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.

As China accelerates its efforts in deep space exploration and long-duration space missions, including the operationalization of the Tiangong Space Station and the development of manned lunar missions, safeguarding astronauts’ physiological and cognitive functions under extreme space conditions becomes a pressing scientific imperative. Among the multifactorial stressors of spaceflight, microgravity emerges as a particularly potent disruptor of neurobehavioral homeostasis. Dopamine (DA) plays a central role in regulating behavior under space microgravity by influencing reward processing, motivation, executive function and sensorimotor integration. Changes in gravity disrupt dopaminergic signaling at multiple levels, leading to impairments in motor coordination, cognitive flexibility, and emotional stability. Microgravity exposure induces a cascade of neurobiological changes that challenge dopaminergic stability at multiple levels: from the transcriptional regulation of DA synthesis enzymes and the excitability of DA neurons, to receptor distribution dynamics and the efficiency of downstream signaling pathways. These changes involve downregulation of tyrosine hydroxylase in the substantia nigra, reduced phosphorylation of DA receptors, and alterations in vesicular monoamine transporter expression, all of which compromise synaptic DA availability. Experimental findings from space analog studies and simulated microgravity models suggest that gravitational unloading alters striatal and mesocorticolimbic DA circuitry, resulting in diminished motor coordination, impaired vestibular compensation, and decreased cognitive flexibility. These alterations not only compromise astronauts’ operational performance but also elevate the risk of mood disturbances and motivational deficits during prolonged missions. The review systematically synthesizes current findings across multiple domains: molecular neurobiology, behavioral neuroscience, and gravitational physiology. It highlights that maintaining DA homeostasis is pivotal in preserving neuroplasticity, particularly within brain regions critical to adaptation, such as the basal ganglia, prefrontal cortex, and cerebellum. The paper also discusses the dual-edged nature of DA plasticity: while adaptive remodeling of synapses and receptor sensitivity can serve as compensatory mechanisms under stress, chronic dopaminergic imbalance may lead to maladaptive outcomes, such as cognitive rigidity and motor dysregulation. Furthermore, we propose a conceptual framework that integrates homeostatic neuroregulation with the demands of space environmental adaptation. By drawing from interdisciplinary research, the review underscores the potential of multiple intervention strategies including pharmacological treatment, nutritional support, neural stimulation techniques, and most importantly, structured physical exercise. Recent rodent studies demonstrate that treadmill exercise upregulates DA transporter expression in the dorsal striatum, enhances tyrosine hydroxylase activity, and increases DA release during cognitive tasks, indicating both protective and restorative effects on dopaminergic networks. Thus, exercise is highlighted as a key approach because of its sustained effects on DA production, receptor function, and brain plasticity, making it a strong candidate for developing effective measures to support astronauts in maintaining cognitive and emotional stability during space missions. In conclusion, the paper not only underscores the centrality of DA homeostasis in space neuroscience but also reflects the authors’ broader academic viewpoint: understanding the neurochemical substrates of behavior under microgravity is fundamental to both space health and terrestrial neuroscience. By bridging basic neurobiology with applied space medicine, this work contributes to the emerging field of gravitational neurobiology and provides a foundation for future research into individualized performance optimization in extreme environments.

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.

The objective of this study was to analyze the effects on cell viability, apoptosis, and cell cycle of non-small cell lung cancer (NSCLC) A549 cells after intervention with Agrimonia pilosa (AP) and investigate Agrimonia pilosa anti-tumor activity in vitro. Meanwhile, liquid chromatography mass spectrometry (LC-MS) metabolomics technology was used to analyze the changes of cellular metabolites and metabolic pathways. The results of this study will provide a theoretical and experimental basis for investigating the mechanism of the effect of Agrimonia pilosa on non-small cell lung cancer A549 cells. The results showed that the cell nucleus of A549 cells crumpled and apoptosis occurred with the increase of drug concentration. The survival rate of the cells decreased, and the inhibition rate reached 21.5% and 91.74% under the low and high dose conditions, respectively. Lactate dehydrogenase (LDH) content increased (P < 0.05). Metabolomics results showed significant differences in metabolism between groups, thirty-three distinct metabolites including LysoPC(24:0/0:0), LysoPC(17:0/0:0) and PC(O-40:5) were deduced. The pathway enrichment showed that the Agrimonia pilosa plays an anti-tumor role mainly by regulating the metabolism of glycerophosphate and purine in A549 cells, in which the effect on glycerophosphate metabolism pathway was most significant. The results of combined pharmacodynamics suggested that Agrimonia pilosa might induce apoptosis and inhibit the growth of A549 cells by regulating LysoPC(24:0/0:0), LysoPC(17:0/0:0) and PC(O-40:5) metabolites in A549 cells.

