Sleep disorder is a common complication in elderly patients with cerebral infarction, which seriously affects the rehabilitation process and quality of life of patients. Currently, the treatment of sleep disorders mainly consists of pharmacological and non-pharmacological interventions. Although pharmacological treatment has a certain effect, there are many adverse reactions, especially for elderly patients with declining body function, whose use carries a higher risk. It is of great significance to develop a reasonable individualized non-pharmacological intervention to prevent and treat the accompanying sleep disorders after cerebral infarction. This paper provides a brief review of present status, influencing factors and non-pharmacological interventions of sleep disorders in patients with acute cerebral infarction.

BACKGROUND:Alzheimer's disease has been associated with sarcopenia,but a causal relationship has not been established.Exploring the causal relationship between the two most common disability-burdening diseases in the aging population-Alzheimer's disease and sarcopenia-and their potential mediating factors holds certain implications for further alleviating the healthcare costs and socioeconomic burden for older adults in China.OBJECTIVE:To explore the potential causal relationship between Alzheimer's disease and sarcopenia in the general population using a Mendelian randomization study and to explore the role of body mass index in this context.METHODS:Two-sample Mendelian randomization analysis based on published genome-wide association studies(GWAS)were used to infer causality,and univariate Mendelian randomization and mediation analyses were used in the study design.Through the Integrative Epidemiology Unit(IEU)database,ieu-b-2 was selected as the Alzheimer's disease dataset(sample size:63 926),ieu-b-4816 as the body mass index dataset(99 998),ebi-a-GCST90000027 as the appendicular lean mass dataset(244 730),ukb-b-7478 as the left hand grip strength dataset(461 026),ukb-b-10215 as the right hand grip strength dataset(461 089)and ukb-b-4711 as the walking pace dataset(459 915).Inverse-variance weighting was used as the primary analysis method,and the results were validated by pleiotropy and heterogeneity analysis.The Steiger Directionality Test was performed to validate the reasonableness of the causal direction.RESULTS AND CONCLUSION:(1)The Mendelian randomization analyses provided evidence that Alzheimer's disease predicted the risk of appendicular lean mass[odds ratio(OR)=1.009;95％confidence interval(Cl),1.001-1.017;P=0.023),and walking pace(OR=1.010;95％Cl,1.003-1.017;P=0.008).No correlation with hand grip strength was observed.(2)Alzheimer's disease was negatively correlated with body mass index(OR=0.893;95％Cl,0.811-0.984;P=0.022);body mass index was positively correlated with appendicular lean mass(OR=1.084;95％Cl,1.031-1.141;P=0.002)and negatively correlated with walking pace(OR=0.975;95％Cl,0.969-0.980;P＜0.001).(3)Mediation analyses showed that the causal relationship between Alzheimer's disease and appendicular lean mass and walking pace was partially mediated by body mass index,with the proportion of mediations being 50.25％and 32.11％,respectively.(4)The results of this study suggest that based on large-scale population studies,genetic prediction of Alzheimer's disease is a potential risk factor for sarcopenia,in which body mass index plays an important mediating role.This suggests that in clinical practice,attention should be paid to the muscle condition of patients with Alzheimer's disease,and weight management should be implemented,as maintaining a body mass index within the normal high range may have a preventive effect on the occurrence of sarcopenia in patients with Alzheimer's disease.However,further research is needed to verify the applicability of this conclusion to other ethnic groups.This study utilized an international public database for analysis,providing a reference for research on the correlation between Alzheimer's disease and sarcopenia in the Chinese population.It also highlights the significant mediating role of body mass index,offering insights for further prevention and treatment of sarcopenia among Chinese individuals.

