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.

Depression, a prevalent mental disorder with significant socioeconomic burdens, underscores the urgent need for safe and effective non-pharmacological interventions. Recent advances in microbiome research have revealed the pivotal role of gut microbiota dysbiosis in the pathogenesis of depression. Concurrently, exercise, as a cost-effective and accessible intervention, has demonstrated remarkable efficacy in alleviating depressive symptoms. This comprehensive review synthesizes current evidence on the interplay among exercise, gut microbiota modulation, and depression, elucidating the mechanistic pathways through which exercise ameliorates depressive symptoms via the microbiota-gut-brain (MGB) axis. Depression is characterized by gut microbiota alterations, including reduced alpha and beta diversity, depletion of beneficial taxa (e.g., Bifidobacterium, Lactobacillus, and Coprococcus), and overgrowth of pro-inflammatory and pathogenic bacteria (e.g., Morganella, Klebsiella, and Enterobacteriaceae). Metagenomic analyses reveal disrupted metabolic functions in depressive patients, such as diminished synthesis of short-chain fatty acids (SCFAs), impaired tryptophan metabolism, and dysregulated bile acid conversion. For instance, Bifidobacterium longum deficiency correlates with reduced synthesis of neuroactive metabolites like homovanillic acid, while decreased Coprococcus abundance limits butyrate production, exacerbating neuroinflammation. Furthermore, elevated levels of indole derivatives from Clostridium species inhibit serotonin (5-HT) synthesis, contributing to depressive phenotypes. These dysbiotic profiles disrupt the MGB axis, triggering systemic inflammation, neurotransmitter imbalances, and hypothalamic-pituitary-adrenal (HPA) axis hyperactivity. Exercise exerts profound effects on gut microbiota composition, diversity, and metabolic activity. Longitudinal studies demonstrate that sustained aerobic exercise increases alpha diversity, enriches SCFA-producing genera (e.g., Faecalibacterium prausnitzii, Roseburia, and Akkermansia), and suppresses pathobionts (e.g., Desulfovibrio and Streptococcus). For example, a meta-analysis of 25 trials involving 1 044 participants confirmed that exercise enhances microbial richness and restores the Firmicutes/Bacteroidetes ratio, a biomarker of metabolic health. Notably, endurance training promotes Veillonella proliferation, which converts lactate into propionate, enhancing energy metabolism and delaying fatigue. Exercise also strengthens intestinal barrier integrity by upregulating tight junction proteins (e.g., ZO-1, occludin), thereby reducing lipopolysaccharide (LPS) translocation and systemic inflammation. However, excessive exercise may paradoxically diminish microbial diversity and exacerbate intestinal permeability, highlighting the importance of moderate intensity and duration. Exercise ameliorates depressive symptoms through multifaceted interactions with the gut microbiota, primarily via 4 interconnected pathways. First, exercise mitigates neuroinflammation by elevating anti-inflammatory SCFAs such as butyrate, which suppresses NF-κB signaling to attenuate microglial activation and oxidative stress in the hippocampus. Animal studies demonstrate that voluntary wheel running reduces hippocampal TNF‑α and IL-17 levels in stress-induced depression models, while fecal microbiota transplantation (FMT) from exercised mice reverses depressive behaviors by modulating the TLR4/NF‑κB pathway. Second, exercise regulates neurotransmitter dynamics by enriching GABA-producing Lactobacillus and Bifidobacterium, thereby counteracting neuronal hyperexcitability. Aerobic exercise also enhances the abundance of Lactobacillus plantarum and Streptococcus thermophilus, which facilitate 5-HT and dopamine synthesis. Clinical trials reveal that 12 weeks of moderate exercise increases fecal Coprococcus and Blautia abundance, correlating with improved 5-HT bioavailability and reduced depression scores. Third, exercise normalizes HPA axis hyperactivity by reducing cortisol levels and restoring glucocorticoid receptor sensitivity. In rodent models, chronic stress-induced corticosterone elevation is reversed by probiotic supplementation (e.g., Lactobacillus), which enhances endocannabinoid signaling and hippocampal neurogenesis. Furthermore, exercise upregulates brain-derived neurotrophic factor (BDNF) via microbial metabolites like butyrate, promoting histone acetylation and synaptic plasticity. FMT experiments confirm that exercise-induced microbiota elevates prefrontal BDNF expression, reversing stress-induced neuronal atrophy. Fourth, exercise reshapes microbial metabolic crosstalk, diverting tryptophan metabolism toward 5-HT synthesis instead of neurotoxic kynurenine derivatives. Butyrate inhibits indoleamine 2,3-dioxygenase (IDO), a key enzyme in the kynurenine pathway linked to depression. Concurrently, exercise-induced Akkermansia enrichment enhances mucin production, fortifies the gut barrier, and reduces LPS-driven neuroinflammation. Collectively, these mechanisms underscore exercise as a potent modulator of the microbiota-gut-brain axis, offering a holistic approach to alleviating depression through microbial and neurophysiological synergy. Current evidence supports exercise as a potent adjunct therapy for depression, with personalized regimens (e.g., aerobic, resistance, or yoga) tailored to individual microbiota profiles. However, challenges remain in optimizing exercise prescriptions (intensity, duration, and type) and integrating them with probiotics, prebiotics, or FMT for synergistic effects. Future research should prioritize large-scale randomized controlled trials to validate causality, multi-omics approaches to decipher MGB axis dynamics, and mechanistic studies exploring microbial metabolites as therapeutic targets. The authors advocate for a paradigm shift toward microbiota-centric interventions, emphasizing the bidirectional relationship between physical activity and gut ecosystem resilience in mental health management. In conclusion, this review underscores exercise as a multifaceted modulator of the gut-brain axis, offering novel insights into non-pharmacological strategies for depression. By bridging microbial ecology, neuroimmunology, and exercise physiology, this work lays a foundation for precision medicine approaches targeting the gut microbiota to alleviate depressive disorders.

