ObjectiveThe present study aimed to investigate alterations in white matter microstructure and spontaneous neural activity in male college smokers, and to further explore their associations with nicotine dependence. Given that adolescence and early adulthood represent critical periods for brain maturation, particularly for white matter development, understanding the neural correlates of smoking behavior during this stage is of substantial importance for both neuroscience and public health. MethodsA total of 115 male undergraduate students were initially recruited for this study. After quality control and exclusion procedures, 52 male college smokers and 42 demographically matched healthy non-smokers were included in the final analysis. All participants underwent multimodal magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI). White matter fiber tracts were reconstructed using the automated fiber quantification (AFQ) method, which enables precise identification and quantification of major fiber bundles. Eighteen major white matter tracts were segmented for each participant. Along the core trajectory of each tract, 100 equidistant nodes were sampled. Fractional anisotropy (FA) was calculated at each node to assess white matter microstructural integrity, while amplitude of low-frequency fluctuation (ALFF) was computed to evaluate spontaneous neural activity within white matter tracts. Between-group differences in FA and ALFF were assessed using two-sample t-tests, with appropriate corrections applied for multiple comparisons. Furthermore, Pearson correlation analyses were conducted to examine the relationships between imaging-derived metrics (FA and ALFF values in regions showing significant group differences) and nicotine dependence severity, as measured by the Fagerström test for nicotine dependence (FTND). ResultsCompared with healthy non-smokers, male college smokers exhibited significantly increased FA values in several white matter tracts, including the left thalamic radiation, right corticospinal tract, forceps major of the corpus callosum, left uncinate fasciculus, and right arcuate fasciculus. These findings suggest altered microstructural organization or increased directional coherence within these pathways. In addition, smokers demonstrated significantly elevated ALFF values in the forceps major, right uncinate fasciculus, and left arcuate fasciculus, indicating enhanced spontaneous neural activity in these white matter regions. Correlation analyses revealed that FA values in the left thalamic radiation and right corticospinal tract were negatively correlated with FTND scores, suggesting that higher levels of nicotine dependence were associated with reduced microstructural integrity or altered fiber organization in these regions. In contrast, ALFF values in the forceps major and right uncinate fasciculus were positively correlated with FTND scores, indicating that greater nicotine dependence was associated with increased spontaneous neural activity in specific white matter pathways. ConclusionThe present study provides evidence that male college smokers exhibit distinct alterations in both white matter microstructure and functional activity. These abnormalities are not uniformly distributed but rather localized to specific fiber tracts implicated in sensorimotor processing, interhemispheric communication, and higher-order cognitive and emotional regulation. Importantly, the observed associations between imaging metrics and nicotine dependence severity suggest that these structural and functional alterations may reflect neurobiological mechanisms underlying addiction. The combination of AFQ-based tract profiling and multimodal MRI offers a sensitive approach for detecting subtle changes along white matter pathways, highlighting its potential utility in identifying neuroimaging biomarkers of nicotine dependence. Overall, these findings indicate that smoking during early adulthood may disrupt ongoing white matter maturation, potentially leading to long-term consequences for brain function. This study provides novel insights into the neural basis of nicotine dependence and underscores the importance of early intervention and prevention strategies targeting young smokers.

Aim To elucidate the underlying mecha-nism by which total triterpenoids extracted from Hove-nia dulcis(H-TP)enhance the sensitivity of A549/DDP cells to cisplatin.Methods The ARE-Nrf2 lu-ciferase reporter assay was applied to investigate the impact of H-TP on Nrf2 expression.Western blot was used to detect the protein levels of Keap-1/Nrf2/HO-1,Nrf2-GPX4 signaling pathway,apoptosis-related proteins of Bcl-2 and Bax.Further validation of its effects on Nrf2 was conducted by using Nrf2 activator/inhibitor.Results H-TP could enhance the sensitivi-ty of A549/DDP cells to cisplatin by modulating the expression of apoptosis-related proteins Bax and Bcl-2,inhibiting the Keap-1/Nrf2/HO-1/GPX4 signating pathway in A549/DDP cells,and inducing ferroptosis.Conclusion H-TP enhances the sensitivity of A549/DDP cells to cisplatin by inducing the Nrf2-mediated ferroptosis pathway.