BACKGROUND:Alzheimer's disease has been associated with sarcopenia,but a causal relationship has not been established.Exploring the causal relationship between the two most common disability-burdening diseases in the aging population-Alzheimer's disease and sarcopenia-and their potential mediating factors holds certain implications for further alleviating the healthcare costs and socioeconomic burden for older adults in China.OBJECTIVE:To explore the potential causal relationship between Alzheimer's disease and sarcopenia in the general population using a Mendelian randomization study and to explore the role of body mass index in this context.METHODS:Two-sample Mendelian randomization analysis based on published genome-wide association studies(GWAS)were used to infer causality,and univariate Mendelian randomization and mediation analyses were used in the study design.Through the Integrative Epidemiology Unit(IEU)database,ieu-b-2 was selected as the Alzheimer's disease dataset(sample size:63 926),ieu-b-4816 as the body mass index dataset(99 998),ebi-a-GCST90000027 as the appendicular lean mass dataset(244 730),ukb-b-7478 as the left hand grip strength dataset(461 026),ukb-b-10215 as the right hand grip strength dataset(461 089)and ukb-b-4711 as the walking pace dataset(459 915).Inverse-variance weighting was used as the primary analysis method,and the results were validated by pleiotropy and heterogeneity analysis.The Steiger Directionality Test was performed to validate the reasonableness of the causal direction.RESULTS AND CONCLUSION:(1)The Mendelian randomization analyses provided evidence that Alzheimer's disease predicted the risk of appendicular lean mass[odds ratio(OR)=1.009;95％confidence interval(Cl),1.001-1.017;P=0.023),and walking pace(OR=1.010;95％Cl,1.003-1.017;P=0.008).No correlation with hand grip strength was observed.(2)Alzheimer's disease was negatively correlated with body mass index(OR=0.893;95％Cl,0.811-0.984;P=0.022);body mass index was positively correlated with appendicular lean mass(OR=1.084;95％Cl,1.031-1.141;P=0.002)and negatively correlated with walking pace(OR=0.975;95％Cl,0.969-0.980;P＜0.001).(3)Mediation analyses showed that the causal relationship between Alzheimer's disease and appendicular lean mass and walking pace was partially mediated by body mass index,with the proportion of mediations being 50.25％and 32.11％,respectively.(4)The results of this study suggest that based on large-scale population studies,genetic prediction of Alzheimer's disease is a potential risk factor for sarcopenia,in which body mass index plays an important mediating role.This suggests that in clinical practice,attention should be paid to the muscle condition of patients with Alzheimer's disease,and weight management should be implemented,as maintaining a body mass index within the normal high range may have a preventive effect on the occurrence of sarcopenia in patients with Alzheimer's disease.However,further research is needed to verify the applicability of this conclusion to other ethnic groups.This study utilized an international public database for analysis,providing a reference for research on the correlation between Alzheimer's disease and sarcopenia in the Chinese population.It also highlights the significant mediating role of body mass index,offering insights for further prevention and treatment of sarcopenia among Chinese individuals.

Objective To identify immune cell-related biomarkers in lung adenocarcinoma (LUAD) using weighted gene co-expression network analysis (WGCNA). Methods Based on data from The Cancer Genome Atlas (TCGA) database, a gene co-expression network was constructed for the TCGA-LUAD dataset using the "WGCNA" R package, and genes were clustered into different modules. Concurrently, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was applied to the tumor samples in the TCGA-LUAD dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to evaluate the biological functions of genes within the most significantly correlated module. Candidate hub genes from the key module were intersected with a protein-protein interaction (PPI) network to identify the final hub genes. The prognostic performance of these hub genes and their correlation with immune cell infiltration were validated using Kaplan-Meier curves and the Tumor IMmune Estimation Resource (TIMER) algorithm. Finally, a multivariate Cox regression analysis was conducted on the identified hub genes to construct a prognostic risk model. Results In the co-expression network, the brown module was found to be highly correlated with the ImmuneScore, StromalScore, and ESTIMATE Score. Five immune-related hub genes were identified: CD53, PLEK, SPI1, IL10RA, and C3AR1. Enrichment analysis of the brown module revealed that its genes were primarily enriched in GO terms such as "regulation of innate immune response" and KEGG pathways like the "NF-kappa B signaling pathway". Furthermore, the expression levels of these five hub genes were significantly and positively correlated with the infiltration abundance of various immune cells. The immune relevance of the model was validated by the Immunophenoscore (IPS) and the Tumor Immune Dysfunction and Exclusion (TIDE) score. Moreover, the established RiskScore demonstrated significant potential in predicting the response to immunotherapy. Conclusion These five immune-related key genes may serve as novel and effective potential therapeutic targets for LUAD immunotherapy, facilitating the development of personalized diagnosis and treatment strategies for patients with LUAD.

