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.

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.

Objective To investigate the application value of indobufen combined with clopidogrel in elderly patients with coronary heart disease after percutaneous coronary intervention(PCI)with aspirin contraindications.Methods Elderly patients with coronary heart disease with aspirin contraindications were selected as study subjects and divided into 2 groups by random number table method.The control group was given oral clopidogrel bisulfate tablet 75 mg,qd;the treatment group was additionally given oral indobufen tablet 200 mg,qd,and both groups were treated for 3 months.Cardiac function indexes,coagulation-fibrinolytic system indexes,platelet function indexes,vascular endothelial function indexes and microcirculation function indexes were compared between the two groups before and after treatment,and the incidence of MACE and adverse drug reactions were analyzed.Results In this trial,39 cases in both the treatment group and the control group were included in the statistical analysis.The total effective rate of treatment group and control group were 94.87％and 79.49％,respectively,and the total effective rate of treatment group were higher than that of control group(P＜0.05).After treatment,the left ventricular ejection fraction(LVEF)of treatment group and control group were(57.13±3.16)％and(55.65±3.01)％,and the left ventricular end-diastolic volume index(LVEDVI)were(61.29±3.46)and(63.78±3.12)mL·m-2,respectively;the cardiac index were(3.68±0.31)and(3.41±0.28)L·min-1·m-2,and the stroke output index(SVI)were(57.37±2.57)and(55.29±2.74)mL·m-2,respectively;plasminogen activator inhibitor-1(PAI-1)levels were(46.29±4.18)and(49.37±5.24)ng·mL-1;antithrombin Ⅲ(AT-Ⅲ)levels were(131.04±10.65)％and(120.95±9.73)％,respectively;tissue plasminogen activator(t-PA)levels were(0.54±0.09)and(0.46±0.10)U·mL-1;fibrinogen(FIB)levels were(3.52±0.61)and(4.03±0.59)g·L-1,respectively;PT were(15.43±0.65)and(14.92±0.57)s,respectively.Compared with control group,the above indexes in treatment group were statistically significant(all P＜0.05).In the treatment group,there were 1 case of malignant arrhythmia in the cardiovascular adverse event(MACE),and in the control group,there were 2 cases of acute myocardial infarction,3 cases of malignant arrhythmia,2 cases of target vessel revascularization,and 1 case of acute thrombus in the stent.The incidence of MACE in the treatment group and the control group were 2.56％and 20.51％,respectively;the difference were statistically significant(P＜0.05).Conclusion In elderly patients with coronary heart disease contraindicated with aspirin after PCI,indobufen combined with clopidogrel can improve the cardiac function and microcirculation function,improve coagulation and fibrinolysis function,reduce vascular endothelial function injury,and reduce the incidence of MACE.

italic>Artemisia argyi is a traditional Chinese medicine in China, which is used as medicine with its leaves. The leaves of A. argyi mainly contain flavonoids, phenolic acids, volatile oils and other compounds, and have a variety of pharmacological activities. AP2/ERF transcription factors are abundant in plants and are mainly involved in plant growth and development, abiotic stress response and secondary metabolite biosynthesis regulation. However, there are few reports on the AP2/ERF gene family and its functions in A. argyi. In this study, we systematically identified the AP2/ERF gene family in A. argyi genome, and analyzed its phylogenetic tree, protein physicochemical properties, subcellular localization, conserved motifs, promoter elements, and expression patterns. The results showed that a total of 204 AP2/ERF transcription factors were identified in A. argyi genome, encoding proteins consisting of 88-483 amino acids with a relative molecular mass of 10-52.94 kDa and a theoretical isoelectric point of 4.62-9.88. Subcellular prediction showed that the majority of AP2/ERFs were located in the nucleus, cytoplasm, and the minority of them is located in the membrane and chloroplasts. According to the Arabidopsis AP2/ERF family classification, A. argyi AP2/ERF proteins were divided into four subfamilies: Soloist, AP2, ERF (B3, B4, B5, B6), and DREB (A1, A2, A4, A5, A6), among which DREB family accounted for the largest proportion, and the same subfamily had similar conserved motifs. cis-Acting element analysis showed that AP2/ERF promoters have a large number of elements responding to light and abiotic stress. Expression pattern analysis showed that most of the genes of the AP2/ERF family were dominantly expressed mainly in roots and stems, of which 19 were dominantly expressed in leaves, 77 would be induced to be expressed by methyl jasmonate, of which 16 genes were both dominantly expressed in leaves and induced to be expressed by methyl jasmonate, and these AP2/ERF genes may be the key regulatory genes for the synthesis of active ingredients in A. argyi leaves.This study lays the foundation for the functional study of AP2/ERF family genes and their regulating roles in the active components biosynthesis in A. argyi leaves.

Lithium(Li)salts are commonly used as psychotropic medications for the treatment of major depressive disorders.However,long-term use of Li salts poses a high risk of toxicity,necessitating continuous monitoring of Li concentration in patient blood to ensure medication safety,which is crucial for clinical treatment.Laser-induced breakdown spectroscopy(LIBS),as a rapid analytical technique,has been widely applied in the elemental analysis of complex matrices in various practical scenarios.In this study,LIBS technology combined with partial least squares(PLS)was employed for quantitative analysis of Li elements in blood matrix.A total of 45 clinical blood samples were utilized,and the quantitative models for plasma and whole blood matrices were separately investigated.The number of latent variables in the PLS algorithm was optimized using a five-fold cross-validation method.Results revealed that the PLS quantitative model constructed on the basis of plasma matrix achieved a predictive determination coefficient(R2)of 0.992,a predictive root mean square error(RMSEP)of 0.204 μg/mL,and a relative standard error(RSD)of 2.14%.In contrast,for the PLS quantitative model constructed on the basis of whole blood matrix,the R2 was 0.984,the RMSEP was 0.728 μg/mL,and the RSD was 3.45%Consequently,the LIBS model constructed on the basis of plasma calibration values demonstrated superior performance in quantitative analysis of Li element in whole blood,and LIBS technology provided a new possibility for rapid assessment of blood Li levels in clinical practice,with promising prospects for application.

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.

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.

China ; History of Medicine ; Medicine/standards*

Humans ; Tricuspid Valve/surgery* ; Heart Valve Prosthesis Implantation ; Surgical Instruments ; Treatment Outcome ; Cardiac Catheterization ; Tricuspid Valve Insufficiency/surgery*