BACKGROUND: Chronic liver disease (CLD), mainly non-alcoholic fatty liver disease (NAFLD), is a significant public health concern worldwide. This study aims to quantify the burden of NAFLD in CLD globally and within China, using data from the Global Burden of Disease (GBD) Study 2021, providing crucial insights for global and local health policies. METHODS: The study used comprehensive data from the GBD study 2021. It included estimates of prevalence, incidence, mortality, and disability-adjusted life years (DALYs). Age-standardized rates and average annual percent change (AAPC) from 2011 to 2021 were reported. A meticulous decomposition analysis was conducted. RESULTS: In 2021, there were 1582.5 million prevalent cases, 47.6 million incident cases, 1.4 million deaths, and 44.4 million DALYs attributable to CLD, globally. Among these, NAFLD has emerged as the predominant cause, accounting for 78.0% of all prevalent CLD cases (1234.7 million) and 87.2% of incident cases (41.5 million). Correspondingly, NAFLD had the highest age-standardized prevalence (15,017.5 per 100,000 population) and incidence (876.5 per 100,000 population) rates among CLDs. In addition, China's CLD age-standardized prevalence rate was 21,659.5 per 100,000 population, and the age-standardized incidence rate was 752.6 per 100,000 population, higher than the global average. From 2011 to 2021, the global prevalence rate of CLD increased slowly (AAPC = 0.17), consistent with the trend in China (AAPC = 0.23). Furthermore, the prevalence rate of NAFLD rose significantly in China (AAPC = 1.30) compared with the global average (AAPC = 0.91). Decomposition analysis also showed the worldwide increase in deaths and DALYs for NAFLD, which were primarily attributable to population growth and aging. CONCLUSIONS The burden of CLD and NAFLD remains substantial globally and within China in terms of high prevalence and incidence. As such, this underscores the need for targeted prevention and treatment strategies. These findings emphasize the importance of continued surveillance and research to mitigate the growing impact of liver diseases on global and Chinese health systems.
Humans ; Non-alcoholic Fatty Liver Disease/mortality* ; Global Burden of Disease ; China/epidemiology* ; Prevalence ; Male ; Disability-Adjusted Life Years ; Female ; Incidence ; Middle Aged ; Chronic Disease ; Adult ; Quality-Adjusted Life Years ; Liver Diseases/epidemiology* ; Aged

The therapeutic effects of Yueju Pills on depression and cardiovascular diseases have been widely recognized. Previous studies have shown that the drug can significantly improve depressive-like behaviors induced by chronic unpredictable mild stress(CUMS) combined with atherosclerosis(AS). Given the complex pathogenesis of psychocardiac diseases, this study integrated metabolomics and network pharmacology to systematically elucidate the mechanism of Yueju Pills in alleviating depressive symptoms in psychocardiac diseases. The results demonstrate that, after Yueju Pill intervention, the levels of 9 abnormal metabolites in the hippocampus restore to normal ranges, primarily involving key pathways or signaling pathways, including the cyclic adenosine monophosphate(cAMP), mammalian target of rapamycin(mTOR), glycine/serine/threonine metabolism, and aminoacyl-tRNA biosynthesis. In a high-fat diet-induced CUMS ApoE~(-/-) mouse model, Yueju Pills significantly increases adenosine monophosphate(AMP) levels and decreases L-alanine and D-glyceric acid levels in the hippocampus. In conclusion, Yueju Pills exert antidepressant effects by regulating multiple metabolic axes, including glycine/serine/threonine metabolism and the cAMP, mTOR signaling pathways. Network pharmacology predictions reveal that the treatment of CUMS combined with AS by its core active components may be realized through modulating pathways concerning neuroinflammation and synaptic plasticity, including serine/threonine-protein kinase 1(AKT1), mitogen-activated protein kinase 1(MAPK1), and prostaglandin-endoperoxide synthase 2(PTGS2). This study provides a theoretical reference for the clinical application of Yueju Pills in alleviating the depressive symptoms of psychocardiac diseases.
Animals ; Network Pharmacology ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Metabolomics ; Male ; Depression/genetics* ; Humans ; Hippocampus/drug effects* ; Mice, Inbred C57BL ; Signal Transduction/drug effects*