Aim To elucidate the underlying mecha-nism by which total triterpenoids extracted from Hove-nia dulcis(H-TP)enhance the sensitivity of A549/DDP cells to cisplatin.Methods The ARE-Nrf2 lu-ciferase reporter assay was applied to investigate the impact of H-TP on Nrf2 expression.Western blot was used to detect the protein levels of Keap-1/Nrf2/HO-1,Nrf2-GPX4 signaling pathway,apoptosis-related proteins of Bcl-2 and Bax.Further validation of its effects on Nrf2 was conducted by using Nrf2 activator/inhibitor.Results H-TP could enhance the sensitivi-ty of A549/DDP cells to cisplatin by modulating the expression of apoptosis-related proteins Bax and Bcl-2,inhibiting the Keap-1/Nrf2/HO-1/GPX4 signating pathway in A549/DDP cells,and inducing ferroptosis.Conclusion H-TP enhances the sensitivity of A549/DDP cells to cisplatin by inducing the Nrf2-mediated ferroptosis pathway.

Objective:To observe and analyze the ocular clinical features and pathogenic genes of horizontal gaze palsy with progressive scoliosis (HGPPS).Methods:A pedigree study was conducted.Two families with HGPPS diagnosed by ophthalmology examination at Henan Children's Hospital from November 2023 to April 2024 were included, with 3 people from two generations in each family.Medical history and family history of the subjects were inquired.Vision acuity, diopter, anterior segment, intraocular pressure, wide-angle laser scanning ophthalmoscopy, optical coherence tomography, visual evoked potential (VEP), electroretinogram (ERG), ocular B-ultrasound, full spine AP+ lateral view, orbit+ skull+ cervical spine+ thoracic spine+ lumbosacral spine MRI plain scan were performed on the subjects.Whole genomic DNA was extracted from 2 ml of peripheral venous blood collected from the subjects, and gene sequencing was performed using whole exome sequencing (WES) technology.Suspicious pathogenic variant loci were verified by Sanger sequencing, and the pathogenicity of gene variant loci was analyzed according to the ACMG standards and guidelines for the interpretation of sequence variants.This study followed the Declaration of Helsinki.The study protocol was reviewed and approved by the Ethics Committee of Henan Children's Hospital (No.2024-KY-0024).All subjects and guardians signed informed consent forms and were informed of relevant matters before genetic testing.Results:The proband from family 1 was male, 3 years and 2 months old.At the age of 6 months, he was found to have head tilted to the left with a right scoliosis of the spine centered on T11-12 and no obvious abnormalities on VEP and ERG examinations.The proband from family 2 was male, 3 years and 4 months old, with a left scoliosis of the spine centered on T12.Both probands developed horizontal fixation paralysis, unable to rotate eye outward, slightly limited inward rotation, left eye hypertropia, mild horizontal nystagmus, normal vertical eye movement, backward development of major movements, and normal vision, anterior segment and fundus.MRI examination showed that the medulla oblongata was butterfly shaped, and a brainstem fissure could be seen in the center of the medulla oblongata.The genetic testing showed that the proband from family 1 had compound heterozygous variations c. 1054delC/p.Gln352Serfs *90 (M1) in exon 7 and c. 1219G>T/p.Gly407Cys (M2) in exon 8 of ROBO3 gene.The father of the proband carried M1 and the mother of the proband carried M2.The proband from family 2 had compound heterozygous variations c. 1888C>T/p.R630X (M3) in exon 12 and c. 2684C>A/p.A895E (M4) in exon 17 of ROBO3 gene.M1 and M3 were possible pathogenic, and M2 and M4 were of unknown clinical significance, and prediction software predicted M2 and M4 were harmful variations. Conclusions:The main clinical features of two HGPPS pedigree are horizontal fixation paralysis and progressive scoliosis, accompanied by nystagmus and strabismus.MRI shows brainstem fissure in the central medullary area.Four variants are novel variants, which increases the variation spectrum of ROBO3 gene.

