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.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

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 assess the risk of aristolochic acid (AA)-associated cancer in patients with AA nephropathy (AAN). METHODS: A retrospective study was conducted on patients diagnosed with AAN at Peking University First Hospital from January 1997 to December 2014. Long-term surveillance and follow-up data were analyzed to investigate the influence of different factors on the prevalence of cancer. The primary endpoint was the incidence of liver cancer, and the secondary endpoint was the incidence of urinary cancer during 1 year after taking AA-containing medication to 2014. RESULTS: A total of 337 patients diagnosed with AAN were included in this study. From the initiation of taking AA to the termination of follow-up, 39 patients were diagnosed with cancer. No cases of liver cancer were observed throughout the entire follow-up period, with urinary cancer being the predominant type (34/39, 87.17%). Logistic regression analysis showed that age, follow-up period, and diabetes were potential risk factors, however, the dosage of the drug was not significantly associated with urinary cancer. CONCLUSIONS No cases of liver cancer were observed at the end of follow-up. However, a high prevalence of urinary cancer was observed in AAN patients. Establishing a direct causality between AA and HCC is challenging.
Humans ; Retrospective Studies ; Incidence ; Carcinoma, Hepatocellular ; Liver Neoplasms/epidemiology* ; Kidney Diseases/chemically induced* ; Aristolochic Acids/adverse effects*

Objective To explore the clinical significance of nucleolar antinuclear antibodies(ANA)in re-lated diseases.Methods This study was a retrospective study.Clinical samples of 71780 patients who visited the hospital from January 2017 to May 2022 were collected.Indirect immunofluorescence was used to detect ANA in clinical samples.Statistical analysis was conducted on the positivity rate of nucleolar ANA in clinical patients,as well as the relevant clinical information and laboratory characteristics of patients with autoimmune diseases(AID)with nucleolar ANA positivity.Results Among 71780 patients who underwent routine ANA testing,16778 were positive for ANA,with a positive rate of 23.37％.Among them,there were 1 708 cases of nucleolar type,accounting for 2.38％of all routine ANA tests,and the proportion of ANA positive cases was 10.18％.There was a statistically significant difference in the positive rate of nucleolar ANA between patients of different genders in the＞20-＜50 year old group and the ≥ 50 year old group(P＜0.05),while there was no statistically significant difference in the positive rate of nucleolar ANA between patients of different genders in the ≤ 20 year old group(P＞0.05).There was a statistically significant difference in the positivity rate of nucleolar ANA among women of different age groups(P＜0.05),among them,the highest positive rate of nucleolar ANA was found in women aged between 20 and 50 years old.There was no statistically significant difference in the positive rate of nucleolar ANA among males of different age groups(P＞0.05).The positivi-ty rate of ANA was the highest among patients in the Department of Rheumatology and Immunology(70.35％),but nucleolar ANA positivity was mainly seen in departments such as Reproductive Medicine Cen-ter(12.90％),Respiratory Medicine(12.40％),and Neurology(11.29％),and the difference in positivity rates between departments was statistically significant(P＜0.05).Out of 1 708 nucleolar ANA positive indi-viduals,420 underwent ANA titers,including 34 AID patients and 386 non AID patients.There was no statis-tically significant difference in nucleolus positive titers between non AID patients and AID patients(P＞0.05).Conclusion The nucleolus type is a common fluorescence pattern in ANA positive individuals,and there are gender and age differences in ANA positive individuals.The positive rate and titer of nucleolar ANA vary among different AID diseases.Combined with other immune function indicators,and it is helpful for early differential diagnosis of AID.

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.

Objective:This study aims to investigate the inner experiences of cancer patients when confronting financial toxicity through a Meta-analytical approach. The goal is to provide evidence-based insights and theoretical references for clinical healthcare professionals to fully understand the feelings and experiences associated with financial toxicity in cancer patients, thereby laying a foundation for targeted interventional measures.Methods:A computerized search was conducted across databases including CINAHL, PubMed, PsycINFO, Embase, Scopus, Web of Science, CNKI, WanFang, VIP, Sinomed and other databases, collecting qualitative studies related to the inner experience of financial toxicity among cancer patients, up until April 12, 2023. Quality appraisal of the included literature was carried out according to JBI′s standards for qualitative research, and results were synthesized using Meta-aggregation methods.Results:Twenty-three studies were included, from which 82 specific outcomes were extracted. These were categorized into 11 new thematic groups and ultimately synthesized into four integrated findings: the impact of objective costs, subjective burden, and unreasonable expectations; diverse cognitive attitudes and management coping strategies; multiple pressures and challenges on patients and their families; and the articulation of needs and perceptions of beneficial growth.Conclusions:Healthcare professionals should pay attention to the financial toxicity issues that patients face at the initial stage of cancer diagnosis. Timely communication about economic issues between patients and healthcare providers is essential to help patients have a preliminary understanding of the impending financial toxicity at the onset of the disease. A comprehensive intervention that emphasizes different aspects of objective and subjective financial toxicity, coupled with multi-dimensional mitigation strategies, can promote active coping in patients, enhance familial emotional and financial support to overcome challenges together, and prioritize patients′ needs and expectations to guide them towards reinforcing positive experiences and minimizing the impact of financial toxicity.

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.