It is considered that lean metabolic associated fatty liver disease （MAFLD） is characte-rized by a state of "lean body but fatty liver". From the perspective of the pathomechanism evolution of "spleen failing to disperse essence， while kidney failing to transform qi"， the disease is systematically treated under the principle that spleen and kidney deficiency as the root and liver constraint with phlegm stasis as the branch. Spleen deficiency with impaired transformation and transportation leads to failure in the distribution of refined substances， initiating lipid turbidity accumulation and obstruction. Kidney dysfunction in qi transformation results in insufficient essence and blood， forming the root of bodily malnourishment. Over time， this further gives rise to liver constraint and loss of dispersing function， disorder in the movement of essential substances， and congealing of phlegm and stasis within hepatic collaterals. The dysfunction of three organs including spleen， kidney and liver constitutes the pathological basis of metabolic disturbances involving qi， blood， body fluids and essence. Treatment should follow the principle of "nourishing the healthy qi leads to spontaneous resolution of pathogenic accumulation"， with three approaches. First， fortifying the spleen and eliminating dampness， using Sini Powder （四逆散） and Erchen Pingwei Powder （二陈平胃散） to restore the transport and transformation function of the middle jiao （焦）， thereby guiding essential substances back to their proper physiological pathways. Second， tonifying the kidney and resolving turbidity， using Jinkui Shenqi Pill （金匮肾气丸） and Effective Integration Decoction （一贯煎） to replenish the lower jiao， promoting the restoration of essence and the transformation of turbid pathogen. Third， soothing the liver and resolving stasis， using Liujunzi Decoction （六君子汤） and Xiaozhi Qinggan Decoction （消脂清肝汤） to vent qi and blood stagnation， thereby interrupting disease progression. Together， these three methods reinforce healthy qi while eliminating pathogen， facilitating the restoration of proper essence transformation and the resolution of lipid turbidity.

Objective: To examine the association between parent-child relationships and bullying behaviors among junior high school students, and to explore the moderating effect of community cohesion, so as to provide evidences for bullying intervention strategies. Methods: From November to December 2017, a cluster sampling method was used to survey 1 589 students in grades 6- 8 from three junior high schools in Jing an District,Shanghai. Anonymous electronic questionnaires collected data on parent-child relationships, community cohesion, and bullying behaviors. Multivariate Logistic regression analyzed the associations and moderation effects. Results: The prevalence of bullying behaviors among junior high school students was 7.80%. Spearman correlation analysis revealed negative associations between both parent-child relationships ( r =-0.13) and community cohesion ( r =-0.10) with bullying behaviors, while parent-child relationships positively correlated with community cohesion ( r =0.29) (all P <0.01). Junior high school students with positive parent-child relationships and higher perceived community cohesion showed lower risks of bullying behaviors ( OR=0.51, 95%CI =0.36-0.72; OR=0.58, 95%CI =0.45-0.76), with a significant interaction effect between the two factors (all P <0.05). Conclusions Positive parent-child relationships and community cohesion are negatively associated with bullying behaviors in middle school students. Supportive family relationships help reduce bullying, while stronger community cohesion enhances the protective effect of positive parent-child relationships against bullying.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

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.

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 explore the design points of a three-dimensional (3D) printed customized cementless intercalary endoprosthesis with an intra-neck curved stem and to evaluate the key points and mid-term effectiveness of its application in the reconstruction of ultrashort bone segments in the proximal femur. METHODS: Between October 2015 and January 2021, 17 patients underwent reconstruction with a 3D printed-customized cementless intercalary endoprosthesis with an intra-neck curved stem. There were 11 males and 6 females, the age ranged from 10 to 76 years, with an average of 30.1 years. There were 9 cases of osteosarcoma, 4 cases of Ewing sarcoma, 2 cases of chondrosarcoma, 1 case of liposarcoma, and 1 case of myofibroblastoma. The disease duration was 5-14 months, with an average of 9.5 months. Enneking staging included 16 cases of stage ⅡB and 1 case of stage ⅢB. The distances from the center of the femoral head to the body midline and the acetabular apex were measured preoperatively on X-ray images. Additionally, the distances from the tip of the intra-neck curved stem to the body midline and the acetabular apex were measured at immediate postoperatively and last follow-up. The neck-shaft angle was also measured preoperatively, at immediate postoperatively, and at last follow-up. The status of osseointegration at the bone-prosthesis interface and bone growth into the prosthesis surface were assessed by X-ray films, CT, and Tomosynthesis-Shimadzu metal artefact reduction technology (T-SMART). The survival status of the patients, presence of local recurrence or distant metastasis, and occurrence of postoperative complications were assessed. The recovery of lower limb function was evaluated pre- and post-operatively using the Musculoskeletal Tumor Society (MSTS) scoring system, and pain relief was evaluated using the visual analogue scale (VAS) scores. RESULTS: The patient's femoral resection length was (163.1±57.5) mm, the remaining proximal femoral length was (69.6±9.3) mm, and the percentage of femoral resection length/total femoral length was 38.7%±14.6%. All 17 patients were followed up 25-86 months with an average of 58.1 months. During the follow-up, 1 patient died of lung metastasis at 46 months postoperatively, and the remaining 16 patients survived tumor-free. There was no complication such as periprosthetic infection, delayed incision healing, aseptic loosening, prosthesis fracture, or periprosthetic fracture. No evidence of micromotion or wear around the implanted stem of the prosthesis was detected in X-ray and T-SMART evaluations. There was no significant radiolucent lines, and radiographic evidence of bone ingrowth into the bone-prosthesis interface was observed in all stems. There was no significant difference in the distance from the tip of the curved stem to the body midline and the apex of the acetabulum at immediate postoperatively and last follow-up compared with the distance from the center of the femoral head to the body midline and the apex of the acetabulum before operation, respectively (P>0.05), and there was no significant difference in the above indexes between immediate postoperatively and last follow-up (P>0.05). The differences in the neck-shaft angle at various time points before and after operation were also not significant (P>0.05). At last follow-up, the MSTS score was 26.1±1.2 and the VAS score was 0.1±0.5, which were significantly improved when compared with those before operation [19.4±2.1 and 5.7±1.0, respectively] (t=14.735, P<0.001; t=21.301, P<0.001). At last follow-up, none of the patients walked with the aid of crutches or other walkers. CONCLUSION The 3D printed customized cementless intercalary endoprosthesis with an intra-neck curved stem is an effective method for reconstructing ultrashort bone segments in the proximal femur following malignant tumor resection. The operation is reliable, the postoperative lower limb function is satisfactory, and the incidence of complications is low.
Female ; Male ; Humans ; Child ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Aged ; Femur/surgery* ; Lower Extremity ; Bone-Implant Interface ; Femur Head ; Artificial Limbs