Objective To explore the differences in heartbeat-evoked response (HER) under drug-related cues and neutral cues in individuals with heroin use disorder (HUD), and analyze the correlation between HER potentials and immediate cue-induced craving scores. Methods Fifty HUD participants were recruited from the Chang’an Compulsory Isolation Drug Rehabilitation Center in Shaanxi Province from June to September 2024. Simultaneous acquisition of 64-channel electroencephalography (EEG) and electrocardiogram signals was performed. Twenty alternating segments of drug-related and neutral cue videos were presented, and participants rated their subjective craving after each segment using visual analogue scale (VAS) scores. Scalp EEG data were source analyzed to obtain cortical EEG signals and corresponding HER. Short-time Fourier transform was used to calculate the power spectral density (PSD) of EEG within a time window from 100 ms before the R-peak to 500 ms after it, using the R-peak as the time zero point. Cluster-based permutation testing was used to analyze PSD differences between drug-related and neutral cues in the HUD individuals. Pearson correlation analysis was performed to evaluate the correlation between HER potentials and VAS scores. Results In the 350–420 ms time window, HER potentials in the left posterior parietal, temporal, and posterior cingulate cortices were significantly lower under drug-related cues compared to neutral cues (P＜0.01); in the 140–210 ms time window, HER potentials in the right prefrontal cortex were significantly higher under drug-related cues compared to neutral cues (P＜0.01). Correlation analysis showed that HER potentials in the left temporal and left posterior cingulate cortices were significantly negatively correlated with VAS scores (P＜0.05). Drug-related cues enhanced PSD of γ power (30–100 Hz) in salience network (fronto-insular), parietal and occipital regions (P＜0.05). PSD integrations of low-γ power (40–60 Hz) in parietal region (350–400 ms) and high-γ power (70–100 Hz) in left salience network (fronto-parietal) and occipital regions (300–350 ms) were positively correlated with VAS scores (P＜0.05). Conclusions Drug-related cues may modulate cortical activity related to heartbeat perception in HUD individuals, and such dynamic changes in both time and frequency domains are stably associated with subjective craving.

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.

Objective To construct a clinical-radiological nomo-gram model for severe mycoplasma pneumoniae pneumonia coinfec-tion with other pathogens(Co-SMPP)in children.Methods The clinical and radiological data of children with severe mycoplasma pneumoniae pneumonia(SMPP)who underwent nucleic acid testing or bronchoalveolar lavage(BAL)were analyzed retrospectively.The data analysis were performed by using SPSS 27.0 software.The group comparison between simple SMPP and Co-SMPP children was conducted by using t-tests,Mann-Whitney U tests,or chi-square tests.Nomogram analysis was performed by using R software and rms packages.The predictive performance of the model was evaluated by using the receiver operating characteristic(ROC)curve.Results A total of 194 SMPP children were included in the study,including 136 cases(70.1％)with simple SMPP,58 cases(29.9％)with Co-SMPP.The fibrinogen and albumin levels were lower in Co-SMPP children[(3.53±0.85)g/L,41.00(39.03,43.68)g/L]than in simple SMPP children[(3.79±0.80)g/L,42.80(41.00,44.40)g/L],with P values of 0.047 and 0.036,respec-tively.The probability of bronchial stenosis and grid shadow were higher in Co-SMPP children than in simple SMPP children,and there were significant differences between the two groups(P＜0.001,P=0.010).The odds ratio of bronchial stenosis in predicting Co-SMPP children was 14.085.The clinical-radiological nomogram model had an area under the curve(AUC)of 0.840,with sensi-tivity and specificity of 0.756 and 0.848,respectively.Conclusion The nomogram model based on clinical-radiological features can effectively predict Co-SMPP.

