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.

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 evaluate tolerability,safety and pharmacokinetics of JS026 and JS026-JS016 single dose intravenous infusion in healthy adults.Methods This phase 1,randomized,double-blind,placebo-controlled,dose-escalation study totally included 48 participants:32 healthy subjects were enrolled in JS026 single intravenous infusion groups and 16 healthy subjects were enrolled in JS026-JS016 groups.JS026 was sequentially administered from low dose to high dose(30-1 000 mg),with intravenous infusion of JS026 or placebo in JS026 single-dose groups,and intravenous infusion of JS026-JS016 or placebo in the combination drug groups.Blood was collected according to the time point designed for trial.Serum concentrations of JS026 and JS016 were determined by enzyme linked immunosorbnent assay(ELISA),and pharmacokinetics parameters were calculated by WinNonlin 8.2.The power model method was used to evaluate the linear analysis of dose and drug exposure.Results 47 subjects completed trial and 1 subject lost to follow-up.After a single intravenous injection of JS026 of 30 mg,100 mg,300 mg,600 mg,and 1 000 mg,mean Cmax were(9.47±1.53),(33.20±4.95),(96.10±13.70),(177.00±22.20)and(353.00±56.70)μg·mL-1,respectively;mean AUC0-∞ were(4 225.00±607.00),(1.78 × 104±3 268.00),(5.83 × 104±1 038.00),(1.07 × 105±152.00),(1.66 × 105±327.00)μg·h·mL-1,respectively;mean t1/2 of JS026 were 563-709 h.The Cmax and AUC0-∞ of JS026 were basically similar alone or in combination with JS016.The results of Power model showed that Cmax and AUC0-∞ increased approximately linearly with the increasing dose of JS026.Treatment emergent adverse event was not increasing when dose increased and most of adverse event associated with drugs were abnormal on laboratory tests and haematuria,thus JS026 and JS016 was well tolerated in all groups.Conclusion The single intravenous infusion of JS026 can almost be thought to be a linear relationship between the doses and drug serum exposure.JS016 had no significant effect on serum concentration of JS026 and JS026 was well tolerated and safe in healthy subjects within 30-1 000 mg.

Xiaoke formula (XKF) is a classic formula for the treatment of insulin resistance (IR), but there is still unclear on bioactive equivalent combinatorial components (BECC) of XKF. In this study, based on the previous research of our team, three components, berberine, astragaloside IV and chlorogenic acid, were selected as the BECC of XKF, and their efficacy and mechanism were investigated. A high-fat diet-induced IR mouse model was used to detect blood glucose, insulin sensitivity, lipid metabolism, immune & inflammatory factors, etc., and staining of pathology sections was used to detect histopathological changes. Network pharmacology was used to predict the potential targets and signaling pathways of XKF and its BECC, and the results of the network were verified by Western blot. The animal welfare and experimental procedures followed the regulations of the Laboratory Animal Ethics Committee of Beijing MDKN Biotech Company (MDKN-2023-019). The results showed that BECC, which was composed of berberine, astragaloside IV and chlorogenic acid in the ratio of the original formula of XKF, was comparable to XKF in improving the glycemia, insulin sensitivity, histopathological damage, dyslipidemia, and immuno-inflammation in IR mice. The results of network pharmacology and Western blot suggested that the BECC of XKF and XKF might alleviate IR by promoting the activation of hepatic phosphatidylinositol 3-kinase (PI3K), phosphorylation of protein kinase B (AKT), and inhibiting the expression of glucose-6-phosphate phosphatase (G6PC) and phosphoenolpyruvate carboxykinase 1 (PCK1), the key limiting enzymes of hepatic gluconeogenesis. The above results suggest that berberine, astragaloside IV and chlorogenic acid can be used as the potential BECC of XKF to improve IR, and can regulate lipid metabolism, immuno-inflammation, and promote hepatic PI3K/AKT signaling to inhibit hepatic gluconeogenesis, regulate glucose homeostasis, and improve IR in mice.

