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.

With the rapid development of social economy and the continuous improvement of human living standard, the incidence, fatality and recurrence rates of cardiovascular disease (CVD) are increasing year by year, which seriously affects people's life and health. Conventional therapeutic drugs have limited improvement on the disability rate, so the search for new therapeutic drugs and action targets has become one of the hotspots of current research. In recent years, the therapeutic role of the natural compound rosmarinic acid (RA) in CVD has attracted much attention, which is capable of preventing CVD by modulating multiple signalling pathways and exerting physiological activities such as antioxidant, anti-apoptotic, anti-inflammatory, anti-platelet aggregation, as well as anti-coagulation and endothelial function protection. In this paper, the role of RA in the prevention of CVD is systematically sorted out, and its mechanism of action is summarised and analysed, with a view to providing a scientific basis and important support for the in-depth exploration of the prevention value of RA in CVD and its further development as a prevention drug.

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.

The objective of this study was to analyze the effects on cell viability, apoptosis, and cell cycle of non-small cell lung cancer (NSCLC) A549 cells after intervention with Agrimonia pilosa (AP) and investigate Agrimonia pilosa anti-tumor activity in vitro. Meanwhile, liquid chromatography mass spectrometry (LC-MS) metabolomics technology was used to analyze the changes of cellular metabolites and metabolic pathways. The results of this study will provide a theoretical and experimental basis for investigating the mechanism of the effect of Agrimonia pilosa on non-small cell lung cancer A549 cells. The results showed that the cell nucleus of A549 cells crumpled and apoptosis occurred with the increase of drug concentration. The survival rate of the cells decreased, and the inhibition rate reached 21.5% and 91.74% under the low and high dose conditions, respectively. Lactate dehydrogenase (LDH) content increased (P < 0.05). Metabolomics results showed significant differences in metabolism between groups, thirty-three distinct metabolites including LysoPC(24:0/0:0), LysoPC(17:0/0:0) and PC(O-40:5) were deduced. The pathway enrichment showed that the Agrimonia pilosa plays an anti-tumor role mainly by regulating the metabolism of glycerophosphate and purine in A549 cells, in which the effect on glycerophosphate metabolism pathway was most significant. The results of combined pharmacodynamics suggested that Agrimonia pilosa might induce apoptosis and inhibit the growth of A549 cells by regulating LysoPC(24:0/0:0), LysoPC(17:0/0:0) and PC(O-40:5) metabolites in A549 cells.

Lattice strain ruthenium nanoparticles uniformly and stably supported on nitrogen-modified carbon nanosheets(RuNPs/NC)were prepared via simple wet-chemical and subsequent pyrolysis method.The nitrogen doped NC could effectively improve their uniform dispersion and lattice compression of RuNPs.Through changing the pyrolysis temperature,the nitrogen content,types and degree of lattice strain of RuNPs could be effectively tuned,which could be used to adjust and control their peroxidase-like activity.The as-prepared RuNPs/NC-900 exhibited highest peroxidase-like activity,and could catalyze the oxidation of 3,3′,5,5′-tetramethylbenzidine(TMB)to produce a blue product with the maximum absorption at 652 nm in the presence of H2O2.The steady-state kinetic analysis indicated that the reaction catalyzed by RuNPs/NC followed the Michaelis-Menten kinetic model.Tannic acid(TA),gallic acid(GA)and ascorbic acid(AA)could effectively inhibit the RuNPs/NC-H2O2-triggered chromogenic reaction of TMB,resulting in color fading and decrease in absorbance.Based on this,a sensitive and accurate system was constructed for detection of TA,GA and AA.The detection limits(3σ/S)for TA,GA and AA were 0.014,0.014 and 0.29 μmol/L,respectively.This study not only developed a colorimetric sensing method based on RuNPs/NC nanozyme but also offered a new approach for the sensitive detection of antioxidants in food.

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.

Objectives:To explore the relationship between insomnia symptoms and neuroticism in patients with COVID-19,and to explore the role of anxiety and psychological capital in the relationship.Methods:Totally 687 patients with COVID-19 were recruited from Shanghai Fangcang Hospital.The Athens Insomnia Scale(AIS),Eysenck Personality Questionnaire-Revised Short Scale for Chinese Neuroticism Subscale(EPQ-RSC-N),Self-Rat-ing Anxiety Scale(SAS)and Psychological Capital Questionnaire(PCQ)were used to measure insomnia symp-toms,neuroticism personality trait,anxiety symptoms and psychological capital levels.The deviation-corrected per-centile Bootstrap method was used to test the mediating effect,and the PROCESS program was used to test the moderated effect.Results:The detection rate of insomnia symptoms was 49.93％.The AIS scores were lower in male patients than in female patients(P＜0.01).The SAS scores partly mediated the relationship between neuroti-cism scores and AIS scores,with an effect size of 0.03,accounting for 18.29％of the total effect.With the im-provement of PCQ scores,the predictive effect of SAS scores on AIS scores gradually decreased(β=-0.01,t=-4.41,P＜0.001).Conclusions:Anxiety symptoms in patients with COVID-19 play a partial mediating role in the positive relationship between insomnia symptoms and neuroticism.The psychological capital moderates the relation-ship between insomnia and anxiety symptoms.

