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.

In view of the problem of slow development of intensive care medicine in Xizang, the research team made full use of the national partner assistance to Xizang, gathered resources across all cities in Xizang, and formed a national academic platform for critical care medicine in plateau areas. Adhering to the academic orientation with hemodynamics as the main topic, critical care ultrasound as the bedside dynamic monitoring and evaluation method, and blood flow-oxygen flow resuscitation as the core connotation, we have achieved the goals of improving the critical care talent echelon throughout Xizang, driving the overall progress of intensive care medicine in Xizang, making a figure in China, and focusing on training of top-notch talents.

The effect of porcine SIRT5 on replication of foot and mouth disease virus type O(FMDV-O)and the underlying regulatory mechanism were investigated.Western blot and RT-qPCR analyses were employed to monitor expression of endoge-nous SIRT5 in PK-15 cells infected with FMDV-O.Three pairs of SIRT5-specific siRNAs were synthesized.Changes to SIRT5 and FMDV-O protein and transcript levels,in addition to virus copy numbers,were measured by western blot and RT-qPCR analyses.PK-15 cells were transfected with a eukaryotic SIRT5 expression plasmid.Western blot and RT-qPCR analyses were used to explore the impact of SIRT5 overexpression on FMDV-O replication.Meanwhile,RT-qPCR analysis was used to detect the effect of SIRT5 overexpression on the mRNA expression levels of type I interferon-stimulated genes induced by SeV and FMDV-O.The results showed that expression of SIRT5 was up-regulated in PK-15 cells infected with FMDV-O and siRNA interfered with SIRT5 to inhibit FMDV-O replication.SIRT5 overexpression promoted FMDV-O replication.SIRT5 over-expression decreased mRNA expression levels of interferon-stimulated genes induced by SeV and FMDV-O.These results suggest that FMDV-O infection stimulated expression of SIRT5 in PK-15 cells,while SIRT5 promoted FMDV-O rep-lication by inhibiting production of type I interferon-stimula-ted genes.These findings provide a reference to further ex-plore the mechanism underlying the ability of porcine SIRT5 to promote FMDV-O replication.

Objective:To explore the effects and mechanisms of chidamide on the osteogenic differentiation of bone marrow mesenchymal stromal cells(MSC)from myelodysplastic syndromes(MDS).Methods:MSC were isolated and cultured from bone marrow of MDS patients and healthy donors.CCK-8 assay was used to detect the effects of chidamide on the proliferation of MSC.The effects of chidamide on the activity of histone deacetylase(HD AC)in MSC was measured by a fluorescence assay kit and Western blot.Alkaline phosphatase(ALP)activity was detected on day 3 and calcium nodule formation was observed by Alizarin Red staining on day 21 after osteogenic differentiation.The expression of early and late osteogenic genes was detected on day 7 and day 21,respectively.RT-PCR and Western blot were used to detect the effects of chidamide on mRNA and protein expression of RUNX2 which is the key transcription factor during osteogenesis.Results:As the concentration of chidamide increased,the proliferation of MSC was inhibited.However,at a low concentration(1 μmol/L),chidamide had no significant inhibitory effect on MSC proliferation but significantly inhibited HD AC activity.In MSC from both MDS patients and healthy donors,chidamide(1 μmol/L)significantly increased ALP activity,calcium nodule formation,thereby mRNA expression of osteogenic genes,and restored the reduced osteogenic differentiation ability of MDS-MSC compared to normal MSC.Mechanistic studies showed that the osteogenic-promoting effect of chidamide may be related to the upregulation of RUNX2.Conclusion:Chidamide can inhibit HD AC activity in MSC,upregulate the expression of the osteogenic transcription factor RUNX2,and promote the osteogenic differentiation of MDS-MSC.

Objective To develop a multicolor photoelectroencephalography evoked flash to identify photosensitive epilepsy patients during the selection of naval aircraft pilots.Methods The multicolor photoelectroencephalography evoked flash was composed of a main body,a control box and a bracket.There were four rows of LED lights in the main body,which emitted four colors of light including red,yellow,green and orange,respectively;there were three sockets for signal,light and power and one color changeover switch on the body of the control box,and a control circuit board was fixed at the bottom inside the control box;the bracket had a double-jointed arm folding structure.The flash developed was compared with the coventional photoelectroencephalography evoked flash to verify its effect for inducing photosensitive epilepsy.Results There were no significant differences between the two flashes in the numbers of identified cases with photosensitive epilepsy when the subjects were under awake and closed-eye conditions(P＞0.05).Condusion The flash developed can make up for the deficiency of the coventional photoelectroencephalography evoked flash when selecting naval aircraft pilots.[Chinese Medical Equipment Journal,2024,45(7):112-114]

Objective To develop a GIS-based 3D playback system for the flight human-plane data to realize the fusion of pilots'airborne flight data and physiological data.Methods The 3D playback system was developed with the Browser/Server(B/S)architecture,micro-server model,Java language and Spring Cloud technology framework,which was composed of three functional modules for flight process reproduction,physiological situational awareness and critical event calibration analysis.Results The system developed achieved time synchronization and data fusion of airborne flight data and physiological data with a time synchronization frequency of 1 Hz and a refresh rate of not less than 120 frames/s.Conclusion The system developed with high safety,stability,reliability and accuracy facilitates pilot in-flight physiological monitoring and fusion and simultaneous display of airborne flight data and physiological data,which can be used as an important platform for decision-making support in flight training.[Chinese Medical Equipment Journal,2024,45(10):14-19]

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Based on WANG Xugao's “thirty methods of treating the liver”， it is believed that the occurrence and development of childhood tic disorders follow the dynamic progression from liver qi disease to liver fire disease and then liver wind disease. The basic pathogenesis of three stages are characterized by binding constraint of liver qi， liver fire hyperactivity， and internal stirring of liver wind. Moreover， liver-blood deficiency and stagnation， and malnutrition of liver yin as the main point in terms of the imbalance of liver qi， blood， yin， and yang should be considered， as well as the imbalance relationship of the five zang organs such as the involvement of other organs and the gradually reach of the other organs. Guided by the principles of “thirty methods of treating the liver”， the treatment of tic disorders in liver qi stage should focus on soothing the liver and rectifying qi， soothing the liver and unblocking the collaterals， using Xiaochaihu Decoction （小柴胡汤） and Sini Powder （四逆散）. The treatment of tic disorders in liver fire stage involves clearing， draining and resolving liver heat， using Longdan Xiegan Decoction （龙胆泻肝汤）， Xieqing Pill （泻青丸）， Danggui Longhui Pill （当归龙荟丸）， and Huagan Decoction （化肝煎）. The treatment of tic disorders in liver wind stage involves extinguishing wind and subduing yang， using Lingjiao Gouteng Decoction （羚角钩藤汤） and Liuwei Dihuang Pill （六味地黄丸）. Throughout the treatment process， attention should be paid to harmonizing the liver's qi， blood， yin， and yang， as well as addressing the pathology of other organs.

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.