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.

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.

BACKGROUND:Previous studies found that the proliferative scar-specific long non-coding RNA lncRNA HSFAS is a novel biomarker that can be used in the diagnosis of hypertrophic scar,but how it functions in hypertrophic scar is not clear. OBJECTIVE:To investigate the role and mechanism of lncRNA HSFAS in hypertrophic scar.METHODS:Fresh scar tissue and surrounding normal skin tissue samples from three patients with hypertrophic scar were collected,and tissue immunofluorescence was used to detect the expression of lncRNA HSFAS in frozen sections of two skin tissues. Primary fibroblasts were isolated from proliferative scarred skin tissue and normal skin tissue and cultured by enzyme digestion method. Quantitative real-time PCR was used to detect the mRNA expression of lncRNA HSFAS in cells. The proteins bound to lncRNA HSFAS were detected by RNA pull-down combined mass spectrometry. GO and KEGG were used to analyze the main functions and pathways of lncRNA HSFAS involved in hypertrophic scar progression. The targeted binding of lncRNA HSFAS to proteins was determined by catRAPID and RPISeq website analysis. RESULTS AND CONCLUSION:Compared with normal skin tissue and fibroblasts from normal skin tissue,the expression of lncRNA HSFAS in human hypertrophic scar tissue and primary fibroblasts from hypertrophic scar tissue was significantly increased (P<0.05). There were 510 proteins clearly bound to lncRNA HSFAS by RNA pull-down combined mass spectrometry. The results of GO and KEGG analyses showed that these proteins were mainly involved in RNA splicing and processing,chromosome synthesis and separation,and cell cycle. Among them,the proteins involved in RNA splicing and processing included scaffold attachment factor B2 and DICER1,and the binding fraction with lncRNA HSFAS was higher. The results of bioinformatics analysis showed that lncRNA HSFAS was bound to scaffold attachment factor B2 and DICER1 proteins. To conclude,lncRNA HSFAS may affect gene expression by interacting with scaffold attachment factor B2 and DICER1 proteins to regulate RNA splicing and processing modification,thus promoting the occurrence and development of hypertrophic scar.

Objective:To understand the usage situation of e-cigarettes among residents aged 18-65 in Beijing, explore the relationship between e-cigarette use and cigarette smoking as well as smoking cessation behaviors, and provide scientific support for the developing and improving policies and measures related to e-cigarettes.Methods:Using 19 684 residents data from the Beijing Non-communication Chronic Disease and Risk Factors Surveillance in 2022, complex sampling weighted methods were used to estimate proportions, and complex sampling logistic regression analysis was applied to explore the relationship between e-cigarette use, cigarette smoking, and smoking cessation.Results:Among all study participants, the proportion of those who had ever used e-cigarettes was 3.36%, with the current e-cigarette use at 1.26%. The proportion of current e-cigarette users (1.87%) and the former e-cigarette users (3.47%) were higher ( χ2=64.70, P<0.001) among males compared to females (0.60% and 0.64% respectively). The top three reasons for using e-cigarettes were wanting to quit smoking, perceiving e-cigarettes as less harmful, and enjoying the flavors of e-cigarettes. 83.54% of e-cigarette users started with cigarettes. The results of the complex sampling multivariable logistic regression analysis showed that current smoking ( OR=61.35, 95% CI: 36.98-101.76) and former smoking ( OR=31.20, 95% CI: 15.52-62.71) were positively associated with e-cigarette, while current e-cigarette use ( OR=0.13, 95% CI: 0.04-0.39) was negatively associated with quitting cigarette smoking. Conclusions:The proportion of e-cigarette use in Beijing was relatively low. E-cigarette use was associated with cigarette use and was not conducive to smoking cessation. Therefore, stronger regulatory measures and health education campaigns regarding the risks of e-cigarettes should be implemented.

Objective To evaluate the quality of cisatracurium besilate injection produced by domestic manufacturers.Methods A comprehensive evaluation of 104 batches of samples was carried out using statutory testing methods combined withexploratory research,including related substances,1,5-pentanediol diacrylate,residual solvents,genotoxic impurities of benzenesulfonate esters,infrared spectrum,and endotoxin examination.The quality of domestic products and the controllability of current specifications were comprehensively evaluated.Results According to the statutory tests,the qualified rate of 104 batches of samples was 100.0％.The exploratory research showed that the results of related substances in the samples produced by 6 manufacturers were far below the limit,and no genotoxic impurities of benzenesulfonate esters were detected.However,the results showed that there was variability in 1,5-pentanediol diacrylate,as well as residual solvents.Conclusions The quality of the cisatracurium besilate injection is good,and the current specifications should be further improved and unified.It was proposed that the infrared spectrum,related substance,and 1,5-pentanediol diacrylate method be added or revised,and the limit of endotoxin strictly controlled.It was proposed that manufacturers pay attention to the quality of API and control injection production.

