Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

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.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology has the characteristics of high specificity and high throughput, making it rapidly applied and developed in the field of clinical testing. Its application in the monitoring of therapeutic drugs can effectively improve the quantitative accuracy and sensitivity, and formulate a personalized and optimal dosing plan for patients. However, this technology still faces some challenges, and automation, quality control, and quantitative traceability will be the future development direction.

Objective To observe the cage subsidence after oblique lateral interbody fusion(OLIF)for lumbar spondylo-sis,summarize the characteristics of the cage subsidence,analyze causes,and propose preventive measures.Methods The data of 144 patients of lumbar spine lesions admitted to our hospital from October 2015 to December 2018 were retrospectively ana-lyzed.There were 43 males and 101 females,and the age ranged from 20 to 81 years old,with an average of(60.90±10.06)years old.Disease types:17 patients of lumbar intervertebral disc degenerative disease,12 patients of giant lumbar disc hernia-tion,5 patients of discogenic low back pain,33 patients of lumbar spinal stenosis,26 patients of lumbar degenerative spondy-lolisthesis,28 patients of lumbar spondylolisthesis with spondylolisthesis,11 patients of adjacent vertebral disease after lumbar internal fixation,7 patients of primary spondylitis in the inflammatory outcome stage,and 5 patients of lumbar degenerative scoliosis.Preoperative dual-energy X-ray bone mineral density examination showed 57 patients of osteopenia or osteoporosis,and 87 patients of normal bone density.The number of fusion segments:124 patients of single-segment,11 patients of two-seg-ment,8 patients of three-segment,four-segment 1 patient.There were 40 patients treated by stand-alone OLIF,and 104 patients by OLIF combined with posterior pedicle screw.Observed the occurrence of fusion cage settlement after operation,conducted monofactor analysis on possible risk factors,and observed the influence of fusion cage settlement on clinical results.Results All operations were successfully completed,the median operation time was 99 min,and the median intraoperative blood loss was 106 ml.Intraoperative endplate injury occurred in 30 patients and vertebral fracture occurred in 5 patients.The mean follow-up was(14.57±7.14)months from 6 to 30 months.During the follow-up,except for the patients of primary lumbar interstitial in-flammation and some patients of lumbar spondylolisthesis with spondylolisthesis,the others all had different degrees of cage subsidence.Cage subsidence classification:119 patients were normal subsidence,and 25 patients were abnormal subsidence(23 patients were grade Ⅰ,and 2 patients were grade Ⅱ).There was no loosening or rupture of the pedicle screw system.The height of the intervertebral space recovered from the preoperative average(9.48±1.84)mm to the postoperative average(12.65±2.03)mm,and the average(10.51±1.81)mm at the last follow-up.There were statistical differences between postop-erative and preoperative,and between the last follow-up and postoperative.The interbody fusion rate was 94.4％.The low back pain VAS decreased from the preoperative average(6.55±2.2 9)to the last follow-up(1.40±0.82),and there was statistically significant different.The leg pain VAS decreased from the preoperative average(4.72±1.49)to the final follow-up(0.60± 0.03),and the difference was statistically significant(t=9.13,P＜0.000 1).The ODI index recovered from the preoperative av-erage(38.50±6.98)％to the latest follow-up(11.30±3.27)％,and there was statistically significant different.The complication rate was 31.3％(45/144),and the reoperation rate was 9.72％(14/144).Among them,8 patients were reoperated due to fusion cage subsidence or displacement,accounting for 57.14％(8/14)of reoperation.The fusion cage subsidence in this group had obvious characteristics.The monofactor analysis showed that the number of abnormal subsidence patients in the osteopenia or osteoporosis group,Stand-alone OLIF group,2 or more segments fusion group,and endplate injury group was higher than that in the normal bone mass group,OLIF combined with pedicle screw fixation group,single segment fusion group,and no endplate injury group,and the comparison had statistical differences.Conclusion Cage subsidence is a common phenomenon after 0-LIF surgery.Preoperative osteopenia or osteoporosis,Stand-alone OLIF,2 or more segments of fusion and intraoperative end-plate injury may be important factors for postoperative fusion cage subsidence.Although there is no significant correlation be-tween the degree of cage subsidence and clinical symptoms,there is a risk of cage migration,and prevention needs to be strengthened to reduce serious complications caused by fusion of cage subsidence,including reoperation.

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.