bjective To analyze the growth trends of height and weight among primary and secondary school students, and explore the developmental characteristics and gender differences at different age groups, and to provide a scientific basis for adolescent health policy formulation. Methods Based on 675 175 health examination records of 227 978 students aged 6-17 years in Shiyan City from 2015 to 2024, a logistic growth model was employed to fit the curves of height and weight changes with age. Results From 2015 to 2024, height and weight showed steady increases across all age groups, exhibiting typical sigmoidal growth patterns. The growth rates varied across age groups: the younger age group (6-9 years) showed a moderate growth (annual height increase of 0.5-1.0 cm, weight increase of 0.03-0.06 kg/year), while the older age group (10-17 years) demonstrated a significant growth (annual height increase of 1.5-2.0 cm, weight increase of 0.22-0.38 kg/year). The growth rate curves displayed a unimodal distribution. The growth inflection points of male students occurred later than that of female students (height inflection point: 9.87 years for males vs. 8.98 years for females; weight inflection point: 10.70 years for males vs. 9.99 years for females). Female students experienced a more concentrated but shorter period of growth and development. The peak height growth rate was 7.40 cm/year at age 9 for females and 7.09 cm/year at age 10 for males, while the peak weight growth rate was 5.04 kg/year at age 10 for females and 5.27 kg/year at age 11 for males. Conclusion The physical development of primary and secondary school students in Shiyan City follows a logistic growth pattern, with significant gender differences and characteristics of adolescent growth spurts. Female students exhibit an earlier and more concentrated growth process.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveThis paper aims to conduct the feature analysis and correlation analysis on the ocular collateral features and differential metabolites in patients with chronic hepatitis B （CHB） complicated by glucose metabolism disorder （GMD），particularly those with the damp-heat syndrome type，by integrating shadowless scleral imaging and metabolomics technologies. MethodsA total of 313 patients were recruited from the Hepatology and Endocrinology Outpatient Departments of Public Health Clinical Center of Chengdu according to the inclusion/exclusion criteria，and they were divided into a CHB group and a CHB complicated by GMD groups （damp-heat syndrome group and non-damp-heat syndrome group）. All patients underwent high-definition ocular image acquisition and feature extraction using an intelligent analysis system for shadowless scleral imaging to analyze the differences in the counting of morphological feature scores of ocular collaterals among groups. By using a digital sampling method，24 patients from each group were randomly selected，along with 20 healthy volunteers，for untargeted metabolomic analysis of peripheral serum. Differential metabolites were identified，statistically analyzed，and subjected to potential biomarker analysis and pathway enrichment. Spearman method was performed to conduct the correlation analysis on the differential ocular collateral features and differential metabolites，followed by correlation network construction. ResultsCompared with those in the CHB group，patients with CHB complicated by GMD showed significant changes in ocular collateral feature scores such as "hillock"，"blood vessels"，and "pale dusky coloration" （P<0.05）. In comparison with those in the healthy group，metabolites including N-acetylglucosamine，acetylhomoserine，and myo-inositol （AUC>0.7） were identified as potential biomarkers for the disease. Compared with those in the CHB complicated by GMD group with non-damp-heat syndrome，patients with damp-heat syndrome exhibited significant changes in feature scores of "plaques"，"yellow coloration"，"spleen"，and "gallbladder" （P<0.05）. In comparison with those in the healthy group，metabolites such as O²′-4a-cyclic tetrahydrobiopterin，theobromine，xanthurenic acid，and L-glutamic acid 5-phosphate （AUC>0.7） were identified as potential biomarkers for the damp-heat syndrome type. The Spearman correlation analysis reveals weak to moderate linear correlations between the differential scleral collateral features and metabolites. By constructing a "disease-syndrome" network of ocular diagnosis and metabolites，"xanthurenic acid-gallbladder" and "theobromine-plaque/yellow coloration" were identified as specific molecular-phenotypic correlated biomarker clusters for CHB complicated by GMD with dampness-heat syndrome. ConclusionPatients with CHB complicated by GMD demonstrate differential ocular diagnostic features and serum metabolites corresponding to disease states and dampness-heat syndrome. These objective biomarkers can guide both clinical syndrome differentiation and medication. The macro-micro integration based on ocular feature clusters and potential metabolic biomarkers offers an innovative approach to a combined traditional Chinese and Western medicine diagnosis and treatment model for this disease.

