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.

Five compounds were isolated and purified from the water extract of Elaeagnus oxycarpa Schlechtend leaf by multi-dimensional reversed-phase preparative liquid chromatographic system based on the separation and enrichment model. Their structures were identified by spectral analysis such as NMR, MS, UV, IR and by comparison with literature information as 2,4(1H,3H)-pyrimidinedione (1), elaeagnussugarester B (2), elaeagnussugarester A (3), elaeagnussugarester C (4), gallic acid (5). Compounds 2-4 are new compounds, compound 1 was isolated from Elaeagnus oxycarpa Schlechtend for the first time. The antioxidant and anti-tyrosinase activities of these compounds were evaluated by using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical method, the 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) free radical method, the potassium ferricyanide reduction method and the colorimetric method with L-tyrosine as substrate. The results showed that compounds 2-5 have good antioxidant activities and inhibitory effect on tyrosinase. Compound 4 exhibited the most strong antioxidant activities, with IC₅₀ = 3.59 ± 0.06 μmol·L^-1 for DPPH free radical scavenging ability, IC₅₀ = 10.04 ± 0.20 μmol·L^-1 for ABTS free radical scavenging ability, and total reduction capacity of compound 4 was better than vitamin C respectively. Compound 3 possessed better inhibitory effect on tyrosinase with IC₅₀ = 0.25 ± 0.06 mmol·L^-1.

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 analyze the adverse treatment outcome status and spatio temporal characteristics of pulmonary tuberculosis cases among students in Qinghai Province, providing a reference basis for pulmonary tuberculosis prevention and control in schools. Methods: The data of student pulmonary tuberculosis cases during 2013-2023 in Qinghai Province were obtained through the "National Tuberculosis Management Information System", and the treatment outcome was retrospectively analyzed. The Joinpoint model was applied to analyze the adverse outcome rate trend. Global and local spatial autocorrelation analysis, and spatiotemporal scan cluster analysis were conducted on the adverse outcome rate of pulmonary tuberculosis among students in Qinghai Province. Results: During 2013-2023, 488 cases of adverse outcomes were reported among 6 155 students with pulmonary tuberculosis in Qinghai Province, with an adverse outcome rate of 7.93%. The reporting adverse outcome rate of pulmonary tuberculosis among students showed a downward trend from 2013 to 2023 (APC=-16.20, t =-3.89, P <0.05). The results of spatial autocorrelation showed that the adverse outcome rate of pulmonary tuberculosis was Moran s I >0 among students in Qinghai Province. Among them, there was a spatially positive correlation in the adverse outcome rate of pulmonary tuberculosis among students in 2020, 2021 and 2022(all Z >1.96, all P <0.05). The results of clustering and outlier analysis in local spatial autocorrelation showed that the areas with high high aggregation were mainly concentrated in Yushu Prefecture(Zhiduo County, Zaduo County, Nangqian County, Yushu City), Huangnan Prefecture (Zeku County, Henan County) and Hainan Prefecture (Tongde County). The low low concentration areas were distributed in Haidong City, Xining City, Haibei Prefecture (Gangcha County, Qilian County), Haixi Prefecture (Tianjun County, Ulan County), Hainan Prefecture (Gonghe County, Guide County) and Huangnan Prefecture (Tongren City, Jianzha County). The spatio temporal scanning showed that a total of two possible aggregation areas had been detected. Among them, the first level aggregation area composed of 10 counties and districts in Yushu Prefecture and Guoluo Prefecture of Qinghai Province, and the cluster radius was 658.09 km, the RR was 10.58 , and the LLR was 305.91; the second level aggregation area was composed of 16 counties and districts in Hainan Prefecture, Haixi Prefecture, Huangnan Prefecture and Guoluo Prefecture, and the cluster radius was 407.02 km, the RR was 9.83, and the LLR was 152.48 (both P <0.05). Conclusions The reporting rates of adverse treatment outcome of pulmonary tuberculosis cases among students in Qinghai Province remain relatively high and unevenly distribute throughout the province. Supervision should be strengthened to improve cases compliance,and to reduce student pulmonary tuberculosis adverse treatment outcomes incidence.