Objective: To investigate the regional differences in the incidence of urothelial carcinoma among kidney transplant recipients between northern and southern China，so as to provide reference for early diagnosis of this disease. Methods: A comprehensive search was conducted across multiple databases，including CNKI，Wanfang，CBM，and PubMed，using the keywords “kidney transplantation” and “tumor” to collect clinical data from qualified kidney transplant centers.The latest and most complete literature data published by 17 transplant centers in northern China and 14 in southern China were included.Statistical analyses were performed to compare the incidence of post-transplant urothelial carcinoma and non-urothelial malignancies. Results: A total of 37 475 kidney transplant recipients were included，among whom 837 (2.23%) developed post-transplant malignancies，including urothelial carcinoma (366/837，43.73%)，non-urothelial carcinoma (444/837，53.05%)，and malignancies with unspecified pathology (27/837，3.23%).The incidence of malignancies was significantly higher in northern China than in southern China [(2.82±1.39)% vs. (1.67±0.83)%，P=0.011]，with a particularly pronounced difference in the incidence of urothelial carcinoma [(1.68±1.12)% vs. (0.32±0.32)%，P<0.001].No significant difference was observed in the incidence of non-urothelial carcinoma between the two regions [(1.11±0.56)% vs. (1.35±0.65)%，P=0.279].Additionally，female transplant recipients exhibited a higher incidence of malignancies than males in both regions (southern China：2.38% vs. 1.80%; northern China：8.93% vs. 2.52%). Conclusion: The incidence of urothelial carcinoma following kidney transplantation is significantly higher in northern China than in southern China，underscoring the importance of implementing regular tumor screening for kidney transplant recipients，particularly for female patients in northern China，to facilitate early diagnosis and timely intervention.

