas,whereas combustion sources and natural sources were more prominent in the suburban area,reflecting distinct emission patterns and anthropogenic activities across the regions.

OBJECTIVE: To explore the improvement effect of rehabilitation nursing based on information-knowledge-belief-behavior (IKAP) theory on urinary incontinence patients after radical prostatectomy. METHODS Sixty-six patients with urinary incontinence who received robot-assisted laparoscopic radical prostatectomy in General Hospital of Eastern Theater Command from January 2021 to January 2023 were selected and divided into control group (n=33) and observation group (n=33) according to random number table method. The patients in the control group were treated with rehabilitation nursing. The patients in the observation group were treated with rehabilitation nursing guided by IKAP theory. The recovery of urinary incontinence, duration of urinary incontinence, subjective well-being, quality of life, psychological and emotional indexes of patients in the two groups were compared. Results： The total effective rate of urinary incontinence recovery in the observation group was significantly higher than that in the control group (90.91% vs 60.61%，P<0.05). The duration of urinary incontinence in the observation group was significantly shorter than that in the control group (［3.36±1.54］d vs ［4.15±1.36］d，P<0.05). And the subjective well-being score in observation group was significantly higher than that in the control group (［19.36±2.69］points vs ［11.65±2.65］points， P<0.05). There was no significant difference in preoperative physical function, social function，and mental health scores between the two groups (P>0.05). And all scores in the observation group were significantly higher than those in the control group after surgery (P<0.05). There was no significant difference in the preoperative SAS and SDS scores between the two groups of patients (P>0.05). And the scores of SAS and SDS in observation group were lower than those of the control group after the operation (P<0.05). Conclusion： Rehabilitation nursing based on IKAP theory can significantly improve urinary incontinence in patients with prostate cancer after surgery, which promotes the recovery of urinary incontinence, shortens the time of urinary incontinence, and improves the subjective well-being and quality of life, as well as reduces the negative impact of negative emotions. Therefore, it can be widely promoted and implemented in clinical practice.
Humans ; Prostatectomy/adverse effects* ; Urinary Incontinence/etiology* ; Male ; Quality of Life ; Rehabilitation Nursing ; Middle Aged ; Aged

OBJECTIVE: To compare the therapeutic efficacy of portal and tail vein transplantation of bone marrow-derived mesenchymal stem cells (BMSCs) against cholestatic liver fibrosis in mice. METHODS: BMSCs were isolated and co-cultured with starvation-activated hepatic stellate cells (HSCs). HSC activation markers were identified using immunofluorescence and qRT-PCR. BMSCs were injected into the liver tissues of bile duct ligation (BDL) mice via the tail and portal veins. Histomorphology, liver function, inflammatory cytokines, and the expression of key proteins were all determined in the liver tissues. RESULTS: BMSCs inhibited HSC activation by reducing α-SMA and collagen I expression. Compared to tail vein injection, DIL-labeled BMSCs injected through the portal vein maintained a high homing rate in the liver. Moreover, BMSCs transplanted through the portal vein resulted in greater improvement in liver color, hardness, and gallbladder size than did those transplanted through the tail vein. Furthermore, BMSCs injected by portal vein, but not tail vein, markedly ameliorated liver function, reduced the secretion of inflammatory cytokines, including TNF-α, IL-6, and IL-1β, and decreased α-SMA + hepatic stellate cell (HSC) activation and collagen fiber formation. CONCLUSION The therapeutic effect of BMSCs on cholestatic liver fibrosis in mice via portal vein transplantation was superior to that of tail vein transplantation. This comparative study provides reference information for further BMSC studies focused on clinical cholestatic liver diseases.
Animals ; Mice ; Mesenchymal Stem Cell Transplantation ; Liver Cirrhosis/etiology* ; Male ; Cholestasis/therapy* ; Mice, Inbred C57BL ; Hepatic Stellate Cells ; Mesenchymal Stem Cells