This study was aimed at developing an in vitro fluorescent substrate assay for the activity of leucyl aminopeptid-ase(LAP)from Echinococcus multilocularis and comparing it with the chemical chromogenic substrate enzyme activity assay.Through the establishment of reaction conditions for the fluorescent substrate-based in vitro enzyme activity assay,we com-pared the differences between the fluorescent substrate L-Leucine-7-amido-4-methylocoumarin(Leu-AMC)and the chemical chromogenic substrate L-Leucine-4-nitroanilide(Leu-pNA)through molecular docking,inhibition rates,and precision measures.Molecular docking revealed that the fluorescent substrate Leu-AMC had higher affinity for the protein than the chemical chromogenic substrate Leu-pNA.Through analysis of the effects of varying reaction conditions on fluorescence intensi-ty,we optimized the fluorescent substrate enzyme activity assay to demonstrate favorable performance at a reaction temperature of 37℃,a pH of 9.0,a protein concentration of 800 nmol/L,and a reaction duration of 60 minutes.Leu-AMC exhibited significant and distinct responses at a 5 μmol/L substrate concentration,under varying substrate conditions.The fluo-rescent substrate assay demonstrated more significant intergroup differences than the chemical chromogenic substrate assay when various inhibitors were added.This study established a fluorescence-based enzyme activity assay for leucyl aminopeptidase from Echinococcus multilocularis by using Leu-AMC as the substrate;this method demonstrated a more significant intergroup difference and sensitivity than the chemical chromogenic substrate assay.

Objective To investigate the dynamic functional connectivity differences between insomnia patients and healthy controls in triple brain networks[the significant network(SN),the default mode network(DMN),and the executive control network(ECN)]using functional magnetic resonance imaging,and uncover their associations with cognitive ability.Methods Dynamic functional connectivity analysis was performed on functional magnetic resonance imaging data from 40 insomnia patients and 40 healthy controls.The changes in dynamic functional connectivity values were studied for SN,DMN,ECN[including the left executive control network(LECN)and the right executive control network(RECN)];the similarities and differences in time characteristic indicators such as time score,average dwell time,and conversion rate were explored;and their associations with clinical information were analyzed.Results The SN-LECN and DMN-RECN dynamic functional connectivity was significantly higher in insomnia patients than in healthy controls(P=0.013,0.047),while the RECN-LECN and RECN internal functional connectivity strength was lower in insomnia patients than in healthy controls(P<0.001).Additionally,the fractional time in state 2 in insomnia group was significantly higher than that in healthy controls(P<0.001),and it was positively correlated with the Pittsburgh sleep quality index(r=0.524,P=0.001).Conclusion Insomnia patients exhibit significant abnormalities in triple brain network dynamic functional connectivity,which may be related to abnormalities in cognitive control and sensory processing in insomnia patients.These findings provide a new perspective for further research on the neural mechanisms and potential intervention strategies for insomnia.

Given the lack of annotations for key lung organs and tissues in existing public datasets,this study collected 863 cases of chest CT scan images and constructed the first comprehensive dataset containing annotations of pulmonary vessels,airways,and nodules using a semi-automated method that combines computer vision algorithms with manual corrections by radiologists.On this basis,a lung nodule segmentation model based on multi-task learning is proposed.By incorporating annotations of pulmonary vessels(pulmonary arteries and veins)and the trachea to enhance model's ability to learn lung features,the proposed model reduces the false discovery rate in lung nodule detection,and improves generalization ability.Additionally,the use of larger image patches further optimizes model performance.The trained VAAN_128 model achieves the best performance,with a Dice coefficient of 0.694 and a false discovery rate of 0.210 for lung nodule segmentation.Moreover,it simultaneously provides accurate segmentation results of pulmonary vessels and the trachea,assisting in the formulation of more precise diagnosis and treatment plans.Based on the VAAN_128 model,a software system for navigation and localization in biopsy procedures is developed.In clinical practice,this system can assist physicians in accurately locating lung nodules,distinguishing critical tissues,and improving preoperative planning efficiency.This provides precise and efficient technical support for early diagnosis and disease monitoring of lung diseases,and is of great significance for path planning in clinical navigation system and future lung imaging research.

Given the lack of annotations for key lung organs and tissues in existing public datasets,this study collected 863 cases of chest CT scan images and constructed the first comprehensive dataset containing annotations of pulmonary vessels,airways,and nodules using a semi-automated method that combines computer vision algorithms with manual corrections by radiologists.On this basis,a lung nodule segmentation model based on multi-task learning is proposed.By incorporating annotations of pulmonary vessels(pulmonary arteries and veins)and the trachea to enhance model's ability to learn lung features,the proposed model reduces the false discovery rate in lung nodule detection,and improves generalization ability.Additionally,the use of larger image patches further optimizes model performance.The trained VAAN_128 model achieves the best performance,with a Dice coefficient of 0.694 and a false discovery rate of 0.210 for lung nodule segmentation.Moreover,it simultaneously provides accurate segmentation results of pulmonary vessels and the trachea,assisting in the formulation of more precise diagnosis and treatment plans.Based on the VAAN_128 model,a software system for navigation and localization in biopsy procedures is developed.In clinical practice,this system can assist physicians in accurately locating lung nodules,distinguishing critical tissues,and improving preoperative planning efficiency.This provides precise and efficient technical support for early diagnosis and disease monitoring of lung diseases,and is of great significance for path planning in clinical navigation system and future lung imaging research.

