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.

It is proposed that the core pathogenesis of porto-sinusoidal vascular disease （PSVD） is characterized by deficiency of healthy qi with collateral stagnation and the binding of stasis and toxin. According to the characteristics of its microvascular lesions， PSVD can be treated through syndrome differentiation in three stages， including the occult stage， progressive stage， and decompensated stage. Considering the properties of insect-derived medicinals， such as rapid movement， penetrating action， and ability to search out pathogen and unblock collaterals， three therapeutic principles are established， which are unblocking collaterals， softening the liver， and dispelling pathogen. Clinically， the New-Modified Chaihu Biejia Decoction （柴胡鳖甲汤） is used as the basic prescription with staged modifications. During the occult stage， medicinals such as Quanxie （Scorpio） and Wugong （Scolopendra） are added to search out pathogen， unblock collaterals， and protect the channels from progression. During the progressive stage， Tubiechong （Eupolyphaga seu Steleophaga） and Qianglang （Catharsius molossus） are added to dispel stasis and soften hard masses， thereby removing concretions and dissipating masses. During the decompensated stage， Shuizhi （Hirudo） and Dilong （Pheretima） are added to eliminate stasis and promote urination， treating blood and fluid retention simultaneously.

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.