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 age-related changes of the mandibular third molar root pulp visibility in individuals in East China,and to explore the feasibility of applying this method to deter-mine whether an individual is 18 years or older.Methods A total of 1 280 oral panoramic images were collected from the 15-30 years old East China population,and the mandibular third molar root pulp visibility in all oral panoramic images was evaluated using OLZE 0-3 four-stage method,and the age distribution of the samples at each stage was analyzed using descriptive statistics.Results Stages 0,1,2 and 3 first appeared in 16.88,19.18,21.91 and 25.44 years for males and in 17.47,20.91,22.01 and 26.01 years for females.In all samples,individuals at stages 1 to 3 were over 18 years old.Conclusion It is feasible to determine whether an individual in East China is 18 years or older based on the mandibular third molar root pulp visibility on oral panoramic images.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.

OBJECTIVES: To study the clinical features of children with autoimmune encephalitis (AE) secondary to epidemic encephalitis B (EEB). METHODS: A retrospective analysis was performed on the medical data of five children with EEB with "bipolar course" who were treated in Children's Hospital Affiliated to Zhengzhou University from January 2020 to June 2022. RESULTS: Among the five children, there were three boys and two girls, with a median age of onset of 7 years (range 3 years 9 months to 12 years) and a median time of 32 (range 25-37) days from the onset of EEB to the appearance of AE symptoms. The main symptoms in the AE stage included dyskinesia (5/5), low-grade fever (4/5), mental and behavioral disorders (4/5), convulsion (2/5), severe disturbance of consciousness (2/5), and limb weakness (1/5). Compared with the results of cranial MRI in the acute phase of EEB, the lesions were enlarged in 3 children and unchanged in 2 children showed on cranial MRI in the AE stage. In the AE stage, four children were positive for anti-N-methyl-D-aspartate receptor antibody (one was also positive for anti-γ-aminobutyric acid type B receptor antibody), and one was negative for all AE antibodies. All five children in the AE stage responded to immunotherapy and were followed up for 3 months, among whom one almost recovered and four still had neurological dysfunction. CONCLUSIONS EEB can induce AE, with anti-N-methyl-D-aspartate receptor encephalitis as the most common disease. The symptoms in the AE stage are similar to those of classical anti-N-methyl-D-aspartate receptor encephalitis. Immunotherapy is effective for children with AE secondary to EEB, and the prognosis might be related to neurological dysfunction in the acute phase of EEB.
Male ; Female ; Humans ; Child ; Infant, Newborn ; Anti-N-Methyl-D-Aspartate Receptor Encephalitis ; Retrospective Studies ; Hashimoto Disease/therapy* ; Encephalitis, Arbovirus