Objective To compare the diagnostic value of ultrasonography and CT in acute appendicitis.Methods A retrospective analysis was conducted on 279 patients who were diagnosed with acute appendicitis and followed emergency surgery.Patients were divided into different subgroups based on postoperative pathological results and body mass index(BMI),and the pathological results were used as the gold standard to analyze whether there were differences in the diagnostic accuracy of ultrasonography and CT examination for acute appendicitis.Results A total of 279 patients with confirmed acute appendicitis,with 64 cases of simple appendicitis,127 cases of suppurative appendicitis,and 88 cases of gangrenous appendicitis according to pathological classification.The diagnostic accuracy of ultrasonography was 68.75%(44/64),73.22%(93/127),and 81.81%(72/88),respectively.The diagnostic accuracy of CT was 71.87%(46/64),82.67%(105/127),and 90.90%(80/88),respectively.There was no statistically significant difference in diagnostic accuracy between the two examinations(P>0.05).Subgroup analysis based on patient BMI showed that there was no difference in diagnostic accuracy of the two examinations for patients with normal BMI(P>0.05),while for overweight and obese patients,the diagnostic accuracy of CT was better than that of ultrasonography,with a statistical difference(P<0.05).Conclusion There is no difference in the diagnostic accuracy of ultrasonography and CT examinations for acute appendicitis of different pathological types.But for overweight and obese acute appendicitis patients,the diagnostic accuracy of CT examination is superior to ultrasonography.

Vitamin D is a unique fat-soluble vitamin that plays an indispensable role in human health. It exists in various forms, the most significant being vitamin D₂ (derived from plant sources) and vitamin D₃ (synthesized naturally in human skin upon exposure to sunlight). Vitamin D’s primary function is to facilitate the absorption of calcium and phosphorus, which are crucial for maintaining healthy bones. Beyond its role in bone health, vitamin D significantly influences the immune system, muscle function, cardiovascular health, and the regulation of brain functions. A deficiency in vitamin D can lead to various chronic diseases such as rickets, osteoporosis, decreased immunity, increased risk of mental disorders, and cancers. The synthesis of vitamin D in the human body, both peripherally and centrally, relies on sunlight exposure, dietary sources, and various supplements. As a neuroactive steroid, vitamin D impacts both the physiological and pathological processes of the nervous system and plays a key role in brain health. It profoundly affects the brain by regulating neurotransmitter synthesis and maintaining intracellular calcium balance. As an essential chemical molecule, vitamin D participates in complex signal transduction pathways, impacting neurotransmitter functions and synaptic plasticity. Vitamin D’s role in regulating dopamine (DA)—a neurotransmitter critical for motivation, reward perception, and other higher cognitive functions—is particularly noteworthy. Recent studies have revealed that vitamin D not only promotes the synthesis of DA but also plays a role in regulating DA levels within the brain. It exerts neuroprotective effects on DA neurons through anti-inflammatory, antioxidant actions, and neurotrophic support, thereby creating an optimal environment for DA neurons, influencing neuronal structure, and affecting the movement of calcium ions within nerve cells, positively impacting the overall health and functionality of the DA system. Furthermore, vitamin D can regulate the synthesis and release of DA, thus affecting the signal transmission of various DA neural projection pathways in the brain. This function is vital for understanding the complex interactions between neural mechanisms and their effects on key behaviors and cognitive functions. This review aims to delve deeply into the synthesis, metabolism, and pathways of vitamin D’s action, especially its regulatory mechanisms on DA neurons. Through this exploration, this article seeks to provide a solid theoretical foundation and research framework for a deeper understanding of vitamin D’s role in motivation and reward behaviors. This understanding is crucial for appreciating the broader significance of vitamin D in the fields of neuroscience and neurology. In summary, research and discoveries regarding vitamin D’s impact on the nervous system highlight its importance in neural health and function. These insights not only enhance our understanding of the complex workings of the nervous system but also open new avenues for the prevention and treatment of neurological diseases. The exploration of vitamin D’s multifaceted roles offers promising prospects for developing new therapeutic strategies, underscoring the compound’s potential in addressing a range of neural dysfunctions and diseases. As research continues to evolve, the profound implications of vitamin D in the field of neurology and beyond become increasingly apparent, marking it as a key target for ongoing and future scientific inquiry.