This experiment aims to study the taste-masking effects of different kinds of corrigent used individually and in combination on ibuprofen oral solution, in order to optimize the taste-masking formulation. Firstly, a wide range of corrigent and the mass fractions were extensively screened using electronic tongue technology. Subsequently, a combination of sensory evaluation, analytic hierarchy process (AHP)-fuzzy mathematics evaluation, and Box-Behnken experimental design were employed to comprehensively assess the taste-masking effects of different combinations of corrigent on ibuprofen oral solution, optimize the taste-masking formulation, and validate the results. The study received ethical approval from the Review Committee of the Beijing University of Chinese Medicine (ethical code: 2024BZYLL0102). The results showed that corrigent fractions and types were screened separately through single-factor experiments. Subsequently, a Box-Behnken response surface design combined with AHP and fuzzy mathematics evaluation was used to fit a functional model: Z = 688.310 11 - 3 023.722 22X₁ - 11.477 00X₂ + 62.721 67X₃ + 14.600 00X₁X₂ - 179.666 67X₁X₃ - 3.152 00X₂X₃ + 4 031.111 11X₁² + 0.525 28X₂² + 9.772 00X₃². This model is stable and reliable, determining the optimal taste-masking formulation to be 3.9 g·L^-1 of stevioside, 100 g L^-1 of xylitol, and 30 g L^-1 of methyl-β-cyclodextrin. The comprehensive score of the verification test is 88.14, with a relative RSD of 0.39%, indicating the feasibility of this model. This study achieved the improvement of the taste of ibuprofen oral solution through a combination of objective and subjective methods. Three types of corrigent were identified for enhancing drug taste, leading to the selection of the optimal taste-masking formulation for ibuprofen oral solution. This significantly enhanced the taste of the original formulation, improved patient compliance, and offered a new approach to mitigating the undesirable taste of formulations.

Objective To investigate the protective effect and mechanism of acellular nerve allografts(ANA)combined with electroacupuncture on spinal ganglia in rats with sciatic nerve injury(SNI).Methods Totally 50 male adult SD rats were randomly selected for this experiment.Ten rats were prepared for the ANA.Forty male SD rats were randomly divided into normal group,model group,ANA group and combinational group,with 10 rats in each group.The SNI model was established by cutting off the nerves 10 mm at the 5 mm on the inferior border of piriformis after separating the right sciatic nerves.The rats in the ANA group were bridged with ANA to the two broken ends of injured nerves.The rats in the combinational group were treated with electroacupuncture 2 days after ANA bridging,Huantiao(GB30)and Yanglingquan(GB34)were performed as the acupuncture points,each electroacupuncture lasted 15 minutes and 7 days as a course of treatment,4 courses in all.Sciatic nerve conduction velocity was measured by electrophysiology to evaluate the regeneration of damaged axons.Morphology of spinal ganglia was observed by Nissl staining.The expression of nerve growth factor(NGF)and brain-derived neurotrophic factor(BDNF)were detected by Western blotting and immunofluorescent staining.Results Compared with the normal group,the sciatic nerve conduction velocity in model group decreased significantly(P<0.01),Nissl bodies in neurons of spinal ganglia were swollen and dissolved,with incomplete structure and the number decreased dramatically(P<0.01),while the level of NGF and BDNF also decreased significantly(P<0.01).Compared with the model group,the sciatic nerve conduction velocity in ANA and combinational groups strongly increased(P<0.01),the damage of Nissl bodies in neurons of spinal ganglia reduced and the number obviously increased(P<0.01),the level of NGF and BDNF increased considerably(P<0.01).Compared with the ANA group,the sciatic nerve conduction velocity in combinational group increased significantly(P<0.01),the morphology of Nissl bodies in neurons of spinal ganglia were more regular and the number increased(P<0.01),moreover,the level of NGF also increased significantly(P<0.01).Conclusion ANA combined with electroacupuncture can enhance the sciatic nerve conduction velocity,improve the morphology of neurons in spinal ganglia and play a protective effect on spinal ganglia.The mechanism can be related to the higher expression of NGF and BDNF proteins,especially the expression of NGF protein.

