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 explore clinical effect of closed reduction percutaneous elastic intramedullary nail assisted by arthrography in the treatment of radial neck fracture in children.Methods A retrospective analysis was performed on 23 chil-dren with radial neck fracture treated with arthrography assisted closed reduction and percutaneous elastic intramedullary nail internal fixation(arthrography with elastic nail group)from January 2019 to December 2022,including 12 males and 11 fe-males,aged from 2 to 12 years old with an average of(7.36±1.89)years old;According to Judet fracture types,14 children were type Ⅲ and 9 children were type Ⅳ.In addition,23 children with radial neck fracture were selected from January 2015 to December 2018 who were treated with closed reduction and percutaneous elastic intramedullary nail fixation(elastic nail group),including 11 males and 12 females,aged from 2 to 14 years old with an average of(7.50±1.91)years old;Judet classi-fication included 15 children were type Ⅲ and 8 children were type Ⅳ.Operative time and intraoperative fluoroscopy times were compared between two groups.Metaizeau evaluation criteria was used to evaluate fracture reduction,and Tibone-Stoltz evaluation criteria was used to evaluate functional recovery of elbow between two groups.Results Both groups were followed up for 12 to 24 months with an average of(16.56±6.34)months.Operative time and intraoperative fluoroscopy times of elastic nail group were(56.64±19.27)min and(21.13±7.87)times,while those of joint angiography with elastic nail group were(40.33±1 1.50)min and(12.10±3.52)times;there were difference between two groups(P＜0.05).According to Metaizeau evaluation,11 patients got excellent result,9 good and 3 fair in joint angiography with elastic nail group,while in elastic nail group,5 ex-cellent,13 good,4 acceptable,and 1 poor;the difference between two groups was statistically significant(P＜0.05).According to Tibone-Stoltz criteria,14 patients got excellent result,8 good,and 1 fair in joint arthrography with elastic nail group;while in elastic nail group,12 patients got excellent result,9 good,1 fair and 1 poor;there was no significant difference between two groups(P＞0.05).Conclusion Compared to percutaneous elastic intramedullary nail fixation,closed reduction assisted by arthrography has advantages of reduced operation time,decreased intraoperative fluoroscopy frequency,and improved fracture reduction.Arthrography enables clear visualization of the anatomical structures of radius,head,neck,bone,and cartilage in children,facilitating comprehensive display of fracture reduction and brachioradial joint alignment.This technique more pre-cisely guides the depth of elastic intramedullary nail implantation in radius neck,thereby enhancing surgical efficiency and success rate.

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 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 evaluate the effectiveness of oral-nasal oxygen supply mouth guard in painless gastroscopy for snoring patients.Methods:The snoring patients who underwent painless gastroscopy at two Endoscopy Centers of Shanghai East Hospital, Tongji University in July 2022 were randomly divided into the observation group (using oral-nasal oxygen supply mouth guard) and the control group (using ordinary nasal oxygen tube and mouth guard). Parameters such as the wearing time and the removal time of the mouth guard, lowest pulse oxygen saturation (SpO 2), incidence of hypoxemia, and the satisfaction of medical staff were compared between the two groups. Results:The wearing time of mouth guard was 11.63±0.84 seconds and the removal time was 5.33±0.76 seconds in the observation group ( n=40), which were lower than those in the control group ( n=47) (14.91±1.21 seconds, t=-14.463, P<0.001; 10.38±0.80 seconds, t=-30.095, P<0.001). The wearing satisfaction score was 9.80±0.61, the lowest SpO 2 was (96.70±3.42)%, the removal satisfaction score was 9.75±0.67, and the anesthesiologists' satisfaction score was 9.20±1.42 in the observation group, which were higher than those in the control group [7.70±0.93, t=12.209, P<0.001; (94.06±3.72)%, t=3.417, P=0.001; 7.96±0.98, t=9.803, P<0.001; 8.13±1.35, t=3.615, P=0.001] with significant difference. There was no significant difference in the incidence of hypoxemia [10.00% (4/40) VS 14.89% (7/47), χ2=0.130, P=0.718] and endoscopic physician satisfaction score (9.30±0.97 VS 9.02±1.31, t=1.112, P=0.269) between the two groups. Conclusion:The oral-nasal oxygen supply mouth guard is easy to wear and remove, effectively reducing SpO 2 fluctuations during painless gastroscopy for snoring patients. It can enhance medical staff satisfaction with high clinical value.

Protein phosphorylation modification is an important mechanism of physiological regulation that is closely related to protein biological functions. In particular, protein kinases are responsible for catalyzing the phosphorylation process of proteins, and phosphatases are responsible for catalyzing the dephosphorylation process of phosphorylation-modified proteins, which together mediate the achievement of dynamic and reversible phosphorylation modifications of proteins. Abnormal phosphorylation levels of proteins contribute to the development of many diseases, such as cancer, neurodegenerative diseases, and chronic diseases. Therefore, rational design of small molecules to regulate protein phosphorylation is an important approach for disease treatment. Based on the mechanism of protein phosphorylation regulation, small molecule drug design strategies can be classified into three types, protein kinase modulators, phosphatase modulators, and bifunctional molecules with proximity-mediated mechanism. This review emphasizes the above three small molecule design strategies for targeting protein phosphorylation regulation, including molecular design ideas, research progress and current challenges, and provides an outlook on small molecule modulators targeting protein phosphorylation modification.

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.

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.