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.

OBJECTIVES: To investigate the clinicopathological characteristics and prognostic factors of pediatric Hodgkin lymphoma (HL). METHODS: A retrospective analysis was conducted on the clinical data of children with newly diagnosed HL from January 2011 to December 2023 at four hospitals: Fujian Medical University Union Hospital, Fujian Medical University Zhangzhou Hospital, First Affiliated Hospital of Xiamen University, and Fujian Children's Hospital. Patients were categorized into low-risk (R1), intermediate-risk (R2), and high-risk (R3) groups based on HL staging and pre-treatment risk factors. The patients received ABVD regimen or Chinese Pediatric HL-2013 regimen chemotherapy. Early treatment response and long-term efficacy were assessed, and prognostic factors were analyzed using the Cox proportional hazards regression model. RESULTS: The overall complete response (CR) rates after 2 and 4 cycles of chemotherapy were 42% and 68%, respectively. Compared with the ABVD regimen group, patients treated with the HL-2013 regimen in the R1 group showed significantly higher CR rates after both 2 and 4 cycles (P<0.05). However, no statistically significant differences in CR rates were observed between the two regimens in the R2 and R3 groups (P>0.05). The 5-year event-free survival (EFS) rate, overall survival rate, and freedom from treatment failure rate were 83%±4%, 97%±2%, and 88%±4%, respectively. Cox analysis indicated that the presence of a large tumor mass at diagnosis and failure to achieve CR after 4 cycles of chemotherapy were independent risk factors for lower EFS rates (P<0.05). CONCLUSIONS Pediatric HL generally has a favorable prognosis. The presence of a large tumor mass at diagnosis and failure to achieve CR after 4 cycles of chemotherapy indicate poor prognosis.
Humans ; Hodgkin Disease/pathology* ; Male ; Child ; Female ; Adolescent ; Retrospective Studies ; Child, Preschool ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Prognosis ; Proportional Hazards Models ; Survival Analysis ; Infant

OBJECTIVE: To analyze clinical characteristics and prognosis of B-cell acute lymphoblastic leukemia (B-ALL) patients with IKZF1 deletion. METHODS: 72 patients with B-ALL admitted to our hospital from April 2020 to January 2023 were selected, IKZF1 deletion were detected, and clinical characteristics and prognosis were analyzed. RESULTS: Among the 72 patients, a total of 32 patients (44.4%) were identified with IKZF1 deletions (IKZF1 ⁺ ). There was no statistically significant difference in basic clinical data between patients with normal IKZF1 (IKZF1 ^-) and those with IKZF1 ⁺ (P >0.05). The proportion of patients with IKZF1 ⁺ in Ph⁺ group was significantly higher than that in Ph^- group (P < 0.05). The main types of IKZF1 ⁺ were exon 1-8 deletion (34.4%) and exon 4-7 deletion (31.2%). The median OS and PFS of IKZF1 ^- patients were significantly longer than those of IKZF1 ⁺ patients (OS: 26.0 months vs 16.0 months, χ ²=23.094, P < 0.05; PFS: 26.0 months vs 16.0 months, χ ²=11.150, P < 0.05). Among IKZF1 ⁺ patients, the median OS of patients who received allogeneic hematopoietic stem cell transplantation (allo-HSCT) was significantly longer than that of patients who did not receive allo-HSCT (no reached vs 15.0 months, χ ²=5.685, P < 0.05). CONCLUSION IKZF1 deletion is a risk factor affecting the prognosis of B-ALL patients.
Humans ; Ikaros Transcription Factor/genetics* ; Prognosis ; Gene Deletion ; Female ; Male ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/genetics* ; Adult ; Precursor B-Cell Lymphoblastic Leukemia-Lymphoma/genetics* ; Adolescent ; Young Adult ; Middle Aged

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

In recent years, artificial intelligence (AI) technology has advanced rapidly and has been widely applied in various fields such as medicine and pharmacy, accelerating the drug development process. Focusing on the application of AI in the discovery and optimization of lead compounds, this review provides a detailed introduction to AI-assisted virtual screening and molecular generation methods for discovering lead compounds, while particularly highlighting the cases of AI-drived drugs into clinical trials. Additionally, we briefly outline the application of AI basic algorithm models in quantitative structure-activity relationship (QSAR) and drug repurposing, offering insights for AI-based drug discovery.

