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.

In order to explore the possible role and molecular mechanism of the combined action of leech and bear bile in liver and gallbladder diseases, this study first used network pharmacology methods to screen the components and targets of leech and bear bile, as well as the related target genes of liver and gallbladder diseases. The selected key genes were subjected to interaction network and GO/KEGG enrichment analysis. Then, using sodium oleate induced HepG2 cell lipid deposition model and DL-ethionine induced mouse fatty liver model, the activity of leech and bear bile alone and in combination in reducing liver fat was evaluated in vitro and in vivo, and the expression of key pathway related proteins suggested by network pharmacology was detected by Western blot. The results of network pharmacology analysis showed that the active ingredients of leech and bear bile have 295 intersecting targets with liver and gallbladder related diseases, involving more than 200 signaling pathways, including the PI3K/Akt signaling pathway and phospholipase D signaling pathway closely related to glucose and lipid metabolism. The results of in vitro validation experiments showed that both leech and bear bile, alone and in combination, can significantly inhibit the lipid deposition induced by sodium oleate in human liver cells, reduce the triacylglycerol level in cell culture supernatant, and inhibit the lipid content in liver cells. The observation results of Nile red staining confocal microscopy showed that the combination of leech and bear bile had better activity in reducing lipid deposition in liver cells compared to using them alone. In a mouse fatty liver model, the combination of leech and bear bile can better reduce elevated organ indices, blood lipids, and liver lipid levels, as well as lower the levels of serum liver injury biomarkers. The animals used in this experiment and related disposal meet the requirements of animal welfare. Before the experiment, it was reviewed and approved by IACUC, Institute of Materia Medica, Chinese Academy of Medical Sciences. The Western blot experiment results showed that the combination of leech and bear bile can significantly upregulate the expression levels of p-PI3K and p-Akt proteins, and increase the p-PI3K/PI3K and p-Akt/Akt ratios, which is consistent with the predicted results of network pharmacology. The combination of leech and bear bile has great potential for treating fatty liver disease, and activating the PI3K/Akt pathway may be one of the important mechanisms for reducing lipid deposition in liver cells.

AIM: To analyze the influencing factors of capsular constriction syndrome(CCS)in cataract patients after phacoemulsification(Phaco)combined with intraocular lens(IOL)implantation.METHODS: Retrospective study. The data of 2 900 cataract patients(2 900 eyes)in our hospital's information system from January 2021 to January 2024 were collected. All patients were treated with Phaco combined with IOL implantation, and the incidence of CCS within 30 wk after surgery was recorded. Patients were categorized into CCS(116 cases, 116 eyes)and N-CCS group(2 784 cases, 2 784 eyes)based on the occurrence of CCS. The basic data of the two groups were compared, and the influencing factors of CCS within 30 wk after Phaco combined with IOL implantation in cataract patients were analyzed by multivariate Logistic regression.RESULTS: Among 2 900 patients(2 900 eyes)included, 116 cataract patients(116 eyes)developed CCS within 30 wk after Phaco combined with IOL implantation, with an incidence rate of 4.00%. The single factor and multi-factor Logistic regression analysis showed that the complicated diabetes, high myopia, complicated glaucoma, and axial length(AL)>30 mm were the risk factors for the occurrence of CCS after Phaco IOL implantation in cataract patients(all P<0.05).CONCLUSION: Attention should be paid to cataract patients with diabetes, high myopia, glaucoma and AL>30 mm, which will increase the risk of CCS within 30 wk after Phaco combined with IOL implantation in cataract patients.