Objective To observe the effect of Shenqi Chongcao (SQCC) Formula on the ASS1/src/STAT3 signaling pathway in a rat model of lung fibrosis and explore its therapeutic mechanism. Methods A total of 120 male SD rats were divided equally into 5 groups, including a blank control group with saline treatment and 4 groups of rat models of idiopathic pulmonary fibrosis induced by intratracheal instillation of bleomycin. One day after modeling, the rat models were treated with daily gavage of 10 mL/kg saline, SQCC decoction (0.423 g/kg), pirfenidone (10 mL/kg), or intraperitoneal injection of arginine deiminase (ADI; 2.25 mg/kg, every 3 days) for 28 days. After the treatments, the lung tissues of the rats were collected for calculating the lung/body weight ratio, observing histopathology using HE and Masson staining, and analyzing the inflammatory cells in BALF using Giemsa staining. Serum chemokine ligand 2 (CCL2) and transforming growth factor-β1 (TGF-β1) levels were measured with ELISA. The protein expressions of src, p-srcTry529, STAT3, and p-STAT3Try705 and the mRNA expressions of ASS1, src and STAT3 in the lung tissues were detected using Western blotting and RT-qPCR. Results The neutrophil, macrophage and lymphocyte counts and serum levels of CCL2 and TGF-β1 were significantly lower in SQCC, pirfenidone and ADI treatment groups than in the model group at each time point of measurement (P<0.05). P-srcTry529 and p-STAT3Try705 protein expression levels and ASS1, src, and STAT3 mRNA in the lung tissues were also significantly lower in the 3 treatment groups than in the model group (P<0.05). Conclusion SQCC Formula can alleviate lung fibrosis in rats possibly by activating the ASS1/src/STAT3 signaling pathway in the lung tissues.

Objective To observe the effect of Shenqi Chongcao (SQCC) Formula on the ASS1/src/STAT3 signaling pathway in a rat model of lung fibrosis and explore its therapeutic mechanism. Methods A total of 120 male SD rats were divided equally into 5 groups, including a blank control group with saline treatment and 4 groups of rat models of idiopathic pulmonary fibrosis induced by intratracheal instillation of bleomycin. One day after modeling, the rat models were treated with daily gavage of 10 mL/kg saline, SQCC decoction (0.423 g/kg), pirfenidone (10 mL/kg), or intraperitoneal injection of arginine deiminase (ADI; 2.25 mg/kg, every 3 days) for 28 days. After the treatments, the lung tissues of the rats were collected for calculating the lung/body weight ratio, observing histopathology using HE and Masson staining, and analyzing the inflammatory cells in BALF using Giemsa staining. Serum chemokine ligand 2 (CCL2) and transforming growth factor-β1 (TGF-β1) levels were measured with ELISA. The protein expressions of src, p-srcTry529, STAT3, and p-STAT3Try705 and the mRNA expressions of ASS1, src and STAT3 in the lung tissues were detected using Western blotting and RT-qPCR. Results The neutrophil, macrophage and lymphocyte counts and serum levels of CCL2 and TGF-β1 were significantly lower in SQCC, pirfenidone and ADI treatment groups than in the model group at each time point of measurement (P<0.05). P-srcTry529 and p-STAT3Try705 protein expression levels and ASS1, src, and STAT3 mRNA in the lung tissues were also significantly lower in the 3 treatment groups than in the model group (P<0.05). Conclusion SQCC Formula can alleviate lung fibrosis in rats possibly by activating the ASS1/src/STAT3 signaling pathway in the lung tissues.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective:To analyze the clinical characteristics and prognosis of patients with CD5+diffuse large B-cell lymphoma(DLBCL).Methods:The clinical data of 161 newly treated DLBCL patients in Gansu Provincial Hospital from January 2013 to January 2020 were retrospectively analyzed.According to CD5 expression,the patients were divided into CD5+group and CD5-group.The clinical characteristics and prognosis of the two groups were statistically analyzed.Results:The median age of patients in CD5+group was 62 years,which was higher than 56 years in CD5-group(P=0.048).The proportion of women in CD5+group was 62.96％,which was significantly higher than 41.79％in CD5-group(P=0.043).The proportion of patients with IPI score＞2 in CD5+group was 62.96％,which was higher than 40.30％in CD5-group(P=0.031).Survival analysis showed that the median overall survival and progression-free survival time of patients in CD5+group were 27(3-77)and 31(3-76)months,respectively,which were both shorter than 30(5-84)and 32.5(4-83)months in CD5-group(P=0.047,P=0.026).Univariate analysis showed that advanced age,positive CD5 expression,triple or double hit at initial diagnosis,high IPI score and no use of rituximab during chemotherapy were risk factors for the prognosis of DLBCL patients.Further Cox multivariate regression analysis showed that these factors were also independent risk factors except for advanced age.Conclusion:CD5+DLBCL patients have a worse prognosis than CD5-DLBCL patients.Such patients are more common in females,with advanced age and high IPI score,which is a special subtype of DLBCL.