ObjectiveTo investigate the relationship between the non-fasting triglyceride and glucose （TyG） index and hyperglycemia in pregnancy during the third trimester in high altitudes. MethodsThis study selected clinical and laboratory data of 774 Tibetan singleton pregnant women who delivered at Chaya People's Hospital of Qamdo city in Xizang autonomous region， from January 2023 to April 2025. The non-fasting TyG index was calculated from non-fasting triglyceride （TG） and random plasma glucose （PG）. Based on the tertiles of the non-fasting TyG index values， the individuals were split into three groups （corresponding to non-fasting TyG index of 8.89 and 9.21， respectively）. The baseline clinical characteristics， lipid levels and the occurrence of developing hyperglycemia in pregnancy were compared among the three groups. Statistical analyses were performed using ANOVA， Kruskal-Wallis H test， Chi-square test， or Fisher exact test and the relationship between the non-fasting TyG index and hyperglycemia in pregnancy were examined using multivariate logistic regression models and curve fitting. ResultsA total of 774 Tibetan singleton pregnant women were included， with a average age of 27.3 ± 6.1 years， a pre-delivery body mass index （Pre-BMI） of （25.2±2.3）kg/m² ， a proportion of 26.7% （207/774） primigravid women， the mean non-fasting TyG index was 9.1 ± 0.4。Thirty pregnant women were diagnosed with hyperglycemia in pregnancy， with a detection rate of 3.9% （30/774）. Statistically significant differences in serum total cholesterol （TC）， TG， low-density lipoprotein cholesterol （LDL-C） and high-density lipoprotein cholesterol （HDL-C） levels were identified when comparing different non-fasting TyG groups （all P values <0.05）. Subsequent trend test analysis indicated that the levels of TC， TG， LDL-C， and PG gradually increased with elevated the non-fasting TyG index （ F_{trend TC}=95.61， P<0.001； F_{trend TG}=1 051.91， P<0.001； F_{trend LDL-C} = 97.20， P < 0.001； F_{trend TG}=195.20； P<0.001）. After adjustment for maternal age， pre-delivery BMI， altitude， TC， LDL-C， and HDL-C， multivariate Logistic regression models revealed independent positive associations between non-fasting TyG index and hyperglycemia in pregnancy （Model 1： OR=2.72， 95% CI： 1.13-6.53， P=0.026； Model 2： OR=2.56， 95% CI： 1.01-6.50， P=0.048； Model 3： OR=2.72， 95% CI： 1.06-6.97， P=0.037； Model 4： OR=4.02， 95% CI： 1.42-11.40， P=0.009） and the incident of hyperglycemia in pregnancy showed an increasing tendency as increasing with the non-fasting TyG index， however， this association did not statistical significance （P _trend >0.05）. Curve fitting by restricted cubic splines （RCS） were used to assess linearity between non-fasting TyG and hyperglycemia in pregnancy， and there was a linear dose-response relationship between non-fasting TyG and hyperglycemia in pregnancy （P _{for non-linear} = 0.515）. ConclusionNon-fasting TyG index in the third trimester is a risk factor for hyperglycemia in pregnancy among the Tibetan singleton pregnant women at high altitudes and there was a possible linear dose-response relationship between the non-fasting TyG index and hyperglycemia in pregnancy.

This article discussed the evolution of the traditional preparation process of Pinelliae Rhizoma Fermentata.The production methods for Pinelliae Rhizoma Fermentata in Song Dynasty include cake-making of Pinelliae Rhizoma together with ginger juice and fermentation after cake-making,and the former method of cake-making was the mainstream.The process technology in Jin and Yuan Dynasties inherited from that in Song Dynasty,and the application of Pinelliae Rhizoma Fermentata had certain limitations.The medical practitioners of Ming Dynasty elucidated the mechanism of processing of Pinelliae Rhizoma Fermentata,and proposed the view of"sliced Pinelliae Rhizoma being potent while fermented Pinelliae Rhizoma being mild".In the Ming Dynasty,LI Shi-Zhen defined the cake-making process and fermentation process for Pinelliae Rhizoma,and HAN Mao's Han Shi Yi Tong(Han's Clear View of Medicine)contained five prescriptions for the processing of Pinelliae Rhizoma Fermentata,which had the epoch-making signficance in the expansion of prescriptions for the processing of Pinelliae Rhizoma Fermentata.In the Qing Dynasty,HAN Fei-Xia's ten methods for making Pinelliae Rhizoma Fermentata were summarized on the basis of the methods recorded in Han Shi Yi Tong,and at that time,the processing of Pinelliae Rhizoma Fermentata and the preparation of Massa Medicata Fermentata interacted with each other.After the founding of the People's Republic of China,the local experience in the preparation of Pinelliae Rhizoma Fermentata was deeply influenced by the methods in the Qing Dynasty,and the local preparation technical standards gradually became the same.Moreover,this article also explored the issues of the importance of"Pinelliae Rhizoma"and"ingredients for fermentation",the pre-treatment of Pinelliae Rhizoma,the distinction between cake-making process and fermentation process for Pinelliae Rhizoma,the amount of flour added as well as the timing of adding,the addition of Massa Medicata Fermentata powder,the role of Alum in Pinelliae Rhizoma Fermentata and so on.

