Objective To investigate the toxicokinetic differences of 3,4-methylenedioxy-N-methylamphetamine(MDMA)and its metabolite 4,5-methylene dioxy amphetamine(MDA)in rats af-ter single and continuous administration of MDMA,providing reference data for the forensic identifica-tion of MDMA.Methods A total of 24 rats in the single administration group were randomly divided into 5,10 and 20 mg/kg experimental groups and the control group,with 6 rats in each group.The ex-perimental group was given intraperitoneal injection of MDMA,and the control group was given intraperi-toneal injection of the same volume of normal saline as the experimental group.The amount of 0.5 mL blood was collected from the medial canthus 5 min,30 min,1 h,1.5 h,2 h,4 h,6 h,8 h,10 h,12 h after administration.In the continuous administration group,24 rats were randomly divided into the experi-mental group(18 rats)and the control group(6 rats).The experimental group was given MDMA 7 d by continuous intraperitoneal injection in increments of 5,7,9,11,13,15,17 mg/kg per day,respectively,while the control group was given the same volume of normal saline as the experimental group by in-traperitoneal injection.On the eighth day,the experimental rats were randomly divided into 5,10 and 20 mg/kg dose groups,with 6 rats in each group.MDMA was injected intraperitoneally,and the con-trol group was injected intraperitoneally with the same volume of normal saline as the experimental group.On the eighth day,0.5 mL of blood was taken from the medial canthus 5 min,30 min,1 h,1.5 h,2 h,4 h,6 h,8 h,10 h,12 h after administration.Liquid chromatography-triple quadrupole tandem mass spectrometry was used to detect MDMA and MDA levels,and statistical software was employed for data analysis.Results In the single-administration group,peak concentrations of MDMA and MDA were reached at 5 min and 1 h after administration,respectively,with the largest detection time limit of 12 h.In the continuous administration group,peak concentrations were reached at 30 min and 1.5 h af-ter administration,respectively,with the largest detection time limit of 10 h.Nonlinear fitting equations for the concentration ratio of MDMA and MDA in plasma and administration time in the single-administration group and continuous administration group were as follows:T=10.362C-1.183,R2=0.974 6;T=7.397 3C-0.694,R2=0.961 5(T:injection time;C:concentration ratio of MDMA to MDA in plasma).Conclusions The toxicokinetic data of MDMA and its metabolite MDA in rats,obtained through single and continuous administration,including peak concentration,peak time,detection time limit,and the relationship between concentration ratio and administration time,provide a theoretical and data foundation for relevant forensic identification.

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 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 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.

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.

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 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 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: 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: 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.