Objective To carry out rigid-body inverse dynamics modelling and analysis of a self-designed 6RSS parallel bio-inspired masticatory robot.Methods Firstly,the functions of kinematic variables including translational/rotational velocities and accelerations were derived for rigid-body inverse dynamics modelling.Secondly,the rigid-body inverse dynamics model was established with the Newton-Euler's law.Finally,the chewing motion trajectories of the oral health volunteers were tracked and numerical calculations were carried out in the case where the robot was subjected to a chewing reaction force.Results Numerical calculations showed that the driving torque and the constraint force of the robot peaked when the chewing reaction force was at its maximum.Conclusion The external force has a large impact on the inverse dynamics of the robot,and theoretical references are provided for the motion control and optimal design of the robot.[Chinese Medical Equipment Journal,2024,45(3):16-22]

Objective To analyze the molecular markers of various nuclei in the human basal ganglia and the differentially expressed genes(DEGs)among different nuclei,gender,and Parkinson's disease(PD),followed by the biological function annotations of the DEGs.Methods Forty-five specimens of basal ganglia from 10 human postmortem brains were divided into control and PD groups,and the control group was further categorized into female and male groups.RNA from each sample was extracted for high-throughput transcriptome sequencing.Bioinformatic analysis was conducted to identify molecular markers of each nuclei in the control group,nuclei-specific,gender-specific,and PD-specific DEGs,followed by gene enrichment analysis and functional annotation.Results Sequencing analysis revealed top DEGs such as DRD1,FOXG1,and FAM183A in the caudate;SLC6A3,EN1,SLC18A2,and TH in the substantia nigra;MEPE and FGF10 in the globus pallidus;and SLC17A6,PMCH,and SHOX2 in the subthalamic nucleus.In them,putamen showed some overlapping DEGs with caudate,such as DRD1 and FOXG1.A significant number of DEGs were identified among different nuclei in the control group,with the highest number between caudate and globus pallidus(9321),followed by putamen and globus pallidus(6341),caudate and substantia nigra(6054),and substantia nigra and subthalamic nucleus(44).Gene enrichment analysis showed that downregulated DEGs between caudate and globus pallidus were significantly enriched in processes like myelination of neurons and cell migration.Upregulated DEGs between putamen and globus pallidus were enriched processes like chemical synaptic transmission and regulation of membrane potential,while downregulated DEGs were enriched in myelination and cell adhesion.Upregulated DEGs between caudate and substantia nigra were enriched in processes like chemical synaptic transmission and axonal conduction,while downregulated DEGs were enriched in myelination of neurons.Totally 468,548,1402,333,and 341 gender-specific upregulated DEGs and 756,988,2532,444,and 1372 downregulated DEGs were identified in caudate,putamen,substantia nigra,globus pallidus,and subthalamus nucleus.Gene enrichment analysis revealed upregulated DEGs mostly enriched in pathways related to immune response and downregulated DEGs in chemical synaptic transmission.At last,709,852,276,507,and 416 PD-specific upregulated DEGs and 830,2014,1218,836,and 1730 downregulated DEGs were identified in caudate,putamen,substantia nigra,globus pallidus,and subthalamus nucleus.Gene enrichment analysis revealed upregulated DEGs mostly enriched in apoptotic regulation and downregulated DEGs in chemical synaptic transmission and action potential regulation.Conclusion We identified and analysed the molecular markers of different human basal ganglia nuclei,as well as DEGs among different nuclei,different gender,and between control and PD.

This paper briefly introduced the varying coefficient model and used the varying coefficient Cox model as an example to demonstrate its application in the fields of medicine and public health through real data analysis,thereby provided methodological references for related research.The example was based on chronic disease management data from a Center for Disease Control and Prevention,fitting a varying coefficient Cox model to explore the time-varying association between body mass index(BMI)and mortality risk among a hypertensive population.The results showed that being underweight(BMI＜18.5 kg/m2)was associated with a higher risk of mortality,but this association weakened over time;being overweight(23 kg/m2≤BMI＜25 kg/m2)was associated with a lower risk of mortality,and this association also weakened over time.The varying coefficient model captured how the impact of exposure factors on outcomes changed with other variables,helping to comprehensively understand the complex relationships between variables,and had significant application and promotion value in medical and public health research.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.