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.

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.

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.

Purpose To demonstrate the advantages of non-contrast-enhanced whole-heart coronary magnetic resonance angiography(NCE-CMRA)in evaluating coronary arteries by comparing ultrasonography(US),and to explore the clinical value of NCE-CMRA in the diagnosis of coronary artery lesions of Kawasaki disease(KD)in children.Materials and Methods NCE-CMRA and US imaging data of 41 children with KD from June 2017 to June 2021 who were diagnosed clinically in Lanzhou University Second Hospital were analyzed retrospectively.The display ability of US and NCE-CMRA in coronary arteries were compared.At the same time,the imaging characteristics of NCE-CMRA were analyzed,and the imaging characteristics such as the range and degree of coronary artery lesions displayed by NCE-CMRA were summarized.Results The overall segment display rate of NCE-CMRA in 41 children with KD was 75.6%;the overall segment display rate of US was 46.3%,with statistical difference between the two techniques(χ2=59.04,P<0.001).Regarding the display of the middle and distal segments of coronary arteries,NCE-CMRA had a clear imaging advantage over US(χ2=57.98 and 161.47,P<0.001).In all cases,25 patients(200 segments)had coronary artery lesions,and 94 segments of coronary artery showed different degrees of dilatation,including 8 segments(8.6%)of giant coronary artery aneurysm,35 segments(37.2%)of medium coronary artery aneurysm,and 51 segments(54.2%)of small coronary artery aneurysm or coronary artery dilation.Conclusion NCE-CMRA technology can objectively and accurately display coronary artery in children,and it can specifically evaluate the degree of damage caused by KD.It has important clinical significance in the diagnosis and evaluation of coronary artery lesions in children with KD.

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.

ObjectiveTo explore the scientific connotation of fried charcoal survivability of Lonicerae Japonicae Flos（LJF） by analyzing the correlation between the color change and the intrinsic components during the processing of LJF Carbonisata（LJFC）， and taking pH， charcoal adsorption and microscopic characteristics as indexes. MethodLJFC samples with different degrees of processing were prepared according to the stir-frying time of 0.0， 1.5， 3.0， 4.5， 6.0， 7.5， 9.0， 10.5 min（numbered S1-S8）， and the contents of gallic acid， chlorogenic acid， cryptochlorogenic acid， rutin， luteoloside， isochlorogenic acid A and isochlorogenic acid C were determined by high performance liquid chromatography（HPLC）， and the L^*（brightness）， a^*（red-greenness） and b^*（yellow-blueness） of LJFC samples with different degrees of processing were determined by spectrophotometer， and the correlation analysis and principal component analysis（PCA） between the contents of seven representative components and the color of the samples were carried out by SPSS 26. 0 and SIMCA-P 14.1. Then pH， adsorption force and characteristic structure of different samples of LJFC were detected and the processing pattern of LJFC was analyzed. ResultThe results of quantitative analysis revealed that the contents of luteoloside， rutin， chlorogenic acid and isochlorogenic acid A gradually decreased， and the contents of cryptochlorogenic acid， isochlorogenic acid C and gallic acid firstly increased and then decreased. The L^* and b^* of the sample powders decreased， and a^* showed a trend of increasing and then decreasing. The L^* and b^* were positively correlated with the contents of chlorogenic acid， rutin， luteoloside， isochlorogenic acid A， b^* was positively correlated with the content of gallic acid， and a^* was positively correlated with the contents of cryptochlorogenic acid and isochlorogenic acid C. PCA revealed that samples could be clearly divided into 3 groups， S1-S2 as one group， S3-S5 as one group， and S6-S8 as one group， with S3 having the highest score. The results of regression analysis showed that only isochlorogenic acid C could be used to predict the contents of components by colorimetric values combined with regression equations. Physicochemical analysis showed that pH of LJFC increased with the increase of degree of charcoal stir-frying， while adsorption force showed a tendency of increasing and then decreasing， with the highest adsorption force in the S5 sample， and the non-glandular hairs， calcium oxalate clusters and pollen grains had a varying degree of decreasing with the deepening of processing degree， and the microstructures of S6-S8 samples were obviously charred with pollen grains almost invisible. ConclusionThe changes in chemical composition and color characteristics of LJFC during the processing have certain correlations， combined with the changes in physicochemical properties， S5 sample is found to be the optimal processed products， which can provide a reference for the processing standardization and quality evaluation of LJFC， and enrich the scientific connotation of fried charcoal survivability of LJF.

Objective To investigate the epidemiological and clinical characteristics of 954 cases of central nervous system(CNS)infections in Chongqing.Methods A retrospective analysis was conducted on 954 patients with CNS infectious disease diagnosed and treated in the Second Affiliated Hospital of Army Medical University from 2008 to 2021.The analysis encompassed pathogens,patient gender,age of onset,time of onset,urban-rural distribution,education level,occupational distribution,and other epidemiological characteristics.The clinical manifestations,the positive rate of metagenomic next-generation sequencing(mNGS),and prognosis were also analyzed.Results Among the 945 cases of CNS infectious diseases,the pathogens were viruses in 393(41.2%),Mycobacterium tuberculosis in 361(37.8%),other bacteria in 108(11.3%),Cryptococcus in 75(7.9%),Treponema pallidum in 16(1.7%)and parasites in 1(0.1%).The number of CNS infection cases from 2015 to 2021 increased by 85.6%compared with that from 2008 to 2014(620 vs.334,P<0.001).There was no significant difference in seasonal distribution of pathogens(P>0.05).CNS infectious diseases were more prevalent in rural areas(58.0%,P<0.001),with a male-to-female ratio of 1.7:1.0,and a higher incidence in individuals aged between 35 and 60 years.The majority of patients were educated at Junior high school level or below(68.7%)and were farmers or workers(68.1%).Clinical symptoms of CNS infectious disease mainly included fever,headache,signs of meningeal irritation,nausea and vomiting,which could be accompanied by consciousness disorder and focal neurological deficits.mNGS significantly improves the accuracy of clinical diagnosis.The rate of good prognosis of CNS infectious diseases was 97.5%,while the mortality rate was 0.3%.Conclusions In Chongqing area,the categories and species of CNS infectious pathogens are diverse,widely prevalent,and the clinical manifestations are complex.Moreover,the number of cases has been increasing in recent years.Understanding the epidemiological and clinical characteristics of CNS infectious diseases can help to recognize the regional differences,promote early accurate diagnosis and treatment,and improve prognosis.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.