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.

Metabolic-associated fatty liver disease (MAFLD) and osteoporosis (OP) are two very common metabolic diseases. A growing body of experimental evidence supports a pathophysiological link between MAFLD and OP. MAFLD is often associated with the development of OP. Rutaecarpine (RUT) is one of the main active components of Chinese medicine Euodiae Fructus. Our previous studies have demonstrated that RUT has lipid-lowering, anti-inflammatory and anti-atherosclerotic effects, and can improve the OP of rats. However, whether RUT can improve both fatty liver and OP symptoms of MAFLD mice at the same time remains to be investigated. In this study, we used C57BL/6 mice fed a high-fat diet (HFD) for 4 months to construct a MAFLD model, and gave the mice a low dose (5 mg·kg^-1) and a high dose (15 mg·kg^-1) of RUT by gavage for 4 weeks. The effects of RUT on liver steatosis and bone metabolism were then evaluated at the end of the experiment [this experiment was approved by the Experimental Animal Ethics Committee of Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences (approval number: IMB-20190124D₃03)]. The results showed that RUT treatment significantly reduced hepatic steatosis and lipid accumulation, and significantly reduced bone loss and promoted bone formation. In summary, this study shows that RUT has an effect of improving fatty liver and OP in MAFLD mice.

Objective To explore the effects of empathy technology on emotion regulation, sleep improvement and quality of life improvement in patients with first benign paroxysmal positional vertigo (BPPV). Methods A total of 100 patients with the first BPPV in The Second Affiliated Hospital of Naval Medical University from December 2023 to November 2024 were selected, and were divided into control group and observation group by random number table method. The patients in both groups received routine reduction treatment and outpatient follow-up every 3 months after discharge, with a total of 4 follow-up visits. The patients in control group received routine health and psychological education; on this basis, the patients in observation group received empathy technique intervention before, during, after reposition and during follow-up. The emotion, sleep and quality of life scales were evaluated before intervention and at the 4th outpatient follow-up. The recurrence rates of BPPV within 1 year after discharge were compared between the two groups. Results One year after discharge, self-rating anxiety scale (SAS), self-rating depression scale (SDS), symptom checklist 90 (SCL-90) and Pittsburgh sleep quality index (PSQI) scores in the observation group were lower than those in the control group, and multi-dimensional quality of life scores in the observation group was higher than that in the control group. These differences were statistically significant (P＜0.05). The recurrence rate of BPPV in the observation group was lower than that in the control group 1 year after discharge (P＜0.05). Conclusions Empathy technology intervention can further improve emotion, sleep quality, and quality of life, but reduce disease recurrence rate in first BPPV patients receiving routine treatment and health education, so it can be widely used in clinical practice.

Duchenne Muscular Dystrophy （DMD） is an X-linked recessive inherited myopathy caused by a pathogenic variant of the amyotrophic protein gene. Currently， DMD patients have needs for treatment， education， and social integration at different stages of the disease. Faced with the multiple needs of DMD patients， based on the professional value and ethics of social work， the integrative orientation of social work， and the multiple roles of social workers， social workers can build a comprehensive service system centred on DMD patients and provide multiple services. Specifically， social work intervention paths can enhance the health and well-being of DMD patients and improve their quality of life by providing various services such as in-hospital and out-of-hospital health management， psychological support， resource links， empowerment， construction of social support networks， science popularization， and policy advocacy.

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.