Objectives: To investigated the safety and efficacy of treating patients with acute non-ST-segment elevation myocardial infarction (NSTEMI) and elevated levels of N-terminal pro-hormone B-type natriuretic peptide (NT-proBNP) with levosimendan within 24 hours of first medical contact (FMC). Methods: This multicenter, open-label, block-randomized controlled trial (NCT03189901) investigated the safety and efficacy of levosimendan as an early management strategy of acute heart failure (EMS-AHF) for patients with NSTEMI and high NT-proBNP levels. This study included 255 patients with NSTEMI and elevated NT-proBNP levels, including 142 males and 113 females with a median age of 65 (58-70) years, and were admitted in the emergency or outpatient departments at 14 medical centers in China between October 2017 and October 2021. The patients were randomly divided into a levosimendan group (n=129) and a control group (n=126). The primary outcome measure was NT-proBNP levels on day 3 of treatment and changes in the NT-proBNP levels from baseline on day 5 after randomization. The secondary outcome measures included the proportion of patients with more than 30% reduction in NT-proBNP levels from baseline, major adverse cardiovascular events (MACE) during hospitalization and at 6 months after hospitalization, safety during the treatment, and health economics indices. The measurement data parameters between groups were compared using the t-test or the non-parametric test. The count data parameters were compared between groups using the χ² test. Results: On day 3, the NT-proBNP levels in the levosimendan group were lower than the control group but were statistically insignificant [866 (455, 1 960) vs. 1 118 (459, 2 417) ng/L, Z=-1.25,P=0.21]. However, on day 5, changes in the NT-proBNP levels from baseline in the levosimendan group were significantly higher than the control group [67.6% (33.8%,82.5%)vs.54.8% (7.3%,77.9%), Z=-2.14, P=0.03]. There were no significant differences in the proportion of patients with more than 30% reduction in the NT-proBNP levels on day 5 between the levosimendan and the control groups [77.5% (100/129) vs. 69.0% (87/126), χ²=2.34, P=0.13]. Furthermore, incidences of MACE did not show any significant differences between the two groups during hospitalization [4.7% (6/129) vs. 7.1% (9/126), χ²=0.72, P=0.40] and at 6 months [14.7% (19/129) vs. 12.7% (16/126), χ²=0.22, P=0.64]. Four cardiac deaths were reported in the control group during hospitalization [0 (0/129) vs. 3.2% (4/126), P=0.06]. However, 6-month survival rates were comparable between the two groups (log-rank test, P=0.18). Moreover, adverse events or serious adverse events such as shock, ventricular fibrillation, and ventricular tachycardia were not reported in both the groups during levosimendan treatment (days 0-1). The total cost of hospitalization [34 591.00(15 527.46,59 324.80) vs. 37 144.65(16 066.90,63 919.00)yuan, Z=-0.26, P=0.80] and the total length of hospitalization [9 (8, 12) vs. 10 (7, 13) days, Z=0.72, P=0.72] were lower for patients in the levosimendan group compared to those in the control group, but did not show statistically significant differences. Conclusions: Early administration of levosimendan reduced NT-proBNP levels in NSTEMI patients with elevated NT-proBNP and did not increase the total cost and length of hospitalization, but did not significantly improve MACE during hospitalization or at 6 months.
Male ; Female ; Humans ; Aged ; Natriuretic Peptide, Brain ; Simendan/therapeutic use* ; Non-ST Elevated Myocardial Infarction ; Heart Failure/drug therapy* ; Peptide Fragments ; Arrhythmias, Cardiac ; Biomarkers ; Prognosis

Objective: To analyze the efficacy and safety of letermovir in primary prophylaxis of cytomegalovirus (CMV) reactivation in patients receiving haploidentical hematopoietic stem cell transplantation. Methods: This retrospective, cohort study was conducted using data of patients who underwent haploidentical transplantation at Peking University Institute of Hematology and received letermovir for primary prophylaxis between May 1, 2022 and August 30, 2022. The inclusion criteria of the letermovir group were as follows: letermovir initiation within 30 days after transplantation and continuation for≥90 days after transplantation. Patients who underwent haploidentical transplantation within the same time period but did not receive letermovir prophylaxis were selected in a 1∶4 ratio as controls. The main outcomes were the incidence of CMV infection and CMV disease after transplantation as well as the possible effects of letermovir on acute graft versus host disease (aGVHD), non-relapse mortality (NRM), and bone marrow suppression. Categorical variables were analyzed by chi-square test, and continuous variables were analyzed by Mann-Whitney U test. The Kaplan-Meier method was used for evaluating incidence differences. Results: Seventeen patients were included in the letermovir prophylaxis group. The median patient age in the letermovir group was significantly greater than that in the control group (43 yr vs. 15 yr; Z=-4.28, P<0.001). The two groups showed no significant difference in sex distribution and primary diseases, etc. (all P>0.05). The proportion of CMV-seronegative donors was significantly higher in the letermovir prophylaxis group in comparison with the control group (8/17 vs. 0/68, χ²=35.32, P<0.001). Three out of the 17 patients in the letermovir group experienced CMV reactivation, which was significantly lower than the incidence of CMV reactivation in the control group (3/17 vs. 40/68, χ²=9.23, P=0.002), and no CMV disease development observed in the letermovir group. Letermovir showed no significant effects on platelet engraftment (P=0.105), aGVHD (P=0.348), and 100-day NRM (P=0.474). Conclusions: Preliminary data suggest that letermovir may effectively reduce the incidence of CMV infection after haploidentical transplantation without influencing aGVHD, NRM, and bone marrow suppression. Prospective randomized controlled studies are required to further verify these findings.
Humans ; Cytomegalovirus ; Retrospective Studies ; Cohort Studies ; Prospective Studies ; Cytomegalovirus Infections/prevention & control* ; Hematopoietic Stem Cell Transplantation/adverse effects* ; Graft vs Host Disease/prevention & control* ; Recurrence ; Antiviral Agents/therapeutic use*

