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.

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.

Objective:To study the clinical safety and validity of retrograde new endoscopic field of vision in miniature pigs.Methods:6 live miniature pigs were selected as study subjects,En-doscopic Retrograde New View(ERNV)was selected.The performance,image quality and intraoper-ative and postoperative complications were evaluated.To evaluate whether all the experimental ani-mals could complete the relevant endoscopy.Verify ERNV's operating performance,including whether the duodenoscope can enter the biliary tract smoothly,and made sure whether the injection,suction,and instrument channels were unobstructed.Choledochoscope image clarity,color resolu-tion,image deformation and distortion,accurate evaluation of lumen conditions and clear observation of mucosal surface conditions were analyzed.Whether there were operant injuries such as bleeding and perforation,as well as adverse events such as respiratory depression and cardiac arrest.The sur-vival status and adverse reactions of all pigs were observed.Results:The choledochoscope was successfully inserted into the bile duct of 6 miniature pigs.The product had good operation perfor-mance and could enter the bile duct through the duodenoscope smoothly.The injection,suction and instrument channels were relatively smooth.In addition,the endoscopic images are clear,with better color resolution,and without image deformation and distortion,which can realize accurate evaluation of the conditions in the lumen and observe the mucosal surface conditions more clearly.No bile duct stenosis or dilatation occurred in all miniature pigs,and the bile duct mucosa was smooth,without hyperemia and edema,and no abnormal thickening or bending of mucous vessels.During the exami-nation,there were no operational injuries such as bleeding and perforation,and no adverse events such as respiratory depression and cardiac arrest occurred.The vital signs of all miniature pigs tended to be stable after operation,and the survival state was good,and there were no complications such as cholangitis,bleeding and perforation.Conclusion:ERNV has good clinical safety and efficacy,ex-cellent operation performance and excellent image quality,and is worthy of clinical application.

Non-small cell lung cancer (NSCLC) is the most important histological type of lung cancer. This disease affects a large number of patients, and the prognosis of advanced patients is poor. Although great progress has been achieved for existing treatment methods, challenges still exist. Cancer is a genetic disease, and its occurrence is accompanied by substantial genomic-sequence instability. (GT/CA)_n repeat sequence is a common microsatellite sequence serving as transcriptional function-related regions, DNA-methylation modification sites, and other functional sites. Its polymorphism is closely related to the expression of EGFR, HO-1, and HIF-1α in NSCLC patients. (GT/CA)_n repeat sequence is the breakthrough point to explore the molecular mechanism of NSCLC occurrence and development, develop molecular markers for diagnosis and prognosis and epigenetics research. This paper summarizes the studies on (GT/CA)_n repeat polymorphisms in NSCLC with the aim of providing references for relevant NSCLC research.

Human mass balance study is a pivotal research in the field of clinical pharmacology, aiming at elucidating the metabolic and excretion pathways of drugs in humans. Currently, human mass balance studies predominantly employ radiolabeling techniques. Recently, both the U.S. Food and Drug Administration (FDA) and the Center for Drug Evaluation (CDE) of the China National Medical Products Administration (NMPA) issued related research drafts and guidelines to encourage and guide the pharmaceutical industry to conduct research in compliance with established standards. The selection of radiolabeling sites is crucial for obtaining critical information on drug metabolism. However, in vivo biotransformation may lead to partial disintegration of the molecular structure, thereby resulting in the loss of metabolic product information of the unlabeled moiety. Administering drugs with different radiolabeling sites separately or in combination, or labeling multiple radioactive isotopes within one molecule, can effectively solve this problem. This article reviews relevant technological progress, analyzes radiolabeling strategies, and discusses the application of drugs with multiple radiolabeling sites in human mass balance studies.

italic>Schisandra chinensis is a traditional Chinese medicine with the functions of reinforcing deficiency, strengthening, and inducing astringency, appliable to treat the chronic cough and deficiency in breath, palpitation, and insomnia, etc. A hybrid mass spectrometry scanning strategy (high-definition data-independent/data-dependent acquisition, HDDIDDA), enabling the ion mobility separation and alternating data-independent acquisition/data-dependent acquisition, was established, which, in combination with in-house library-driven automatic peak annotation workflows facilitated by the UNIFI software, was utilized to systematically characterize the multi-classes of chemical components from S. chinensis. The use of an HSS T3 column (100 mm × 2.1 mm, 1.8 μm), 0.1% formic acid in H₂O-acetonitrile as the mobile phase running at the flow rate of 0.3 mL·min^-1, and column temperature at 35 ℃, could enable good separation of the S. chinensis components within 42 min. HDDIDDA scan in both the positive and negative ion modes was employed for data acquisition. Based on the automatic peak annotation, reference standards comparison, MS² data interpretation, and literature analysis, we were able to identify or tentatively characterize 105 compounds in the S. chinensis decoction, involving 56 terpenoids, 42 lignans, five glycosides, one organic acid, and one flavonoid. HDDIDDA scanning can improve the coverage of data acquisition and improve the accuracy of identification, while CCS prediction analysis provides the possibility to distinguish isomers by the ion mobility technology. The results provide reference for the intelligent material basis research of TCM.

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.

Purpose/Significance To improve the classification and evaluation mode of medical safety incidents,and to improve work efficiency and timeliness.Method/Process The data of previous medical safety incidents are pre-processed,BERT model is used for training,testing and iterative optimization,and an intelligent classification and prediction model for medical safety incidents is built.Re-sult/Conclusion The model is used to classify 466 medical safety incidents reported by clinical departments from January to November 2022,and F1 value reaches 0.66.The application of BERT model in the classification and evaluation of medical safety incidents can im-prove work efficiency and timeliness,and help timely intervene in medical safety risks.

Objective To explore the efficacy and safety of recombinant human anti-severe acute respiratory syn-drome coronavirus 2(anti-SARS-CoV-2)monoclonal antibody injection(F61 injection)in the treatment of patients with coronavirus disease 2019(COVID-19)combined with renal damage.Methods Patients with COVID-19 and renal damage who visited the PLA General Hospital from January to February 2023 were selected.Subjects were randomly divided into two groups.Control group was treated with conventional anti-COVID-19 therapy,while trial group was treated with conventional anti-COVID-19 therapy combined with F61 injection.A 15-day follow-up was conducted after drug administration.Clinical symptoms,laboratory tests,electrocardiogram,and chest CT of pa-tients were performed to analyze the efficacy and safety of F61 injection.Results Twelve subjects(7 in trial group and 5 in control group)were included in study.Neither group had any clinical progression or death cases.The ave-rage time for negative conversion of nucleic acid of SARS-CoV-2 in control group and trial group were 3.2 days and 1.57 days(P=0.046),respectively.The scores of COVID-19 related target symptom in the trial group on the 3rd and 5th day after medication were both lower than those of the control group(both P＜0.05).According to the clinical staging and World Health Organization 10-point graded disease progression scale,both groups of subjects improved but didn't show statistical differences(P＞0.05).For safety,trial group didn't present any infusion-re-lated adverse event.Subjects in both groups demonstrated varying degrees of elevated blood glucose,elevated urine glucose,elevated urobilinogen,positive urine casts,and cardiac arrhythmia,but the differences were not statistica-lly significant(all P＞0.05).Conclusion F61 injection has initially demonstrated safety and clinical benefit in trea-ting patients with COVID-19 combined with renal damage.As the domestically produced drug,it has good clinical accessibility and may provide more options for clinical practice.