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.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

ObjectiveTo learn the density and seasonal variation of adult mosquitoes in Pudong New Area of Shanghai, and to explore the influence of meteorological factors on the density of adult mosquitoes. MethodsFrom April to November in 2017‒2021, adult mosquito density in Pudong New Area was monitored every ten days a time by using CO₂ trapping light method. Meteorological data were collected during the same time, and Pearson correlation analysis and multiple linear regression model were used to investigate the correlation between adult mosquito density and meteorological factors. ResultsThe seasonal variation of adult mosquito density showed a single-peak pattern, with the peak of 7.09 mosquitoes·(set·time)^-1 in July. The adult mosquito density was positively correlated with the monthly average temperature, monthly maximum temperature, monthly minimum temperature, and monthly average relative humidity (r=0.813, 0.793, 0.820, 0.617, all P<0.05), but negatively correlated with monthly average air pressure (r=-0.738, P<0.05). The regression equation of the adult mosquito density and monthly minimum temperature in Pudong New Area of Shanghai was Y=0.066 X₃-0.884, with a corrected R² of 0.673, indicating a good model fitting. ConclusionThe overall seasonal variation of adult mosquito density in Pudong New Area showed a single-peak pattern. The density of adult mosquitoes was correlated with the monthly average temperature, monthly maximum temperature, monthly minimum temperature, monthly average relative humidity, and monthly average air pressure, and linearly correlated with monthly minimum temperature.

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.

Child ; Humans ; Mpox (monkeypox)/prevention & control* ; Disease Outbreaks

Objective To observe the effects of Tianma Gouteng Decoction combined with magnesium sulfate and Labetalol on lowering blood pressure and improving hemorheology in patients with gestational hypertension.Methods Ninety patients with gestational hypertension of liver-yang hyperactivity type were randomly divided into the combination group and the control group,with 45 cases in each group.The control group was treated with magnesium sulfate combined with Labetalol,and the combination group was treated with Tianma Gouteng Decoction on the basis of treatment for the control group.The course of treatment lasted for 5 days.The changes of systolic blood pressure(SBP),diastolic blood pressure(DBP),urinary protein level,and hemorheological indicators of the two groups were observed before and after the treatment.Moreover,the adverse pregnancy outcomes,adverse reactions,and patients'satisfaction of the two groups were compared.Finally,the influencing factors of patients'adverse pregnancy outcomes were investigated by logistic regression analysis.Results(1)After treatment,the SBP,DBP and urinary protein level of patients in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the combination group was significantly superior to that in the control group(P＜0.01).(2)After treatment,the hemorheological indicators of plasma viscosity,whole blood viscosity and hematocrit of patients in the two groups were significantly decreased compared with those before treatment(P＜0.05),and the decrease in the combination group was significantly superior to that in the control group(P＜0.05 or P＜0.01).(3)The total incidence of adverse pregnancy outcomes in the combination group was 11.11%(5/45),which was significantly lower and that in the control group(33.33%,15/45),the difference being statistically significant(P＜0.05).(4)The patients'satisfaction of the combination group was 97.78%(44/45),which was significantly higher than that of the control group(84.44%,38/45),and the difference was statistically significant(P＜0.05).(5)The total incidence of adverse reactions in the combination group was 13.33%(6/45)and that in the control group was 8.89%(4/45),but the intergroup comparison showed no significant difference between the two groups(P＞0.05).(6)Logistic regression analysis of influencing factors showed that no medication of Tianma Gouteng Decoction combined with Labetalol and magnesium sulfate,and poor antihypertensive effect were the independent risk factors for adverse pregnancy outcomes in patients with gestational hypertension(all OR＞1,P＜0.05).Conclusion Tianma Gouteng Decoction combined with magnesium sulfate and Labetalol in treating gestational hypertension exerts certain antihypertensive effect,and the therapy can effectively improve the hemorheological indicators and the adverse pregnancy outcomes,and enhance the patients'satisfaction.

