ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

The chemical constituents from Cephalotaxus lanceolata were isolated and purified by using multiple chromatographic techniques, including octadecylsilane(ODS), silica gel, Sephadex LH-20 column chromatography, and semi-preparative high-performance liquid chromatography(HPLC). A total of 17 compounds obtained were identified by using spectroscopic methods such as nuclear magnetic resonance(NMR), mass spectrometry(MS), and ultraviolet(UV) combined with literature data. Compound 1 was a new alkaloid dimer, named cephalancetine E. The known compounds were determined as cephalancetine A(2), 11-hydroxycephalotaxine(3), 4-hydroxycephalotaxine(4), cephalotaxine(5), epicephalotaxine(6), cephalotaxine β-N-oxide(7), acetylcephalotaxine(8), cephalotine A(9), cephalotine B(10), 11-hydroxycephalotaxine hemiketal(11), 3-deoxy-3,11-epoxy-cephalotaxine(12), cephalotaxinone(13), isocephalotaxinone(14), 2,11-epoxy-1,2-dihydro-8-oxo-cephalotaxine(15), cephalotaxamide(16), and drupacine(17), respectively. Compounds 11, 12, and 15 were isolated from the Cephalotaxus genus for the first time. The biological activity was tested for compounds 1-17. The results reveal that compound 17 displays potent inhibitory activities against three human cancer cell lines(HepG-2, MCF-7, and SH-SY5Y).
Cephalotaxus/chemistry* ; Humans ; Cell Line, Tumor ; Drugs, Chinese Herbal/pharmacology* ; Harringtonines/pharmacology* ; Molecular Structure ; Dimerization ; Alkaloids/isolation & purification* ; Magnetic Resonance Spectroscopy

OBJECTIVE: To explore the mechanism of colon dialysis with Yishen Decoction (YS) in improving the autophagy disorder of intestinal epithelial cells in chronic renal failure (CRF) in vivo and in vitro. METHODS: Thirty male SD rats were randomly divided into normal, CRF, and colonic dialysis with YS groups by a random number table method (n=10). The CRF model was established by orally gavage of adenine 200 mg/(kg•d) for 4 weeks. CRF rats in the YS group were treated with colonic dialysis using YS 20 g/(kg•d) for 14 consecutive days. The serum creatinine (SCr) and urea nitrogen (BUN) levels were detected by enzyme-linked immunosorbent assay. Pathological changes of kidney and colon tissues were observed by hematoxylin and eosin staining. Autophagosome changes in colonic epithelial cells was observed with electron microscopy. In vitro experiments, human colon cancer epithelial cells (T84) were cultured and divided into normal, urea model (74U), YS colon dialysis, autophagy activator rapamycin (Ra), autophagy inhibitor 3-methyladenine (3-MA), and SIRT1 activator resveratrol (Re) groups. RT-PCR and Western blot were used to detect the mRNA and protein expressions of zonula occludens-1 (ZO-1), Claudin-1, silent information regulator sirtuin 1 (SIRT1), LC3, and Beclin-1 both in vitro and in vivo. RESULTS: Colonic dialysis with YS decreased SCr and BUN levels in CRF rats (P<0.05), and alleviated the pathological changes of renal and colon tissues. Expressions of SIRT1, ZO-1, Claudin-1, Beclin-1, and LC3II/I were increased in the YS group compared with the CRF group in vivo (P<0.05). In in vitro study, compared with normal group, the expressions of SIRT1, ZO-1, and Claudin-1 were decreased, and expressions of Beclin-1, and LC3II/I were increased in the 74U group (P<0.05). Compared with the 74U group, expressions of SIRT1, ZO-1, and Claudin-1 were increased, whereas Beclin-1, and LC3II/I were decreased in the YS group (P<0.05). The treatment of 3-MA and rapamycin regulated autophagy and the expression of SIRT1. SIRT1 activator intervention up-regulated autophagy as well as the expressions of ZO-1 and Claudin-1 compared with the 74U group (P<0.05). CONCLUSION Colonic dialysis with YS could improve autophagy disorder and repair CRF intestinal mucosal barrier injury by regulating SIRT1 expression in intestinal epithelial cells.
Animals ; Sirtuin 1/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Autophagy/drug effects* ; Male ; Intestinal Mucosa/drug effects* ; Rats, Sprague-Dawley ; Epithelial Cells/metabolism* ; Colon/drug effects* ; Humans ; Kidney Failure, Chronic/drug therapy* ; Signal Transduction/drug effects* ; Renal Dialysis ; Rats ; Kidney/drug effects*

Objective To explore the efficacy and safety of recombinant human anti-severe acute respiratory syn-drome coronavirus 2(anti-SARS-CoV-2)monoclonal antibody injection(F61 injection)in the treatment of patients with coronavirus disease 2019(COVID-19)combined with renal damage.Methods Patients with COVID-19 and renal damage who visited the PLA General Hospital from January to February 2023 were selected.Subjects were randomly divided into two groups.Control group was treated with conventional anti-COVID-19 therapy,while trial group was treated with conventional anti-COVID-19 therapy combined with F61 injection.A 15-day follow-up was conducted after drug administration.Clinical symptoms,laboratory tests,electrocardiogram,and chest CT of pa-tients were performed to analyze the efficacy and safety of F61 injection.Results Twelve subjects(7 in trial group and 5 in control group)were included in study.Neither group had any clinical progression or death cases.The ave-rage time for negative conversion of nucleic acid of SARS-CoV-2 in control group and trial group were 3.2 days and 1.57 days(P=0.046),respectively.The scores of COVID-19 related target symptom in the trial group on the 3rd and 5th day after medication were both lower than those of the control group(both P＜0.05).According to the clinical staging and World Health Organization 10-point graded disease progression scale,both groups of subjects improved but didn't show statistical differences(P＞0.05).For safety,trial group didn't present any infusion-re-lated adverse event.Subjects in both groups demonstrated varying degrees of elevated blood glucose,elevated urine glucose,elevated urobilinogen,positive urine casts,and cardiac arrhythmia,but the differences were not statistica-lly significant(all P＞0.05).Conclusion F61 injection has initially demonstrated safety and clinical benefit in trea-ting patients with COVID-19 combined with renal damage.As the domestically produced drug,it has good clinical accessibility and may provide more options for clinical practice.

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/Significance To improve the classification and evaluation mode of medical safety incidents,and to improve work efficiency and timeliness.Method/Process The data of previous medical safety incidents are pre-processed,BERT model is used for training,testing and iterative optimization,and an intelligent classification and prediction model for medical safety incidents is built.Re-sult/Conclusion The model is used to classify 466 medical safety incidents reported by clinical departments from January to November 2022,and F1 value reaches 0.66.The application of BERT model in the classification and evaluation of medical safety incidents can im-prove work efficiency and timeliness,and help timely intervene in medical safety risks.

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.

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.

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.