BACKGROUND:Previous studies found that the proliferative scar-specific long non-coding RNA lncRNA HSFAS is a novel biomarker that can be used in the diagnosis of hypertrophic scar,but how it functions in hypertrophic scar is not clear. OBJECTIVE:To investigate the role and mechanism of lncRNA HSFAS in hypertrophic scar.METHODS:Fresh scar tissue and surrounding normal skin tissue samples from three patients with hypertrophic scar were collected,and tissue immunofluorescence was used to detect the expression of lncRNA HSFAS in frozen sections of two skin tissues. Primary fibroblasts were isolated from proliferative scarred skin tissue and normal skin tissue and cultured by enzyme digestion method. Quantitative real-time PCR was used to detect the mRNA expression of lncRNA HSFAS in cells. The proteins bound to lncRNA HSFAS were detected by RNA pull-down combined mass spectrometry. GO and KEGG were used to analyze the main functions and pathways of lncRNA HSFAS involved in hypertrophic scar progression. The targeted binding of lncRNA HSFAS to proteins was determined by catRAPID and RPISeq website analysis. RESULTS AND CONCLUSION:Compared with normal skin tissue and fibroblasts from normal skin tissue,the expression of lncRNA HSFAS in human hypertrophic scar tissue and primary fibroblasts from hypertrophic scar tissue was significantly increased (P<0.05). There were 510 proteins clearly bound to lncRNA HSFAS by RNA pull-down combined mass spectrometry. The results of GO and KEGG analyses showed that these proteins were mainly involved in RNA splicing and processing,chromosome synthesis and separation,and cell cycle. Among them,the proteins involved in RNA splicing and processing included scaffold attachment factor B2 and DICER1,and the binding fraction with lncRNA HSFAS was higher. The results of bioinformatics analysis showed that lncRNA HSFAS was bound to scaffold attachment factor B2 and DICER1 proteins. To conclude,lncRNA HSFAS may affect gene expression by interacting with scaffold attachment factor B2 and DICER1 proteins to regulate RNA splicing and processing modification,thus promoting the occurrence and development of hypertrophic scar.

Objective:To analyze the prevalence trends and epidemiological characteristics of cardiometabolic multimorbidity(CMM) in Beijing from 2005 to 2022.Methods:A series of representative cross-sectional surveys were conducted in Beijing between 2005 and 2022 using a stratified multistage cluster random sampling method. A total of 110 496 permanent residents aged 18-79 years participated in face-to-face interviews, physical examinations, and laboratory testing. Complex sampling logistic regression models were employed to identify factors associated with CMM, and Joinpoint regression was used to assess temporal trends in prevalence. Results:The prevalence of CMM was 22.3% in 2005 and 24.3% in 2022, with an average annual percent change of 0.1%(95% CI -1.3%-1.3%, P>0.05). In rural areas, the prevalence increased by 1.3% per year(95% CI 0.2%-2.6%, P<0.05), while among obese individuals, it decreased by 1.0% annually( P<0.05). The most common CMM patterns were hypertension combined with dyslipidemia(13.2%), hypertension combined with diabetes(7.0%), and diabetes combined with dyslipidemia(5.8%). The prevalence of hypertension and dyslipidemia comorbidity showed a long-term decline among females, those aged 60-79 and obese individuals( P<0.05). In contrast, the prevalence of hypertension and diabetes comorbidity increased over time in rural residents and individuals with normal body weight( P<0.05). Furthermore, diabetes and dyslipidemia comorbidity rates increased significantly among males, adults aged 18-59 years, those with a college education or above, rural residents and individuals with normal body weight( P<0.05). Multivariable logistic regression indicated that male, older age, overweight, obese, and lower education level were independently associated with a higher risk of CMM( P<0.05). Conclusion:From 2005 to 2022, the prevalence of CMM remained high among adults in Beijing. While prevalence decreased among obese individuals, it increased significantly in rural areas. Hypertension combined with dyslipidemia was the most common multimorbidity pattern throughout the study period.

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.

Osteoporosis is a prevalent skeletal condition characterized by reduced bone mass and strength, leading to increased fragility. Buqi-Tongluo (BQTL) decoction, a traditional Chinese medicine (TCM) prescription, has yet to be fully evaluated for its potential in treating bone diseases such as osteoporosis. To investigate the mechanism by which BQTL decoction inhibits osteoclast differentiation in vitro and validate these findings through in vivo experiments. We employed MTS assays to assess the potential proliferative or toxic effects of BQTL on bone marrow macrophages (BMMs) at various concentrations. TRAcP experiments were conducted to examine BQTL's impact on osteoclast differentiation. RT-PCR and Western blot analyses were utilized to evaluate the relative expression levels of osteoclast-specific genes and proteins under BQTL stimulation. Finally, in vivo experiments were performed using an osteoporosis model to further validate the in vitro findings. This study revealed that BQTL suppressed receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis and osteoclast resorption activity in vitro in a dose-dependent manner without observable cytotoxicity. The inhibitory effects of BQTL on osteoclast formation and function were attributed to the downregulation of NFATc1 and c-fos activity, primarily through attenuation of the MAPK, NF-κB, and Calcineurin signaling pathways. BQTL's inhibitory capacity was further examined in vivo using an ovariectomized (OVX) rat model, demonstrating a strong protective effect against bone loss. BQTL may serve as an effective therapeutic TCM for the treatment of postmenopausal osteoporosis and the alleviation of bone loss induced by estrogen deficiency and related conditions.
Animals ; NFATC Transcription Factors/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Ovariectomy ; Osteoclasts/metabolism* ; Female ; Osteogenesis/drug effects* ; Rats, Sprague-Dawley ; Rats ; NF-kappa B/genetics* ; Osteoporosis/genetics* ; Signal Transduction/drug effects* ; Bone Resorption/genetics* ; Cell Differentiation/drug effects* ; Humans ; RANK Ligand/metabolism* ; Mitogen-Activated Protein Kinases/genetics* ; Transcription Factors