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.

Neck dissection(ND)is one of the main treatment methods for oral and maxillofacial malignancies.Although ND type is in con-stant improvement,but intraoperative peal-pull-push injury of the accessory nerve,muscle,muscle membrane,fascia and ligament induced shoulder syndrome(SS)is still a common postoperative complication,combined with the influence of radiochemotherapy,not only can cause pain,stiffness,numbness,limited dysfunction of shoulder neck and arm,but also may have serious impact on patient's life quality and phys-ical and mental health.At present,there is still a lack of a systematic evaluation and rehabilitation management program for postoperative SS of oral and maxillofacial malignant tumors.Based on the previous clinical practice and the current available evidence,refer to the relevant lit-erature at home and abroad,the experts in the field of maxillofacial tumor surgery and rehabilitation were invited to discuss,modify and reach a consenusus on the etiology,assessment diagnosis,differential diagnosis,rehabilitation strategy and prevention of SS,in order to provide clinical reference.

Objective To explore characteristics of co-infection of Chlamydia pneumoniae(Cpn)and severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),and identify their effect on SARS-CoV-2-induced inflammatory response.Methods Patients with coronavirus disease 2019(COVID-19)who received treatment in a hospital in Chenzhou City from December 20,2022 to February 20,2023 were selected.According to the severity of COVID-19,severe and critical cases were classified as the severe symptom group,while mild and moderate cases were classified as the mild symptom group.Meanwhile,according to the age of patients(≥18 years old as adults,＜18 years old as juveniles),they were divided into the adult severe symptom group,adult mild symptom group,juvenile severe symptom group,and juvenile mild symptom group.Propensity score was adopted to match age,gender,and under-lying diseases of patients in severe symptom and mild symptom group in a 1∶1 ratio.Bronchoalveolar lavage fluid(BALF),throat swabs,and serum specimens of patients were collected.Cpn IgG/IgM antibody was detected by enzyme-linked immunosorbent assay(ELISA),levels of 12 common cytokines(including interleukin-8[IL-8])in BALF were detected by flow cytometry,differences among groups were compared.Results A total of 102 patients were included,with 61 severe and critical(severe symptom)patients,as well as 41 mild and moderate(mild symp-tom)patients.There were 71 patients aged ≥18 years and 31 juvenile patients aged＜18 years.There were 39 pa-tients in the adult severe symptom group and 32 in the adult mild symptom group,and 30 pairs were successfully matched through propensity score analysis.There were 22 patients in the juvenile severe symptom group and 9 in the juvenile mild symptom group,and 8 pairs were successfully matched through propensity score analysis.Among COVID-19 patients,the positive rates of Cpn IgG and IgM were 36.27％(n=37)and 8.82％(n=9),respective-ly,with 1 case positive for both Cpn IgG and IgM.The level of interferon(IFN)-α in serum specimens from adult patients with severe symptom combined with positive Cpn IgG was higher than that of IgG negative patients(P=0.037).There was no statistically significant difference in the levels of other cytokines in BALF and serum speci-mens between the two groups of patients(all P＞0.05).The levels of IL-8 and IL-17 in serum specimens of patients with positive Cpn IgG in the adult mild symptom group were both higher than those in Cpn IgG negative patients(both P＜0.05).The levels of IL-8 in both BALF and serum specimens from Cpn IgM positivity patients in the ju-venile mild symptom group were higher than those from patients with negative Cpn IgM(both P＜0.05).Logistic regression analysis results showed that Cpn IgG and IgM positivity were not risk factors for the development of se-vere COVID-19.Conclusion Combined Cpn infection is not a risk factor for the development of severe symptom in COVID-19 patients,and Cpn infection has limited impact on the secretion of inflammatory factors caused by SARS-CoV-2.