ObjectiveTo characterize the seasonal patterns of influenza in Kunming City, Yunnan Province before, during, and after the COVID-19 pandemic, and provide scientific evidence for optimizing influenza prevention and control strategies. MethodsInfluenza-like illness (ILI) and etiological surveillance data for influenza from the 14^th week of 2010 to the 13^th week of 2024 in Kunming City of Yunnan Province were collected. Harmonic regression models were constructed to analyze the epidemic characteristics and seasonal patterns of influenza before (2010/2011‒2019/2020 influenza seasons), during (2020/2021‒2022/2023 influenza seasons), and after (2023/2024 influenza season) the COVID-19 pandemic. ResultsBefore the COVID-19 pandemic, influenza in Kunming City mainly exhibited an annual cyclic pattern without a significant semi-annual periodicity, peaking from December to February of the next year, with an epidemic duration of 20‒30 weeks. During the pandemic, influenza seasonality shifted, with an increase in semi-annual periodicity and an approximate one month delay in annual peaks. However, after the pandemic, the annual amplitude of influenza increased compared with that before the pandemic, and the epidemic duration extended by about one month. Although the annual peak largely reverted to the pre-pandemic levels, the annual peaks for different influenza subtypes/lineages had not fully recovered. ConclusionInfluenza seasonality in Kunming City underwent substantial alterations following the COVID-19 pandemic and has not yet fully reverted to pre-pandemic levels. Continuous surveillance on different subtypes/lineages of influenza viruses remains essential, and prevention and control strategies should be adjusted and optimized in a timely manner based on current epidemic trends.

As a serious cardiovascular disease, atherosclerosis (AS) causes chronic inflammation and oxidative stress in the body and poses a threat to human health. Lipoprotein-associated phospholipase A2 (Lp-PLA2) is a member of the phospholipase A2 (PLA2) family, and its elevated levels have been shown to contribute to AS. Lp-PLA2 is closely related to a variety of lipoproteins, and its role in promoting inflammatory responses and oxidative stress in AS is mainly achieved by hydrolyzing oxidized phosphatidylcholine (oxPC) to produce lysophosphatidylcholine (lysoPC). Moreover, macrophage apoptosis within plaque is promoted by localized Lp-PLA2 which also promotes plaque instability. This paper reviews those researches of Chinese medicine in treating AS via reducing Lp-PLA2 levels to guide future experimental studies and clinical applications related to AS.
Humans ; 1-Alkyl-2-acetylglycerophosphocholine Esterase ; Medicine, Chinese Traditional ; Atherosclerosis/drug therapy* ; Lipoproteins ; Plaque, Atherosclerotic ; Biomarkers

Aim To anticipate the mechanism of zuka- mu granules (ZKMG) in the treatment of bronchial asthma, and to confirm the projected outcomes through in vivo tests via using network pharmacology and molecular docking technology. Methods The database was examined for ZKMG targets, active substances, and prospective targets for bronchial asthma. The protein protein interaction network diagram (PPI) and the medication component target network were created using ZKMG and the intersection targets of bronchial asthma. The Kyoto Encyclopedia of Genes and Genomics (KEGG) and gene ontology (GO) were used for enrichment analysis, and network pharmacology findings were used for molecular docking, ovalbumin (OVA) intraperitoneal injection was used to create a bronchial asthma model, and in vivo tests were used to confirm how ZKMG affected bronchial asthma. Results There were 176 key targets for ZKMG's treatment of bronchial asthma, most of which involved biological processes like signal transduction, negative regulation of apoptotic processes, and angiogenesis. ZKMG contained 194 potentially active components, including quercetin, kaempferol, luteolin, and other important components. Via signaling pathways such TNF, vascular endothelial growth factor A (VEGFA), cancer pathway, and MAPK, they had therapeutic effects on bronchial asthma. Conclusion Key components had strong binding activity with appropriate targets, according to molecular docking data. In vivo tests showed that ZKMG could reduce p-p38, p-ERKl/2, and p-I

In recent years, with the rapid development of organ donation after citizen’s death and transplantation, central and local governments in China have successively released incentive policies. To protect the legitimate rights and interests of organ donors after citizen’s death and their families, current status of incentive policies for organ donation after citizen’s death was illustrated and analyzed from the perspective of ethics. Combining with the principles of justice, respect for autonomy, nonmaleficence and beneficence, the problems existing in the implementation of incentive policies for organ donation after citizen’s death were identified in China, such as lack of continuous psychological intervention in spiritual incentives, the misinterpretation of humanitarian assistance in practice and the impact of indirect economic incentives on individual donation autonomy, etc. Relevant countermeasures and suggestions were proposed at the government, society and individual levels, aiming to provide reference for improving the incentive policies for organ donation after citizen’s death and accelerate the development of organ donation in China.