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.

Objective To study the pharmacokinetics and bioequivalence of two formulations of rasagiline mesylate tablets in healthy subjects under fasting and fed conditions.Methods The two-period,two-sequence,crossover study design was adopted in the fasting study.Thirty-six subjects were enrolled and given either test preparation or reference preparation 1 mg respectively in two periods.After collecting plasma samples,the plasma concentration of rasagiline was determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS)and the bioequivalence was evaluated using the average bioequivalence(ABE)method.The four-period,two-sequence,fully replicate crossover study design was adopted in the fed study.Forty-eight subjects were enrolled and given the test preparation or the reference preparation at a dose of 1 mg twice respectively in four periods.According to the degree of intra-individual variation of Cmax,AUC0-t and AUC0-∞,the equivalence was evaluated using the reference-scaled average bioequivalence and ABE method,respectively.Results In the fasting study,the pharmacokinetic parameters of rasagiline of the test and reference preparation were as follow:Cmax were(9.70±3.14)and(9.62±3.85)ng·mL-1,AUC0-t were(6.03±1.47)and(6.02±1.95)ng·h·mL-1,AUC0-∞ were(6.13±1.51)and(6.12±1.97)ng·h·mL-1.The 90％confidence interval(CI)of the geometric mean ratio(GMR)were 94.11％-118.06％,99.22％-107.74％and 99.16％-107.44％for Cmax,AUC0-t and AUC0-∞,respectively,which were within the acceptance criteria of 80.00％-125.00％.In the fed study,the pharmacokinetic parameters of rasagiline of the test and reference preparation were as follow:Cmax were(3.00±1.92)and(3.52±1.77)ng·mL-1,AUC0_t were(5.02±1.20)and(5.06±1.20)ng·h·mL-1,AUC0-∞ were(5.11±1.23)and(5.14±1.22)ng·h·mL-1.The 90％CI of GMR were 96.99％-101.19％and 97.17％-101.41％for AUC0-t and AUC0-∞,which were within the acceptance criteria of 80.00％-125.00％.The 95％upper confidence bound of Cmax for were less than"0",and the point estimate of GMR were within the acceptance criteria of 80.00％-125.00％.The incidence of adverse events in fasting and fed studies was 22.86％and 22.92％,respectively,and all adverse events were moderate to mild.Conclusion The two rasagiline mesylate tablets were bioequivalent,and both the formulations were well tolerated.