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 investigate the disease spectrum and pathogenic genes of inherited metabolic disorder(IMD)among neonates in Gansu Province of China.Methods A retrospective analysis was conducted on the tandem mass spectrometry data of 286 682 neonates who received IMD screening in Gansu Provincial Maternal and Child Health Hospital from January 2018 to December 2021.A genetic analysis was conducted on the neonates with positive results in tandem mass spectrometry during primary screening and reexamination.Results A total of 23 types of IMD caused by 28 pathogenic genes were found in the 286 682 neonates,and the overall prevalence rate of IMD was 0.63‰(1/1 593),among which phenylketonuria showed the highest prevalence rate of 0.32‰(1/3 083),followed by methylmalonic acidemia(0.11‰,1/8 959)and tetrahydrobiopterin deficiency(0.06‰,1/15 927).In this study,166 variants were identified in the 28 pathogenic genes,with 13 novel variants found in 9 genes.According to American College of Medical Genetics and Genomics guidelines,5 novel variants were classified as pathogenic variants,7 were classified as likely pathogenic variants,and 1 was classified as the variant of uncertain significance.Conclusions This study enriches the database of pathogenic gene variants for IMD and provides basic data for establishing an accurate screening and diagnosis system for IMD in this region.

Objective:To investigate the clinical features and prognosis of male with anti-melanoma differentiation-associated gene 5 (MDA5) autoantibody.Methods:The clinical data of 246 patients with DM and anti-MDA5 autoantibodies hospitalized by Jiangsu Myositis Cooperation Group from 2017 to 2020 were collected and retrospectively analyzed. Chi-square test was performed to compared between counting data groups; Quantitative data were expressed by M ( Q1, Q3), and rank sum test was used for comparison between groups; Single factor survival analysis was performed by Kaplan-Meier method and Log rank test; Cox regression analysis were used for multivariate survival analysis. Results:①The male group had a higher proportion of rash at the sun exposure area [67.1%(47/70) vs 52.8%(93/176), χ2=4.18, P=0.041] and V-sign [50.0%(35/70) vs 30.7%(54/176), χ2=8.09, P=0.004] than the female group. The male group had higher levels of creatine kinase [112(18, 981)U/L vs 57 (13.6, 1 433)U/L, Z=-3.50, P<0.001] and ferritin [1 500 (166, 32 716)ng/ml vs 569 (18, 14 839)ng/ml, Z=-5.85, P<0.001] than the female group. The proportion of ILD [40.0%(28/70) vs 59.7%(105/176), χ2=7.82, P=0.020] patients and the red blood cell sedimentation rate[31.0(4.0, 101.5)mm/1 h vs 43.4(5.0, 126.5)mm/1 h, Z=-2.22, P=0.026] in the male group was lower than that of the female group, but the proportion of rapidly progressive interstitial lung disease (PR-ILD) [47.1%(33/70) vs 31.3%(55/176), χ2=5.51, P=0.019] was higher than that of the female group. ②In male patients with positive anti-MDA5 antibodies,the death group had a shorter course of disease[1.0(1.0, 3.0) month vs 2.5(0.5,84) month, Z=-3.07, P=0.002], the incidence of arthritis [16.7%(4/24) vs 42.2%(19/45), χ2=4.60, P=0.032] were low than those in survival group,while aspartate aminotransferase (AST)[64(22.1, 565)U/L vs 51(14,601)U/L, Z=-2.42, P=0.016], lactate dehydrogenase (LDH) [485(24,1 464)U/L vs 352(170, 1 213)U/L, Z=-3.38, P=0.001], C-reactive protein (CRP) [11.6(2.9, 61.7) mg/L vs 4.95(0.6, 86.4) mg/L, Z=-1.96, P=0.050], and ferritin levels [2 000(681, 7 676) vs 1 125 (166, 32 716)ng/ml, Z=-3.18, P=0.001] were higher than those in the survival group, and RP-ILD [95.8%(23/24) vs 22.2%(10/45), χ2=33.99, P<0.001] occurred at a significantly higher rate. ③Cox regression analysis indicated that the course of disease LDH level, and RP-ILD were related factors for the prognosis of male anti-MDA5 antibodies [ HR (95% CI)=0.203(0.077, 0.534), P=0.001; HR (95% CI)=1.002(1.001, 1.004), P=0.003; HR (95% CI)=95.674 (10.872, 841.904), P<0.001]. Conclusion:The clinical manifestations of male anti-MDA5 antibody-positive patients are different from those of female. The incidence of ILD is low, but the proportion of PR-ILD is high. The course of disease, serum LDH level, and RP-ILD are prognostic factors of male anti-MDA5 antibody-positive patients.

Purpose/Significance To expound the development status,difficulties and challenges of smart hospital in China,so as to pro-vide references for the subsequent related research.Method/Process By using the methods of bibliometrics and literature review,the definition of smart hospital is summarized and feasible suggestions on the construction of smart hospital are put forward.Result/Conclusion Smart hospital in China has initially established a"trinity"structural framework of smart healthcare,smart service and smart management,playing a positive role in improving patient satisfaction and promoting high-quality development of hospitals.It is necessary for the government,hospitals,social capital and other multi-party cooperation to jointly promote the construction of smart hospital in China and better protect people's health.