Objective: To analyze the detection rate, clinical significance, and prognosis of Epstein-Barr virus (EBV) in the cerebrospinal fluid (CSF) of patients following allogeneic hematopoietic stem cell transplantation. Methods: A retrospective analysis was performed on 1100 patients who underwent the CSF virus test after allogeneic hematopoietic stem cell transplantation in Peking University People's Hospital between January 2017 and June 2022. Among them, 19 patients were screened positive for EBV in their CSF, and their clinical characteristics, treatment, and prognosis were analyzed. Results: Among 19 patients with EBV-positive cerebrospinal fluid, 12 were male and 7 were female, with 5 patients aged <18 years and 12 aged ≥18 years, with a median age of 27 (5-58) years old. There were 7 cases of acute myeloid leukemia, 8 of acute lymphocytic leukemia, 2 of aplastic anemia, 1 of Hodgkin's lymphoma, and 1 of hemophagocytic syndrome. All 19 patients underwent haploid hematopoietic stem cell transplantation, including 1 secondary transplant. Nineteen patients had neurological symptoms (headache, dizziness, convulsions, or seizures), of which 13 had fever. Ten cases showed no abnormalities in cranial imaging examination. Among the 19 patients, 6 were diagnosed with EB virus-related central nervous system diseases, with a median diagnosis time of 50 (22-363) days after transplantation. In 9 (47.3%) patients, EBV was detected in their peripheral blood, and they were treated with intravenous infusion of rituximab (including two patients who underwent lumbar puncture and intrathecal injection of rituximab). After treatment, EBV was not detected in seven patients. Among the 19 patients, 2 died from EBV infection and 2 from other causes. Conclusion: In patients who exhibited central nervous system symptoms after allogeneic hematopoietic stem cell transplantation, EBV should be screened as a potential pathogen. EBV detected in the CSF may indicate an infection; however, it does not confirm the diagnosis.
Humans ; Male ; Female ; Adolescent ; Adult ; Middle Aged ; Herpesvirus 4, Human ; Epstein-Barr Virus Infections/complications* ; Rituximab/therapeutic use* ; Retrospective Studies ; Clinical Relevance ; Hematopoietic Stem Cell Transplantation/adverse effects* ; Lymphoproliferative Disorders/drug therapy*

Objective To measure the prevalence of mental health symptoms and identify the associated factors among college students at the beginning of coronavirus disease 2019(COVID-19)outbreak in China. Methods We carried out a multi-center cross-sectional study via snowball sampling and convenience sampling of the college students in different areas of China.The rates of self-reported depression,anxiety,and stress and post-traumatic stress disorder(PTSD)were assessed via the 21-item Depression-Anxiety-Stress Scale(DASS-21)and the 6-item Impact of Event Scale-Revised(IES-6),respectively.Covariates included sociodemographic characteristics,health-related data,and information of the social environment.Data pertaining to mental health service seeking were also collected.Multivariate Logistic regression analyses were performed to identify the risk factors. Results A total of 3641 valid questionnaires were collected from college students.At the beginning of the COVID-19 outbreak,535(14.69%)students had negative emotions,among which 402(11.04%),381(10.49%),and 171(4.90%)students had the symptoms of depression,anxiety,and stress,respectively.Meanwhile,1245(34.19%)college students had PTSD.Among the risk factors identified,male gender was associated with a lower likelihood of reporting depression symptoms(AOR=0.755,P=0.037],and medical students were at higher risk of depression and stress symptoms than liberal arts students(AOR=1.497,P=0.003;AOR=1.494,P=0.045).Family support was associated with lower risks of negative emotions and PTSD in college students(AOR=0.918,P<0.001;AOR=0.913,P<0.001;AOR=0.899,P<0.001;AOR=0.971,P=0.021). Conclusions College students were more sensitive to public health emergencies,and the incidence of negative emotions and PTSD was significantly higher than that before the outbreak of COVID-19.More attention should be paid to female college students who were more likely to develop negative emotions.We should strengthen positive and proper propaganda via mass media and help college students understand the situation and impact of COVID-19.Furthermore,we should enhance family support for college students.The government and relevant agencies need to provide appropriate mental health services to the students under similar circumstances to avoid the deterioration of their mental well-being.
COVID-19/epidemiology* ; Cross-Sectional Studies ; Female ; Health Status ; Humans ; Male ; Students/psychology* ; Universities