OBJECTIVE To investigate the therapeutic mechanisms of electroacupuncture at"Tianshu"(ST25)and"Sanyinjiao"(SP6)acupoints combined with metformin in the treatment of type 2 diabetes mellitus(T2DM)using serum non-targeted metabolomics.METHODS Male SD rats were randomly divided into blank group,model group,metformin group,electroacupunc-ture group,and acupuncture-medicine combination(electroacupuncture combined with metformin)group.A type 2 diabetes model was established by high-fat diet combined with intraperitoneal injection of streptozotocin.The metformin group was treated with 250 mg·kg-1 metformin by gavage,the electroacupuncture group was treated with bilateral Tianshu and Sanyinjiao,the acupuncture-medicine combination group was treated with metformin by gavage combined with electroacupuncture,and the blank group and model group were treated with normal saline by gavage.All rats were treated 6 times a week for 7 weeks.After the intervention,the blood glucose level in the tail vein of the rats was measured using a blood glucose meter in the fasting state.The blood glucose levels of the rats were measured at 30,60,120,and 240 min after intraperitoneal injection of 50%glucose solution(4 mL·kg-1)to evaluate glu-cose tolerance.The serum insulin level of the rats was detected by ELISA and the insulin resistance index was calculated.The blood biochemical parameters were measured by an automatic blood biochemical analyzer.HE staining was used to evaluate the pathological conditions of the liver and pancreatic tissues of the rats.Ultra-performance liquid chromatography-mass spectrometry(UPLC-MS)technology was used for mass spectrometry detection to identify differential metabolites,and MetaboAnalyst 5.0 was used for pathway enrichment analysis.RESULTS Compared with the blank group,the fasting blood glucose,area under the glucose tolerance curve,and insulin resistance index of the model group rats were significantly increased(P<0.001),blood TP and GLB were significantly de-creased(P<0.01),AST,ALT,and ALP were significantly increased(P<0.05,P<0.01,P<0.001),and obvious inflammatory cell infiltration and pathological damage were observed in the liver and pancreas tissues;compared with the model group,the fasting blood glucose,area under the glucose tolerance curve,and insulin resistance index of the acupuncture-medicine combination group were sig-nificantly decreased(P<0.05,P<0.01,P<0.001),blood ALP was significantly decreased(P<0.01),TP and GLB were significant-ly increased(P<0.05),and the pathological damage of the liver and pancreas was significantly improved.Serum metabolomics showed that the metabolic profiles of the groups were well distinguished.Compared with the blank group,the differential metabolites in the model group were enriched in histidine metabolism,thiamine metabolism,taurine and hypotaurine metabolism,ascorbic acid and alde-hyde ester metabolism,valine,leucine and isoleucine biosynthesis pathways;compared with the model group,237 metabolites such as 3-aminoadipic acid,3-oxocyclobutanecarboxylic acid and phosphorylcholine in the acupuncture-medicine combination group were sig-nificantly reduced,and the pathways were enriched in histidine metabolism,linoleic acid metabolism,thiamine metabolism,taurine and hypotaurine metabolism,valine,leucine and isoleucine biosynthesis pathways.CONCLUSION Electroacupuncture combined with metformin can effectively improve the glucose and lipid metabolism of T2DM rats,and its potential mechanism may be related to the regulation of amino acid metabolism.

Electroencephalogram(EEG)refers to the electrical activity generated by neuronal discharges in the human brain,conventionallyrecorded on the scalp of the brain.EEG technology enables the non-invasive detection of electrophysiological activity in neuronal populations within the cerebral cortex,capturing millisecond-level changes in brain potentials with high temporal resolution.In recent years,lots of international researchers have leveraged EEG technology to investigate smoking addiction and further explore the millisecond-level electrophysiological changes in the brains of smokers.This review synthesizes recent advances in resting-state EEG and task-evoked event-related potential research among smokers,summarizing the electrophysiological activity changes associated with smoking addiction.Future researches will employ both longitudinal tracking and machine learning algorithms to further reveal the relationship between EEG signatures and smoking behaviors,thereby providing a scientific foundation for early intervention and treatment strategies targeting smoking addiction.

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.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

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.