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 investigate the protective effect and mechanism of cryptotanshinone （CTS） on heart and kidney function in rats with cardiorenal syndrome （CRS） by regulating phosphoinositide kinase-3 （PI3K）/protein kinase B （Akt）/ mammalian target of rapamycin （mTOR） signaling pathway. METHODS CRS model of rats was induced by left anterior descending coronary artery ligation combined with acute renal ischemia-reperfusion injury. Model rats were randomly divided into CRS model group （CRS group）， low-dose CTS group （CTS-L）， high-dose CTS group （CTS-H group）， high-dose CTS+PI3K activator 740Y-P group （CTS-H+740Y-P group）， with 12 rats in each group. Another 12 rats were selected as the normal control group （Normal group） and were carried out surgery without modeling. CTS-L group and CTS-H group were respectively given CTS 30 and 60 mg/kg intragastrically， once a day， for consecutive 14 d. Besides the intervention of CTS 60 mg/kg intragastrically， CTS-H+740Y-P group was given 10 mg/kg 740Y-P intraperitoneally， once a day， for 14 consecutive days. After the last medication， the levels of cardiac function [left ventricular ejection fraction （LVEF）， left ventricular end-systolic diameter （LVESD）， left ventricular end-diastolic diameter （LVEDD）， left ventricular fraction shortening （LVFS）] and renal function [24 h urinary protein， blood urea nitrogen （BUN）， serum creatinine （Scr）， brain natriuretic peptide （BNP）] were detected in rats. The pathological changes and fibrosis of the heart and kidney in rats were observed； the expressions of PI3K/Akt/mTOR signaling pathway in heart and renal tissue were all detected. RESULTS Compared with Normal group， the levels of LVEF and LVFS in rats were all decreased significantly in CRS group （P＜0.05）； the levels of LVESD， LVEDD， 24 h urinary protein， serum levels of BUN， Scr and BNP， collagen area and the phosphorylation of PI3K， Akt and mTOR protein in heart and renal tissue were all increased significantly （P＜0.05）. The morphology of myocardial cells was enlarged and disordered； the structure ofrenal tubules was disordered， epithelial cells were wrinkled， and there was infiltration of inflammatory cells. Compared with CRS group， the above indexes of rats were reversed significantly in CTS-L group and CTS-H group （P＜0.05）； heart and kidney function had been restored， and pathological damage and fibrosis had been reduced. PI3K activator 740Y-P weakened the protective effect of CTS on cardiac and renal function in CRS rats. CONCLUSIONS CTS can protect heart and kidney function in CRS rats， the mechanism of which may be associated with inhibiting the PI3K/Akt/mTOR signaling pathway.

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.

Critically ill patients are at high risk for hospital acquired infections, which can significantly increase the mortality rate and treatment costs for these patients. Therefore, in the process of treating the primary disease, strict prevention and control of new hospital infections is an essential component of the treatment for critically ill patients. The treatment of critically ill patients involves multiple steps and requires a concerted effort from various aspects such as theory, management, education, standards, and supervision to achieve effective prevention and control of hospital infections. However, there is currently a lack of unified understanding and standards for hospital infection prevention and control. To address this, in March 2024, a group of experts in critical care medicine, infectious diseases, and hospital infection from China discussed the current situation and issues of hospital infection control in the intensive care unit together. Based on a review of the latest evidence-based medical evidence from both domestic and international sources, 2024 Expert Consensus on Hospital Acquired Infection Control Principles in the Department of Critical Care Medicine was formed, aiming to provide a basis for the development of hospital infection prevention and control strategies in the field of critical care medicine.

Objective Recombinase-aided polymerase chain reaction(RAP)is a sensitive,single-tube,two-stage nucleic acid amplification method.This study aimed to develop an assay that can be used for the early diagnosis of three types of bacteremia caused by Staphylococcus aureus(SA),Pseudomonas aeruginosa(PA),and Acinetobacter baumannii(AB)in the bloodstream based on recombinant human mannan-binding lectin protein(M1 protein)-conjugated magnetic bead(M1 bead)enrichment of pathogens combined with RAP. Methods Recombinant plasmids were used to evaluate the assay sensitivity.Common blood influenza bacteria were used for the specific detection.Simulated and clinical plasma samples were enriched with M1 beads and then subjected to multiple recombinase-aided PCR(M-RAP)and quantitative PCR(qPCR)assays.Kappa analysis was used to evaluate the consistency between the two assays. Results The M-RAP method had sensitivity rates of 1,10,and 1 copies/μL for the detection of SA,PA,and AB plasmids,respectively,without cross-reaction to other bacterial species.The M-RAP assay obtained results for<10 CFU/mL pathogens in the blood within 4 h,with higher sensitivity than qPCR.M-RAP and qPCR for SA,PA,and AB yielded Kappa values of 0.839,0.815,and 0.856,respectively(P<0.05). Conclusion An M-RAP assay for SA,PA,and AB in blood samples utilizing M1 bead enrichment has been developed and can be potentially used for the early detection of bacteremia.

