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 This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

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.

China has become the country with the highest global burden of heart failure(HF).Despite the widespread use of prognostic-improving medications today,the mortality rate of HF remains high,reaching 13.7％at one year-particularly among patients with heart failure with reduced ejection fraction(HFrEF).HF interventional device therapy(structural intervention)targets the structural factors underlying HF,including atrial pressure,ventricular remodeling,and valvular intervention.It leverages the heart's intrinsic physiological properties and pathological progression mechanisms to deliver treatments through interventions without external active forces,achieving anatomical or functional repair.This field has emerged as a rapidly growing area and plays an increasingly critical role in HF management.This article provides a comprehensive review and summary of the latest advancements in HF and cardiomyopathy interventional therapy over the past year.It covers various novel technologies and products currently in the research phase,aiming to provide an in-depth analysis of the current status and future directions of HF interventional therapy,and further advance the development of this discipline.

Objective To design a shared appointment pool system to realize shared appointment resources for on-line and off-line ways based on data synchronization and information sharing.Methods The system was designed with Internet Plus on-line and off-line intelligent medical appointment platform,which used Oracle 11g database for data storage and the front-end server and data center server for data exchange.PowerBuilder language and Java language were used for the development of the system,and there were five functional modules included in the system for appointment resource definition,appointment resource generation,appointment resource distribution,appointment list adjustment and outpatient consultation arrangement.Results The system developed contributed to unified managment of on-line and off-line appointment resources,and could be used for tracing,summarization and analysis of appointment resources.Conclusion The system developed realizes the synch-ronization of multi-way consultation data and the maximum sharing of appointment resources under the background of smart healthcare,which is of conducive for improving the utilization rate of medical resources.[Chinese Medical Equipment Journal,2025,46(3):42-47]

Objective To design a shared appointment pool system to realize shared appointment resources for on-line and off-line ways based on data synchronization and information sharing.Methods The system was designed with Internet Plus on-line and off-line intelligent medical appointment platform,which used Oracle 11g database for data storage and the front-end server and data center server for data exchange.PowerBuilder language and Java language were used for the development of the system,and there were five functional modules included in the system for appointment resource definition,appointment resource generation,appointment resource distribution,appointment list adjustment and outpatient consultation arrangement.Results The system developed contributed to unified managment of on-line and off-line appointment resources,and could be used for tracing,summarization and analysis of appointment resources.Conclusion The system developed realizes the synch-ronization of multi-way consultation data and the maximum sharing of appointment resources under the background of smart healthcare,which is of conducive for improving the utilization rate of medical resources.[Chinese Medical Equipment Journal,2025,46(3):42-47]

China has become the country with the highest global burden of heart failure(HF).Despite the widespread use of prognostic-improving medications today,the mortality rate of HF remains high,reaching 13.7％at one year-particularly among patients with heart failure with reduced ejection fraction(HFrEF).HF interventional device therapy(structural intervention)targets the structural factors underlying HF,including atrial pressure,ventricular remodeling,and valvular intervention.It leverages the heart's intrinsic physiological properties and pathological progression mechanisms to deliver treatments through interventions without external active forces,achieving anatomical or functional repair.This field has emerged as a rapidly growing area and plays an increasingly critical role in HF management.This article provides a comprehensive review and summary of the latest advancements in HF and cardiomyopathy interventional therapy over the past year.It covers various novel technologies and products currently in the research phase,aiming to provide an in-depth analysis of the current status and future directions of HF interventional therapy,and further advance the development of this discipline.

People frequently struggle to juggle their work, family, and social life in today’s fast-paced environment, which can leave them exhausted and worn out. The development of technologies for detecting fatigue while driving is an important field of research since driving when fatigued poses concerns to road safety. In order to throw light on the most recent advancements in this field of research, this paper provides an extensive review of fatigue driving detection approaches based on electroencephalography (EEG) data. The process of fatigue driving detection based on EEG signals encompasses signal acquisition, preprocessing, feature extraction, and classification. Each step plays a crucial role in accurately identifying driver fatigue. In this review, we delve into the signal acquisition techniques, including the use of portable EEG devices worn on the scalp that capture brain signals in real-time. Preprocessing techniques, such as artifact removal, filtering, and segmentation, are explored to ensure that the extracted EEG signals are of high quality and suitable for subsequent analysis. A crucial stage in the fatigue driving detection process is feature extraction, which entails taking pertinent data out of the EEG signals and using it to distinguish between tired and non-fatigued states. We give a thorough rundown of several feature extraction techniques, such as topology features, frequency-domain analysis, and time-domain analysis. Techniques for frequency-domain analysis, such wavelet transform and power spectral density, allow the identification of particular frequency bands linked to weariness. Temporal patterns in the EEG signals are captured by time-domain features such autoregressive modeling and statistical moments. Furthermore, topological characteristics like brain area connection and synchronization provide light on how the brain’s functional network alters with weariness. Furthermore, the review includes an analysis of different classifiers used in fatigue driving detection, such as support vector machine (SVM), artificial neural network (ANN), and Bayesian classifier. We discuss the advantages and limitations of each classifier, along with their applications in EEG-based fatigue driving detection. Evaluation metrics and performance assessment are crucial aspects of any detection system. We discuss the commonly used evaluation criteria, including accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. Comparative analyses of existing models are conducted, highlighting their strengths and weaknesses. Additionally, we emphasize the need for a standardized data marking protocol and an increased number of test subjects to enhance the robustness and generalizability of fatigue driving detection models. The review also discusses the challenges and potential solutions in EEG-based fatigue driving detection. These challenges include variability in EEG signals across individuals, environmental factors, and the influence of different driving scenarios. To address these challenges, we propose solutions such as personalized models, multi-modal data fusion, and real-time implementation strategies. In conclusion, this comprehensive review provides an extensive overview of the current state of fatigue driving detection based on EEG signals. It covers various aspects, including signal acquisition, preprocessing, feature extraction, classification, performance evaluation, and challenges. The review aims to serve as a valuable resource for researchers, engineers, and practitioners in the field of driving safety, facilitating further advancements in fatigue detection technologies and ultimately enhancing road safety.