Objective To investigate the effect and mechanism of pomalidomide(POM)on airway inflammation and mucus hypersecretion in rats with chronic obstructive pulmonary disease(COPD).Methods Thirty-six SD rats were randomly divided into control group,model group and POM group,with 12 in each group,half male and half female.The COPD model was established by smoke exposure combined with Klebsiella pneumoniae infection in model group and POM group.The rats in POM group were treated with POM(0.5 mg/kg,once a day for 1 week).The lung function,lung tissue pathology,the proportion of inflammatory cells in bronchoalveolar lavage fluid(BALF)and the levels of serum inflammatory factors tumor necrosis factor-α(TNF-α),interleukin(IL)-1β,IL-6 and IL-13 were observed and detected in each group.AB-PAS staining and immunohistochemistry were used to analyze the proliferation of goblet cells and the secretion of mucin(MUC)5AC and MUC5B in airway epithelium of rats.The expression levels of TNF-α receptor 1(TNFR1),IκB kinase(IKK),phosphorylated IKK(p-IKK)and P65 protein in lung tissue were detected by Western blotting.Results Compared with control group,model group showed significant decreased of tidal volume(TV),minute ventilation(MV),forced expiratory vital capacity(FVC),0.1s forced expiratory volume(FEV0.1)and 0.3 s forced expiratory volume(FEV0.3)(P<0.05),increased of the mean linear intercept(MLI)of the alveoli(P<0.01),decreased of the mean alveolar number(MAN)(P<0.01),increased of the proportion of neutrophils and lymphocytes in BALF sediment(P<0.05),and decreased of the proportion of macrophages in BALF sediment(P<0.01);increased of the levels of serum inflammatory factors TNF-α,IL-1β,IL-13 and IL-6(P<0.05),the proportion of goblet cells in airway epithelium(P<0.01),the secretion of MUC5AC and MUC5B in lung tissue(P<0.01),the content of TNFR1 and the ratio of p-IKK/IKK(P<0.01),the content of P65 in nucleus(P<0.01);and decreased of the content of P65 in cytoplasm(P<0.05).Compared with model group,after one week of POM treatment,POM group showed significant improved of the TV,MV,FVC,FEV0.1,FEV0.3,MLI and MAN of rats(P<0.05);decreased of the proportion of neutrophils and lymphocytes in BALF(P<0.05);increased of the proportion of macrophages(P<0.01);decreased of the levels of serum TNF-α,IL-1β,IL-6 and IL-13(P<0.05),the proportion of goblet cells in airway(P<0.01),the secretion of MUC5AC and MUC5B(P<0.01),and the expression of TNFR1,P-IKK and P65(nucleus)(P<0.05);and increased of the level of P65(cytoplasm)(P<0.01).Conclusions POM can improve airway inflammation and mucus hypersecretion in COPD rats,which may be achieved by inhibiting TNF-α/NF-κB signaling pathway.

Objective To investigate the protective effect and mechanism of acellular nerve allografts(ANA)combined with electroacupuncture on spinal ganglia in rats with sciatic nerve injury(SNI).Methods Totally 50 male adult SD rats were randomly selected for this experiment.Ten rats were prepared for the ANA.Forty male SD rats were randomly divided into normal group,model group,ANA group and combinational group,with 10 rats in each group.The SNI model was established by cutting off the nerves 10 mm at the 5 mm on the inferior border of piriformis after separating the right sciatic nerves.The rats in the ANA group were bridged with ANA to the two broken ends of injured nerves.The rats in the combinational group were treated with electroacupuncture 2 days after ANA bridging,Huantiao(GB30)and Yanglingquan(GB34)were performed as the acupuncture points,each electroacupuncture lasted 15 minutes and 7 days as a course of treatment,4 courses in all.Sciatic nerve conduction velocity was measured by electrophysiology to evaluate the regeneration of damaged axons.Morphology of spinal ganglia was observed by Nissl staining.The expression of nerve growth factor(NGF)and brain-derived neurotrophic factor(BDNF)were detected by Western blotting and immunofluorescent staining.Results Compared with the normal group,the sciatic nerve conduction velocity in model group decreased significantly(P<0.01),Nissl bodies in neurons of spinal ganglia were swollen and dissolved,with incomplete structure and the number decreased dramatically(P<0.01),while the level of NGF and BDNF also decreased significantly(P<0.01).Compared with the model group,the sciatic nerve conduction velocity in ANA and combinational groups strongly increased(P<0.01),the damage of Nissl bodies in neurons of spinal ganglia reduced and the number obviously increased(P<0.01),the level of NGF and BDNF increased considerably(P<0.01).Compared with the ANA group,the sciatic nerve conduction velocity in combinational group increased significantly(P<0.01),the morphology of Nissl bodies in neurons of spinal ganglia were more regular and the number increased(P<0.01),moreover,the level of NGF also increased significantly(P<0.01).Conclusion ANA combined with electroacupuncture can enhance the sciatic nerve conduction velocity,improve the morphology of neurons in spinal ganglia and play a protective effect on spinal ganglia.The mechanism can be related to the higher expression of NGF and BDNF proteins,especially the expression of NGF protein.

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 analyze the atrioventricular synchronization rate after implantation of Micra AV leadless pacemaker,and the impact of postoperative programming optimization on atrioventricular synchronization rate.Methods A prospective cohort study was conducted to select patients with complete atrioventricular block who underwent Micra AV leadless pacemaker implantation at Beijing Anzhen Hospital from August 2022 to June 2023.Programming optimization were performed at 1 week,1 month,and 3 months postoperatively,and atrioventricular synchronization rate,electrical parameters,and echocardiography were recorded.Results A total of 68 patients with complete atrioventricular block implanted with Micra AV were selected,with an average age of(68.2±9.7)years,including 47 males(69.1％).All patients were successfully implanted with Micra AV,and there were no serious postoperative complications;The average threshold,sense,and impedance parameters were stable during 1 week,1 month,and 3 months after the procedure;There was no significant difference in the EF value of postoperative echocardiography(P=0.162);The average atrioventricular synchronization rates at 1 week,1 month,and 3 months postoperatively were(75.2％vs.83.8％vs.91.6％,P=0.001).Conclusions As an mechanical atrial sensing,Micra AV requires personalized adjustment of relevant parameters;Postoperative follow-up programming optimization plays an important role in the atrioventricular synchronization and comfort level in patients with complete atrioventricular block after implantation of Micra AV leadless pacemaker.