The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine and intelligent care. However, the artificial intelligent medical device sector still faces significant challenges, such as data privacy protection and algorithm reliability. This study, based on invention patent analysis, revealed the technological innovation trends in the field of artificial intelligent medical devices from aspects such as patent application time trends, hot topics, regional distribution, and innovation players. The results showed that global invention patent applications had remained active, with technological innovations primarily focused on medical image processing, physiological signal processing, surgical robots, brain-computer interfaces, and intelligent physiological parameter monitoring technologies. The United States and China led the world in the number of invention patent applications. Major international medical device giants, such as Philips, Siemens, General Electric, and Medtronic, were at the forefront of global technological innovation, with significant advantages in patent application volumes and international market presence. Chinese universities and research institutes, such as Zhejiang University, Tianjin University, and the Shenzhen Institute of Advanced Technology, had demonstrated notable technological innovation, with a relatively high number of patent applications. However, their overseas market expansion remained limited. This study provides a comprehensive overview of the technological innovation trends in the artificial intelligent medical device field and offers valuable information support for industry development from an informatics perspective.
Artificial Intelligence ; Patents as Topic ; Humans ; Inventions ; China ; Brain-Computer Interfaces ; Telemedicine ; Equipment and Supplies ; Robotics ; Algorithms

The rapid development of artificial intelligence technology is driving profound changes in medical practice, particularly in the field of medical device application. Based on data from the U.S. clinical trials registry, this study analyzes the global registration landscape of clinical trials involving artificial intelligence-based medical devices, aiming to provide a reference for their clinical research and application. A total of 2 494 clinical trials related to artificial intelligence medical devices have been registered worldwide, with participation from 66 countries or regions. The United States leads with 908 trials, while for other countries or regions, including China, each has fewer than 300 trials. Germany, the United States, and Belgium serve as central hubs for international collaboration. Among the sponsors, 63.96% are universities or hospitals, 22.36% are enterprises, and the remainder includes individuals, government agencies and others. Of all trials, 79.99% are interventional studies, 94.67% place no restrictions on participant gender, and 69.69% exclude children. The targeted diseases are primarily neurological and mental disorders. This study systematically reveals the global distribution characteristics and research trends of artificial intelligence medical device clinical trials, offering valuable data support and practical insights for advancing international collaboration, resource allocation, and policy development in this field.
Artificial Intelligence ; Humans ; Clinical Trials as Topic/statistics & numerical data* ; Equipment and Supplies ; Registries ; United States

Objective To compare the effect of different intensity of neuromuscular training(NMT)on muscle strength and knee joint function of patients after anterior cruciate ligament reconstruction(ACLR).Methods From January,2023 to January,2024,60 ACLR patients in Changhai Hospital were selected,and they received the same intensity of NMT from one to eight weeks after surgery.Eight weeks after surgery,they were randomly divided into low intensity group(n=30)and high intensity group(n=30),and then they received different inten-sities of NMT from nine to 16 weeks after surgery,each training session lasted one hour,with three sessions per week,totaly 48 sessions.The Lysholm score,knee flexor and extensor muscle strength and muscle endurance-were compared at eight weeks and 16 weeks after surgery.Results After group training,the Lysholm score significantly increased in both groups(|t|>13.739,P<0.001),and was higher in the high intensity group than in the low intensity group(t=-2.574,P<0.05);in the high intensity group,the relative peak torque and endurance of the extensor and flexor muscles improved at angular velocities of 60°/s,120°/s and 180 °/s(|t|>2.320,P<0.05);in the low intensity group,the flexor peak torque improved at all the three angular velocities(t>2.177,P<0.05),the extensor peak torque improved at angular velocities of 60°/s and 180°/s(|t|>1.715,P<0.05),and the extensor endurance improved at angular velocity of 60°/s(t=-2.293,P<0.05).However,there was no significant difference in the relative peak torque and endurance of the extensor and flexor muscles at all the three angular velocities(P>0.05).Conclusion Both high and low intensity NMT could improve the muscle strength,muscle endurance and knee joint func-tion.Maybe,high intensity is superior to low intensity.Further verification is still needed.