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.

Nuclear factor E2-related factor 2 (Nrf2) is a major regulator of redox homeostasis in cells, and Nrf2 signaling pathway has anti-inflammatory, anti-oxidative stress, and anti-fibrosis effects while plays an important role in wound healing and pathological scar formation and progression. This article reviewed the related research regarding the effect of oxidative stress and Nrf2 signaling pathway on pathological scars, furthermore, it investigated the relationship between Nrf2 signaling pathway, oxidative stress and pathological scars, providing a new perspective for the study of mechanism and clinical prevention and treatments of pathological scars.

Nuclear factor E2-related factor 2 (Nrf2) is a major regulator of redox homeostasis in cells, and Nrf2 signaling pathway has anti-inflammatory, anti-oxidative stress, and anti-fibrosis effects while plays an important role in wound healing and pathological scar formation and progression. This article reviewed the related research regarding the effect of oxidative stress and Nrf2 signaling pathway on pathological scars, furthermore, it investigated the relationship between Nrf2 signaling pathway, oxidative stress and pathological scars, providing a new perspective for the study of mechanism and clinical prevention and treatments of pathological scars.

Objective To investigate the effect of evodiamine on renal fibrosis in rats with chronic renal failure(CRF)by regulating sphingosine kinase 1(SphK1)/S1P signaling pathway.Methods CRF rat model was established by feeding 0.5％adenine diet.The rats after modeling were randomly divided into model group,evodiamine low-and high-dose groups,Niaoduqing Granules group and evodiamine high-dose+K6PC-5(SphK1 activator)group.At the end of intervention,the levels of 24-hour urinary protein(24 h-UTP),serum urea nitrogen(BUN)and serum creatinine(SCr)were detected.The levels of monocyte chemoattractant protein 1(MCP-1)and interleukin 6(IL-6)in serum of rats were detected by enzyme-linked immunosorbent assay(ELISA).The pathological changes of renal tissue and collagen volume fraction(CVF)were observed by hematoxylin-eosin(HE)and Masson staining.The mRNA expressions of transforming growth factor β1(TGF-β1)and type Ⅳcollagen(COL-Ⅳ)in renal tissue were detected by real-time quantitative polymerase chain reaction(qRT-PCR).The protein expressions of SphK1 and S1P in renal tissue were detected by Western Blot.Results Compared with the normal group,the model group showed obvious inflammatory infiltration and collagen fiber deposition,the number of glomeruli decreased in renal tissue,and the expression levels of SCr,BUN,24h-UTP,IL-6,MCP-1,CVF,mRNA expressions of TGF-β1 and COL-Ⅳ,protein expessions of SphK1 and S1P were significantly increased(P＜0.05).Compared with the model group,the histopathological changes of the low-dose and high-dose evodiamine groups and the Niaoduqing Granules group was improved,and the expression levels of SCr,BUN,24 h-UTP,IL-6,MCP-1,CVF,mRNA expressions of TGF-β1 and COL-Ⅳ,protein expessions of SphK1 and S1P were significantly decreased.For intergroup comparison of each index,there were differences between the low-dose evodiamine group and the high-dose evodiamine group(P＜0.05),but there was no significant difference between the high-dose evodiamine group and the Niaoduqing Granules group(P＞0.05).Compared with the high-dose evodiamine group,the histopathological changes of the high-dose evodiamine+K6PC-5 group was further aggravated,and the improvement effect of all indexes were reversed(P＜0.05).Conclusion Evodiamine improves renal fibrosis in CRF rats by inhibiting SphK1/S1P signaling pathway.