Objective To explore the therapeutic effect and mechanism of pressing and rubbing method on myofascial pain syndrome(MPS)rats.Methods A total of 12 rats were randomly selected from 60 rats as the normal group,and the remaining rats were used to construct the MPS model by blunt strike combined with centrifugal exercise.Then 48 successfully modeled rats were randomly divided into model group,pressing and rubbing method group,pressing and rubbing method + Dantrolene[ryanodine receptor(RyR)inhibitor]group,pressing and rubbing method + normal saline group,with 12 rats in each group.The mechanical pain threshold was measured by von-Frey method.Detection of soft tissue tension,electromyography was performed;the ultrastructure of the pain point tissue was observed by transmission electron microscopy.The content of calcium ion(Ca2+)in the tissue of trigger point was detected by colorimetry.The protein expressions of dihydropyridine receptor(DHPR)α1,RyR and acetylcholinesterase(AChE)in the pain points of rats were detected by Western Blot.Results Compared with the normal group,the mechanical pain threshold,soft tissue tension in trigger point and the protein expressions of DHPRα1,RyR and AChE in the model group were decreased and the Ca2+ + content was increased(all P＜0.05),and the peak potential with higher amplitude was observed in the electromyogram.The ultrastructure of the trigger point tissue was damaged.Compared with the model group,the mechanical pain threshold,soft tissue tension of trigger point and the protein expressions of DHPRα1,RyR and AChE in the trigger point tissue of the rats in the pressing and rubbing method group and the pressing and rubbing method + normal saline group were increased,and the Ca2+ content was decreased(all P＜0.05),the electromyography was restored to be stable,the ultrastructural damage of the trigger point tissue was alleviated.Compared with the pressing and rubbing method group,the mechanical pain threshold,soft tissue tension of trigger point and protein the expressions of DHPRα1,RyR and AChE in the trigger point tissue of the rats in the pressing and rubbing method + Dantrolene group were decreased,and the Ca2+ content was increased(all P＜0.05),the electromyogram showed electrical activity changes,the ultrastructure of the trigger point tissue was damaged.Conclusion The pressing and rubbing method may improve MPS in rats by activating the DHPR/RyR signaling pathway.

Objective To investigated vascular characteristics and clinical follow-up results of gestational trophoblastic neoplasia(GTN)with uterine arteriovenous malformation(UAVM)using contrast-enhanced magnetic resonance angiography(CE-MRA).Methods Patients clinically suspected of GTN at Obstetrics and Gynecology Hospital,Fudan University from Dec 2015 to Dec 2020 were selected.Imaging characteristics of conventional magnetic resonance imaging and CE-MRA before treatment.The International Federation of Gynecology and Obstetrics(FIGO)2000 clinical staging and prognosis scoring system was used to evaluate the severity of the condition and related risk factors,the treatment methods(chemotherapy,surgical treatment,and arterial embolization)and prognosis determined during follow-up were recorded.Results A total of 44 cases were included,including 5 cases of placental site trophoblastic tumor(PSTT)and 39 cases of the other GTN.There were 3 cases of PSTT combined with UAVM and 23 cases of the other GTN combined with UAVM.Thirty-nine cases of the other GTN were divided into two groups according to the presence or absence of UAVM.Data regarding the β-human chorionic gonadotropin(β-hCG)value(＜10 000 mIU/mL and≥10 000 mIU/mL)were evaluated using Chi-square test,and the difference was statistically significant(P=0.001).The average FIGO scores of the two groups were 4.19±3.69 and 6.70±3.39,and the difference was statistically significant(P=0.035).Conclusion When β-hCG value≥10 000 mIU/mL,the probability of UAVM occurrence increases.The higher the prognosis score is,the more possibility of formation of UAVM.The use of CE-MRA technology helps to better diagnose UAVM.