Objective To explore the quantitative evaluation of integrity risk prevention and control in public hospitals,provide reference for improving the quality and efficiency of integrity risk prevention and control.Methods Self-designed"Inspection Standards for Integrity Risk Prevention and Control of Power Matters in Public Hospitals"was used to score and rate the power matters provided by each functional department/clinical department of West China Hospital of Sichuan University from three aspects:the clarity of power operation process,the accuracy of finding integrity risk points,the effectiveness of prevention and control measures.Results A total of 236 power matters of the hospital were inspected for integrity risk prevention and control,and according to the inspection criteria,57 items were rated as first grade,103 items were rated as second grade,and 76 items were rated as third grade,accounting for 24.15％,43.64％and 32.20％,respectively.The score for the special work of integrity risk prevention and control was 5.82±1.92 points,of which the process dimension score was 2.11±0.75 points,the risk points dimension score was 1.89±0.92 points,the prevention and control dimension score is 1.89± 0.79 points,which reflects the problems of unclear workflow,inaccurate finding of individual risk points,and unspecified prevention and control measures in some units.Conclusion Hospitals should focus on the concreteness,accuracy,salience and quantification in the long-term construction of integrity risk prevention and control from the aspects of thought,behavior,effectiveness and evaluation.

AIM: To prepare a nanodrug MMC-ATS-@PLGA using polylactic acid hydroxyacetic acid copolymer(PLGA)as a carrier and mitomycin C(MMC)loaded on PLGA, and to analyse the biological safety and treatment effect of this nanodrug on inhibiting the proliferation of filtering bleb scarring after glaucoma surgery in vivo.METHODS: The thin-film dispersion hydration ultrasonic method was used to prepare the MMC-ATS-@PLGA, and its physical and chemical properties were detected. The effect of MMC-ATS@PLGA on rabbit corneas was analysed through corneal fluorescence staining and HE staining, and tear film rupture time(BUT), Schirmer test and intraocular pressure data were collected to analyse ocular surface biosafety. A slit lamp was used to observe and calculate the filtration bubble size, and the tissue morphological changes were analysed by conjunctival HE staining. In addition, immunohistochemistry and Elisa were used to compare the anti-inflammatory effects of Flumiolone Eye Drops(FML), MMC, and MMC-ATS-@PLGA nanoparticles on inhibiting the formation of filtering bleb scarring after glaucoma surgery from multiple perspectives via comparative proteomic analysis.RESULTS: The average particle size and zeta potential of MMC-ATS-@PLGA were 128.78±2.54 nm and 36.49±4.25 mV, respectively, with an encapsulation efficiency and a drug loading rate of(78.49±2.75)% and(30.86±1.84)%, respectively. At 33°C(the ocular surface temperature), the cumulative release rate of the MMC-ATS-@PLGA nanoparticles reached(76.58±2.68)% after 600 min. Moreover, corneal fluorescence staining, HE, BUT, Schirmer, and intraocular pressure results showed that MMC-ATS-@PLGA had good biocompatibility with the ocular surface of rabbits. At 3 wk after surgery, the area of filtering blebs in the MMC-ATS-@PLGA group was significantly larger than that in the FML group and MMC group, and the filtering blebs in the control group had basically disappeared. Pathological tissue analysis of the conjunctiva in the filtering blebs area of the eyes of the rabbits revealed that compared with that in the normal group, the morphology of the collagen fibres in the MMC-ATS-@PLGA group was relatively regular, the fibres were arranged neatly, and the tissue morphology was similar to that of the normal group. Immunohistochemistry and Elisa confirmed that compared with those in the normal group, the expression levels of α-SMA, CTGF, and type Ⅲ collagen fibre antibodies were significantly increased in the control group. After FML, MMC, or MMC-ATS-@PLGA treatment for 3 wk, the expression of inflammatory factors gradually decreased. Among the groups, the MMC-ATS-@PLGA group showed the most significant decrease(P<0.05).CONCLUSION: This study successfully synthesized a nanomedicine(MMC-ATS-@PLGA)that inhibits scar proliferation after glaucoma filtration surgery. The drug had stable physicochemical properties, good biocompatibility, and better anti-inflammatory effects by inhibiting the expression of α-SMA, CTGF, and type Ⅲ collagen fibres, which can prevent the formation of scarring in the filtering blebs area, thereby improving the success rate of glaucoma filtering surgery.