Objective To investigate and analyze clinical characteristics and related factors of elderly patients with type 2 diabetes mellitus (T2DM) and frailty. Methods A total of 310 elderly patients with T2DM admitted to the hospital from January 2023 to June 2024 were selected as the research subjects. Their general information and disease-related information was collected through questionnaires. The Fried Frailty Scale was used to evaluate frailty status, and the patients were divided into frailty group and non-frailty group based on the Fried Frailty Scale score. Factors related to T2DM with frailty in the elderly were analyzed. Results The incidence of frailty in this study was 31.61% (98/310), and those without frailty accounted for 68.39% (212/310). There were statistically significant differences between the two groups in terms of age, body mass index (BMI), Self-rating Depression Scale (SDS) score, number of chronic complications, glycosylated hemoglobin (HbA1c) level, hemoglobin level, Mini-Nutritional Assessment-Short Form (MNA-SF) score, and Charlson Comorbidity Index (CCI) score (P<0.05). Multivariate logistic regression analysis showed that age, HbA1c level, SDS score, MNA-SF score, and CCI score were risk factors for frailty in elderly patients with T2DM (P<0.05). Conclusion The incidence of frailty is relatively high in elderly patients with T2DM. It is influenced by factors such as age , SDS score , HbA1c level , MNA-SF score and CCI score, and deserves clinical attention.

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.

Aim To explore the effects of Lycium berry seed oil on Nrf2/ARE pathway and oxidative damage in testis of subacute aging rats. Methods Fifty out of 60 male SD rats, aged 8 weeks, were subcutaneously injected with 125 mg • kg"D-galactosidase in the neck for 8 weeks to establish a subacute senescent rat model. The presence of senescent cells was observed using P-galactosidase ((3-gal), while testicular morphology was examined using HE staining. Serum levels of testosterone (testosterone, T), follicle-stimulating hormone ( follicle stimulating hormone, FSH ) , luteinizing hormone ( luteinizing hormone, LH ) , superoxide dis-mutase ( superoxide dismutase, SOD ) , glutathione ( glutathione, GSH) and malondialdehyde ( malondial-dehyde, MDA) were measured through ELISA, and the expressions of factors related to aging, oxidative damage, and the Nrf2/ARE pathway were assessed via immunohistochemical analysis and Western blotting. Results After successfully identifying the model, the morphology of the testis was improved and the intervention of Lycium seed oil led to a down-regulation in the expression of [3-gal and -yH2AX. The serum levels of SOD, GSH, T, and FSH increased while MDA and LH decreased (P 0. 05) . Additionally, there was an up-regulated expression of Nrf2, GCLC, NQOl, and SOD2 proteins in testicular tissue ( P 0. 05 ) and nuclear expression of Nrf2 in sertoli cells. Conclusion Lycium barbarum seed oil may reduce oxidative damage in testes of subacute senescent rats by activating the Nrf2/ARE signaling pathway.

Quercetin can mediate the activation of mitogen-activated protein kinase(MAPK)signaling pathways to prevent osteoporosis(OP).This paper comprehensively discusses the interrelationship between MAPK and osteoporosis-related cells based on the latest domestic and international research.Additionally,it elucidates the research progress of quercetin in mediating the MAPK signaling pathway for OP prevention.The aim is to provide an effective foundation for the clinical prevention and treatment of OP and the in-depth development of quercetin.

Background::Oral anti-coagulants (OAC) are the intervention for the prevention of stroke, which consistently improve clinical outcomes and survival among patients with atrial fibrillation (AF). The main purpose of this study is to identify problems in OAC utilization among hospitalized patients with AF in China.Methods::Using data from the Improving Care for Cardiovascular Disease in China-Atrial Fibrillation (CCC-AF) registry, guideline-recommended OAC use in eligible patients was assessed.Results::A total of 52,530 patients with non-valvular AF were enrolled from February 2015 to December 2019, of whom 38,203 were at a high risk of stroke, 9717 were at a moderate risk, and 4610 were at a low risk. On admission, only 20.0% (6075/30,420) of patients with a diagnosed AF and a high risk of stroke were taking OAC. The use of pre-hospital OAC on admission was associated with a lower risk of new-onset ischemic stroke/transient ischemic attack among the diagnosed AF population (adjusted odds ratio: 0.54, 95% confidence interval: 0.43–0.68; P <0.001). At discharge, the prescription rate of OAC was 45.2% (16,757/37,087) in eligible patients with high stroke risk and 60.7% (2778/4578) in eligible patients with low stroke risk. OAC utilization in patients with high stroke risk on admission or at discharge both increased largely over time (all P <0.001). Multivariate analysis showed that OAC utilization at discharge was positively associated with in-hospital rhythm control strategies, including catheter ablation (adjusted odds ratio [OR] 11.63, 95% confidence interval [CI] 10.04–13.47; P <0.001), electronic cardioversion (adjusted OR 2.41, 95% CI 1.65–3.51; P <0.001), and anti-arrhythmic drug use (adjusted OR 1.45, 95% CI 1.38–1.53; P <0.001). Conclusions::In hospitals participated in the CCC-AF project, >70% of AF patients were at a high risk of stroke. Although poor performance on guideline-recommended OAC use was found in this study, over time the CCC-AF project has made progress in stroke prevention in the Chinese AF population.Registration::ClinicalTrials.gov, NCT02309398.