Objective To observe the value of conventional MRI and diffusion weighted imaging(DWI)for differentiating soft tissue lymphoma(STL)and soft tissue sarcoma(STS).Methods Conventional MRI and DWI data of 25 cases of STL(STL group)and 38 cases of STS(STS group)were retrospectively analyzed.MRI features being statistically different between groups were included in logistic regression analysis to screen the independent risk factors of STL and to evaluate the sensitivity,specificity and accuracy of their combination for predicting STL.Receiver operating characteristic curve was generated,the area under the curve(AUC)was calculated to assess the diagnostic efficacy of the mean apparent diffusion coefficient(ADCmean),the minimum apparent diffusion coefficient(ADCmin),the maximum apparent diffusion coefficient(ADCmax)values for distinguishing STL from STS.Results Slightly hyperintensity on T1WI,non-necrosis,involvement of multiple muscle groups and homogeneous enhancement were all independent risk factors of STL(all P＜0.05).The sensitivity,specificity and accuracy of their combination for predicting STL was 72.00％(18/25),89.47％(34/38)and 82.54％(52/63),respectively.ADCmean,ADCmin and ADCmax values of STL was(1.06±0.18)× 10-3,(0.77±0.14)×10-3 and(1.47±0.31)× 10-3mm2/s,respectively,all lower than those of STS([1.31±0.17]× 10-3,[1.02±0.23]× 10-3 and[1.64±0.16]× 10-3 mm2/s;t=-4.829--2.498,all P＜0.05).The AUC of ADCmean,ADCmin and ADCmax values and their combination for differential diagnosis of STL and STS was 0.845,0.844,0.683 and 0.877,respectively.Conclusion Conventional MRI features,including T1WI signal intensity,necrosis,involvement of multiple muscle groups and enhancement pattern,along with ADCmean and ADCmin values derived from DWI contributed to differentiating STL and STS.

Objective To investigate the correlation between dual-energy computed tomography angiography(CTA)parameters of carotid plaques and acute stroke events.Methods A retrospective analysis was conducted on the clinical and imaging data of patients who underwent dual-energy head and neck CTA and brain MRI scans.Utilizing the Siemens workstation(Syngo.Via VB40B),region of interest(ROI)were placed on the thickest slice of the carotid plaque in the axial plane to obtain parameters such as fat fraction(FF),virtual non-contrast(VNC)value,iodine concentration(IC),electron density(Rho),effective atomic number(Zeff),dual energy index(DEI),spectral curve,and corresponding CT values at 40 keV(40 keVHU)and 90 keV(90 keVHU).The slope of the energy spectrum curve(λ)was calculated within the 40 keV-90 keV range.Patients with acute cerebral infarction(ACI)in the ipsilateral anterior circulation territory were classified into the ACI group,while those without were classified into the non-acute cerebral infarction(NACI)(NACI group).Qualitative data were analyzed using the x2 test,and quantitative data were analyzed using the t-test.The predictive performance was assessed using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve,and the differences between different ROC curves were compared using the DeLong test.Results A total of 72 patients were included,with 21 in the ACI group and 51 in the NACI group.The mean values of FF,Zeff,and 40 keVHU in the ACI group were greater than those in the NACI group.Statistically significant differences were observed between the groups for Zeff,DEI,40 keVHU,and λ(P＜0.05).40 keVHU demonstrated the highest predictive performance,and the AUC,sensitivity,and specificity was 0.789,81.0％,and 74.5％,respectively.A combined variable constructed through logistic regression analysis yielded an AUC,sensitivity,and specificity of 0.796,85.7％,and 70.6％,respectively,with no significant statistical differences compared to single factor variables.Conclusion Dual-energy CTA parameters of carotid plaques may aid in predicting intraplaque hemorrhage(IPH)and the occurrence of acute stroke events.

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.