Background Workers in the cable manufacturing industry are exposed to high-speed machinery and equipment for a long time, coupled with heavy workload, which poses significant risks to their physical health. However, the issue of occupational injuries in this industry has not received enough attention yet. Objective To understand the incidence of occupational injury of workers in cable manufacturing industry and to analyze the influencing factors. Method A basic information questionnaire and an occupational injury questionnaire were developed to investigate the occupational injuries of 1 343 workers in a cable manufacturing enterprise in the past year, and a total of 1 225 valid questionnaires were recovered, with an effective rate of 91.2%. Descriptive statistics were used to characterize the causes, injury locations, injury types, and other characteristics of employees’ occupational injuries. Chi-square test was used to analyze the occupational injury status of groups with different demographic characteristics, occupational characteristics, lifestyles, and interpersonal relationships. Logistic regression was used to analyze the influencing factors of occupational injuries. Result The incidence of occupational injuries among workers in a cable manufacturing enterprise in the past year was 8.6%, which mainly happened in male workers (80.0%) and occurred from May to July in summer (45.7%). The main causes were mechanical injuries (32.4%) and object blows (27.6%). The main sources of damage were machinery and equipment (36.2%) as well as raw materials and products (15.2%). The main injuries were located in upper limbs (53.3%) and lower limbs (22.9%). The main types of injuries were fractures (33.3%) and abrasions/contusions/puncture wounds (19.0%). The results of univariate analysis showed that there were statistically significant variations in the incidence of occupational injuries by gender, overtime, pre-job training, years of service in current position, alcohol consumption, physical exercise per week, and co-worker relationship (P<0.05). The logistic regression model showed that workers who exercised less than twice a week, did not participate in pre-job training, worked overtime, and had fair/poor/very poor colleague relationship had a higher risk of occupational injury, while women had a lower risk of occupational injury. Conclusion The distribution of occupational injury population is mainly male, and the time distribution is mainly from May to July. Gender, physical exercise, pre-job training, overtime, and colleague relationship are the influencing factors of occupational injuries. We should strengthen pre-job training, arrange work hours reasonably, and create a good working atmosphere to reduce the occurrence of occupational injuries.

Background Social psychological factors have emerged as a key area of research in occupational injury prevention. Occupational stress, a significant component of social psychology, has garnered widespread attention due to its potential impact on occupational injury. Objective To analyze the factors influencing occupational stress among cable manufacturing workers and explore the relationship between occupational stress and occupational injury, and to provide scientific evidence for reducing occupational stress and injury. Methods A questionnaire on basic demographics, occupational injury, and occupational stress (Effort-Reward Imbalance, ERI) was used to investigate 1343 workers in a cable enterprise. Chi-square test was employed to analyze the distribution of occupational stress across different worker characteristics. Logistic regression was used to identify factors influencing occupational stress, and a decision tree model was applied to assess the impact of occupational stress on injury risk. Results A total of 1225 valid questionnaires were collected, yielding a response rate of 91.2%. The positive rate of occupational stress was 28.7%, while the incidence of occupational injury in the past year was 8.6%. The results of univariate analysis revealed that workers who had experienced occupational injuries had a higher positive rate of occupational stress compared to those without injuries (P<0.05), and there were significant differences in the distribution of occupational stress by genders, overtime work, shift work, daily working hours, fatigue levels, sleep duration, family relationships, and colleague relationships (P<0.05). The logistic regression results showed that individuals who worked shifts, had fair/poor/very colleague relationships, and frequently worked overtime had a higher risk of occupational stress (P<0.05). The final decision tree model confirmed that occupational stress was a risk factor for occupational injury. Additional factors influencing occupational injury included physical exercise, colleague relationships, pre-job training participation, and gender. Workers who exercised less than twice per week and experienced occupational stress had a significantly higher risk of injury (23.6%) compared to those without occupational stress (10.9%). Conclusion Shift work, colleague relationships, and overtime work are correlated with the positive rate of occupational stress among cable manufacturing workers. Implementing a reasonable work schedule and fostering a positive work environment are recommended. Reducing occupational stress may help lower the risk of occupational injuries in this industry.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.