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.

Osteoarthritis (OA) is a chronic degenerative joint disease and the most common type of arthritis. It involves almost any joint and can lead to chronic pain and disability. In the late 19th century, Roentgen discovered X-rays, and then began to use radiotherapy to treat tumors. In the 1980s, Luckey thought that low-level radiation (LDRT) might be beneficial to biology, and it was gradually applied to the treatment of some diseases. This paper introduces the epidemiology, risk factors, clinical manifestations and treatment methods of OA, points out that the cartilage injury and the important effect of synovial inflammation in the pathogenesis of OA, namely when the homeostasis of articular cartilage are destroyed, synthetic metabolism and catabolism imbalances, cartilage cells damaged their breakdown products consumed by synovial cells. Synovial cells and synovial macrophages secrete proinflammatory cytokines, metalloproteinases and proteolytic enzymes, leading to cartilage matrix degradation and chondrocyte damage, which aggravates synovial inflammation and cartilage damage, forming a vicious cycle. The possible mechanism and clinical research progress of LDRT in alleviating OA are discussed. LDRT can regulate inflammatory response, inhibit the production of pro-inflammatory cytokines, and promote the production of anti-inflammatory cytokines, thereby achieving anti-inflammatory effect. Studies have shown that after irradiation, the expression of inducible nitric oxide synthase (iNOS) was decreased, the release of reactive oxygen species (ROS) and the production of superoxide were inhibited, the anti-inflammatory phenotype of macrophages was differentiated from M1 to M2, the inflammatory CD8⁺ T cells were transformed into CD4⁺ T cells, and the number of dendritic cells (DC) was significantly reduced. LDRT inhibit the production of proinflammatory factors in leukocytes, reduce their recruitment and adhesion, and down-regulate the expression levels of cell adhesion molecules such as selectin, intercellular adhesion molecule (ICAM) and vascular endothelial cell adhesion molecule (VCAM). LDRT can regulate endothelial cells, stimulate endothelial cells to produce a large amount of TGF-β1, reduce the adhesion of endothelial cells to peripheral blood mononuclear cells (PBMC), and contribute to the anti-inflammatory effect of LDRT. It also exerted anti-inflammatory effects by regulating mitochondrial growth differentiation factor 15 (GDF15). After low-level radiation, the MMP-13 (matrix metalloproteinases-13) and the ADAMTS5 (recombinant a disintegrin and metalloproteinase with thrombospondin-5) decreased, the Col2a1 (collagen type 2) increased in chondrocytes. In the existing clinical studies, most patients can achieve relief of joint pain and recovery of joint mobility after irradiation, and the patients have good feedback on the efficacy. The adverse reactions (acute reactions and carcinogenic risks) caused by LDRT in the treatment of OA are also discussed. During the treatment of OA, a few patients have symptoms such as redness, dryness or itching at the joint skin, and the symptoms are mild and do not require further treatment. Patients are thus able to tolerate more frequent and longer doses of radiotherapy. In general, LDRT itself has the advantages of non-invasive, less adverse reactions, and shows the effect of pain relief and movement improvement in the treatment of OA. Therefore, LDRT has a broad application prospect in the treatment of OA.