Background: Advances in intensive care have markedly improved survival after severe brain injury, leading to a growing population of patients with prolonged disorders of consciousness (pDOC). Current management of pDOC remains largely supportive, and evidence-based neuromodulatory interventions are limited; moreover, existing guidelines provide insufficiently explicit recommendations regarding mechanisms of action and objective biomarkers of treatment response. Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential noninvasive intervention; however, its modulatory effects on brain function in pDOC are not yet well characterized, and the paucity of integrative mechanistic evidence has constrained its translation into routine clinical practice. Objectives: Within a multimodal assessment framework, this study aims to systematically elucidate the neurobiological mechanisms by which taVNS modulates brain function and autonomic activity in patients with pDOC, and to evaluate its clinical potential to enhance levels of consciousness. Methods: In this randomized controlled trial, 60 patients with vegetative state/minimally conscious state will be enrolled and randomly allocated to a taVNS group, a transcutaneous nonauricular vagus nerve stimulation group (sham), or a control group (n = 20 per group) for a 4-week intervention. The primary outcome will be changes in the Coma Recovery Scale-Revised scores from baseline to weeks 1, 2, and 4 of treatment. Secondary outcomes will include functional brain activity assessed by electroencephalography and functional near-infrared spectroscopy, as well as autonomic modulation indexed by heart rate variability. Functional prognosis will be evaluated using the Glasgow Outcome Scale-Extended at the end of treatment and at a 6-month follow-up. Safety will be assessed by continuous monitoring and documentation of adverse events throughout the study period. Results and discussion: By integrating electroencephalography–functional near-infrared spectroscopy with heart rate variability, this study will characterize the effects of taVNS on functional brain networks and consciousness recovery in pDOC across complementary behavioral, electrophysiological, hemodynamic, and autonomic domains, while interrogating potential sources of clinical and neurobiological heterogeneity. The findings are expected to provide a mechanistic and evidence-based foundation for the mechanism-driven clinical implementation of taVNS and the optimization of stimulation protocols in pDOC. Clinical trial registration: International Traditional Medicine Clinical Trial Registry, ITMCTR20250021041, https://itmctr.ccebtcm.org.cn.

Objective To analyze the emergency testing results of a mixed poisoning incident involving methyl acetate and dimethyl carbonate in a composite fabric processing enterprise. Methods A mixed poisoning incident involving methyl acetate and dimethyl carbonate occurred at a composite fabric processing enterprise in a city of Guangdong Province. On-site occupational health investigation data and laboratory testing results related to this incident were collected, and an integrated analysis was conducted. Results The on-site occupational health investigation revealed inadequate ventilation and non-standard use of personal protective equipment. Volatile organic component analysis of raw and auxiliary materials showed that methyl acetate and dimethyl carbonate had relatively high percentages of peak area among all used volatile organic chemical agents in post-incident emergency testing, accounting for 65.10% and 38.25%, respectively. The exposure concentration of short term (C_STE) of methyl acetate in the production line air was 15.13 mg/m³. In the chemical storage area, C_STE of methyl acetate and methanol were 451.04 and <0.21 mg/m³, respectively, all below their respective occupational exposure limits (500.00 and 50.00 mg/m³). In the poisoned workers, blood methanol, blood formic acid, and urinary formic acid levels were 147, 414, and 4 389 mg/L, respectively. Conclusion Occupational exposure to methyl acetate and dimethyl carbonate may lead to acute occupational poisoning and cause health damage. Employers should fulfill their responsibilities for occupational disease prevention by ensuring proper operation of protective facilities, while workers should enhance awareness of occupational disease prevention and correctly use personal protective equipment.