Objective:To evaluate the value of 18F-FDG PET metabolic parameters in predicting histopathological grade of soft tissue sarcoma (STS). Methods:From December 2012 to December 2021, 51 patients (26 males, 25 females, age range: 32-84 years) who underwent 18F-FDG PET/CT imaging before treatment and confirmed STS pathologically in the First Affiliated Hospital of Dalian Medical University were retrospectively collected. 18F-FDG PET metabolic parameters SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and intertumoral FDG uptake heterogeneity (IFH) were measured. Kruskal-Wallis rank sum test was used to analyze the differences in metabolic parameters among different groups and Spearman rank correlation analysis was used to analyze the correlation of each metabolic parameter and histological grade. Logistic regression was used to screen and construct the prediction model for high-grade STS. ROC curve was plotted and Delong test was used to analyze the differences among AUCs. Results:The metabolic parameters SUV max, MTV, TLG and IFH were significantly different among French Federation of Cancer Centers Sarcoma Group (FNCLCC)Ⅰ( n=8), Ⅱ( n=10) and Ⅲ ( n=33) grade groups ( H values: 16.24, 10.52, 19.29 and 16.99, all P<0.05), and each metabolic parameter was positively correlated with histological grade ( rs values: 0.58, 0.45, 0.52, and 0.62, all P<0.05). Multivariate logistic regression analysis showed that SUV max(odds ratio ( OR)=1.27, 95% CI: 1.06-1.51, P=0.009) and IFH ( OR=6.83, 95% CI: 1.44-32.27, P=0.015) were independent risk indicators for high-grade STS. The prediction model constructed by combining SUV max and IFH had better diagnostic efficacy for differentiating high-grade STS with the AUC of 0.93, and the sensitivity of 93.9%(31/33) and the specificity of 16/18, respectively. The AUC of prediction model was significant different from SUV max, MTV, TLG and IFH (AUCs: 0.81, 0.78, 0.86 and 0.85; z values: 2.69, 2.53, 1.94 and 1.97, all P<0.05). Conclusions:The metabolic parameters SUV max, MTV, TLG and IFH are valuable predictors for histological grade of STS. The combination of SUV max and IFH may be a more meaningful method than using each of the above metabolic parameters alone.

Objective:To investigate the clinicopathological features and the prognosis of IgA nephropathy (IgAN) in children with massive proteinuria.Methods:It was a retrospective cohort study. Clinical data of IgAN children with massive proteinuria admitted to the Department of Nephrology, Children's Hospital Affiliated to Capital Institute of Pediatrics from January 2008 to December 2021 were retrospectively analyzed. Patients were divided into effective group and ineffective group according to whether urine protein turned negative after 6 months of initial treatment. The follow-up endpoint event was defined as a reduction in proteinuria of less than 50% or end-stage renal disease (ESRD) achievement. MedCalc software was used to perform Kaplan-Meier survival analysis, and Log-rank test was used to compare the difference of renal survival between the two groups.Results:A total of 127 patients were diagnosed as primary IgAN by renal biopsy, of whom 57 patients with IgAN showed massive proteinuria. These 57 IgAN patients with macroproteinuria accounted for 44.9% of the total IgAN patients and were enrolled in the study. Among the 57 cases, 33 cases (57.9%) were Lee's grade Ⅲ, 11 cases (19.3%) were below Lee's grade Ⅲ, and 13 cases (22.8%) were above Lee's grade Ⅲ. The follow-up time was 4.0 (3.0,5.8) years. In the initial treatment, among 57 patients, 46 (80.7%) were effective (effective group) and 11 (19.3%) were ineffective (ineffective group). Compared with the effective group, the ineffective group had a higher proportion of concurrent AKI at the onset of disease and longer recovery time of renal function, with significant difference (7/11 vs. 13/46, χ2=4.878, P=0.027). Compared with the effective group, the proportion of Lee grade Ⅲ or above was higher in the ineffective group, and the difference was statistically significant (5/11 vs. 8/46, χ2=3.971, P=0.046). There were significant differences in endocapillary hypercellularity (E1), segmental glomerulosclerosis or adhesion (S1) and cellular/fibrocellular crescents (C2) of Oxford classification between IgAN children with Lee grade Ⅲ or below and those over Lee grade Ⅲ (11/13 vs. 20/44, χ2=6.204, P=0.013; 12/13 vs. 17/44, χ2=11.566, P=0.001; 9/13 vs. 7/44, χ2=14.131, P=0.001). Among 57 patients, endpoint events occurred in 2 patients who both were urinary protein unmitigated, and none of the children progressed to ESRD. There was no significant difference in cumulative renal survival between the two groups by Kaplan-Meier survival analysis and Log-rank test ( χ2=0.537, P=0.460) after addition of calcineurin inhibitors (CNIs) to the initial treatment ineffective group. Conclusions:Macroproteinuria is the prominent manifestation of IgAN in children. The pathological type is mainly Lee grade Ⅲ. Children with macroproteinuria have a good prognosis in the short and medium term after active treatment. For IgAN with macroproteinuria that does not respond well to initial treatment, AKI is more common at onset, and renal function recovery time is longer. The application of CNIs may have a certain effect on improving the renal outcome of IgAN with massive proteinuria.