Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective To explore the effect of different post-processing methods of total spinal CT angiography(CTA)for displaying spinal dural arteriovenous fistula(SDAVF).Methods Total spinal CTA data of 55 patients with SDAVF were retrospectively analyzed.Traditional post-processing of original CTA images(modified tissue growth boneless volume rendering[VR]and full-range axial maximum intensity projection[MIP])were performed,while the axial,sagittal and coronal MIP reconstructions,axial,sagittal and coronal VR reconstructions,as well as axial,sagittal and coronal MIP+VR reconstructions of original CTA images on lesion layers were completed,respectively.Taken digital subtraction angiography(DSA)as the gold standards,a 5-point scale was used to subjectively evaluate the effect of displaying the location,the range and feeding artery of fistula shown on CTA images based on different post-processing methods.Results No significant difference of subjective score of location nor feeding artery of fistula was found among axial MIP,VR and MIP+VR images(all P＞0.05),which were all higher than that of CTA images reconstructed using other post-processing methods(all P＜0.05).Meanwhile,no significant difference of subjective scores of the range of SDAVF was detected among sagittal MIP,VR and MIP+VR images(all P＞0.05),which were all higher than that of CTA images obtained using other post-processing methods(all P＜0.05).Conclusion The location and feeding artery of SDAVF could be observed based on axial MIP,VR and MIP+VR reconstructions of the total spinal CTA,while sagittal MIP,VR and MIP+VR reconstructions were conducive to display the range of SDAVF.Combination of multiple post-processing methods was helpful for comprehensive understanding the composition and range of SDAVF.

Objective To explore the effect of different post-processing methods of total spinal CT angiography(CTA)for displaying spinal dural arteriovenous fistula(SDAVF).Methods Total spinal CTA data of 55 patients with SDAVF were retrospectively analyzed.Traditional post-processing of original CTA images(modified tissue growth boneless volume rendering[VR]and full-range axial maximum intensity projection[MIP])were performed,while the axial,sagittal and coronal MIP reconstructions,axial,sagittal and coronal VR reconstructions,as well as axial,sagittal and coronal MIP+VR reconstructions of original CTA images on lesion layers were completed,respectively.Taken digital subtraction angiography(DSA)as the gold standards,a 5-point scale was used to subjectively evaluate the effect of displaying the location,the range and feeding artery of fistula shown on CTA images based on different post-processing methods.Results No significant difference of subjective score of location nor feeding artery of fistula was found among axial MIP,VR and MIP+VR images(all P＞0.05),which were all higher than that of CTA images reconstructed using other post-processing methods(all P＜0.05).Meanwhile,no significant difference of subjective scores of the range of SDAVF was detected among sagittal MIP,VR and MIP+VR images(all P＞0.05),which were all higher than that of CTA images obtained using other post-processing methods(all P＜0.05).Conclusion The location and feeding artery of SDAVF could be observed based on axial MIP,VR and MIP+VR reconstructions of the total spinal CTA,while sagittal MIP,VR and MIP+VR reconstructions were conducive to display the range of SDAVF.Combination of multiple post-processing methods was helpful for comprehensive understanding the composition and range of SDAVF.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective To explore the effect of galangin(Gal)on inflammation in hepatitis B rats.Methods The rats were randomly divided into control group,hepatitis B group,Gal-L and Gal-H groups,positive drug lamivudine group and Gal-H+AMPK inhibitor(compound C)group with 12 in each.After modeling,medication treatment was performed once a day for 8 weeks.The level of alanine aminotransferase(ALT),total bilirubin(TBIL)and aspartate aminotransferase(AST)in the serum of rats were detected.HE staining microscopy was ap-plied to detect pathological changes in liver tissue.TUNEL staining microscopy was applied to detect cell apoptosis in liver tissue.Chromatin immuno-precipitation was applied to detect HBV viral load in liver tissue.ELISA was ap-plied to detect the levels of monocyte chemotactic protein-1(MCP-1),interleukin-12(IL-12),and tumor necrosis factor-α(TNF-α)in liver tissue.Western blot was applied to detect the expression of caspase-3,Bcl-2 associated X protein(Bax),p-AMPK and SIRT1 proteins in liver tissue.Results Compared with hepatitis B group,the liver damage of rats in the Gal-L group,Gal-H group and lamivudine group was alleviated;The level of serum AST,TBIL,and ALT,apoptosis rate,HBV viral load and the level of MCP-1,IL-12,TNF-α as well as the expression of caspase-3 and Bax proteins in liver tissue were all reduced.The expression of p-AMPK and SIRT1 proteins in liv-er tissue increased(P＜0.05).Compound C baffled inhibitory effects of high-dose Gal on inflammation,cell apopto-sis,and HBV viral load in liver tissue of hepatitis B rats.Conclusions The mechanism of Gal in inhibiting inflam-mation,cell apoptosis and HBV virus replication in hepatitis B rats is potentially attributed to up-regulation of the AMPK/SIRT1 pathway.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.

With the broad application of high-resolution computed tomography (CT) and high rates of early lung cancer screening, the number of patients diagnosed with synchronous multiple primary lung cancer (sMPLC) has been increasing. It becomes of great prominence to distinct sMPLC from intrapulmonary metastases in clinical practice. An increasing number of studies have developed high-throughput sequencing based genetic approaches to specify the molecular characteristics of sMPLC, which contributes to a better understanding of its tumorigenesis. The genetic profile of sMPLC also benefits its diagnosis, which mainly relies on its clinicopathological criteria. Here, we summarize the progresses on the diagnostic criteria for sMPLC, and also molecular features of sMPLC from the perspective of clonality analysis.

[Abstract] Objective: To explore the mechanism of miR-145-5p on malignant biological behaviors, such as pro-liferation, invasion, migration and epithelial-mesenchymal transition (EMT), of esophageal squamous cell carcinoma (ESCC) TE-10 cells. Methods: The expression of miR-145-5p in ESCC cell lines and normal cells was detected by PCR. Dual luciferase reporter gene assay was used to detect the targeted regulation between miR-145-5p and insulin-like growth factor 1 receptor (IGF1R). The expres-sions of IGF1R protein and EMT related proteins were detected by Western blotting. Transwell assay and CCK-8 assay were carried out to detect the effects of miR-145-5p/IGF1R axis on the proliferation, migration andinvasionofTE-10 cells. Results: miR-145-5p was down-regulated in ESCC cell lines with the lowest expression in TE-10 cells (P<0.01orP<0.05).Over-expression of miR-145-5p significantly inhibited proliferation, invasion, migration and EMT of TE-10 cells (P<0.01 or P<0.05). Dual luciferase reporter gene assay con-firmed that miR-145-5p targetedly down-regulated IGF1R expression (P<0.01). The restora-tion experiments further confirmed that simultaneous over-expression of miR-145-5p and IGF1R significantly attenuated the promotion effect of IGF1R on proliferation, invasion, migration and EMT of TE-10 cells (P<0.01 or P<0.05). Conclusions: Over-expression of miR-145-5p inhibits proliferation, invasion, migration and EMT of ESCC TE-10 cells by down-regulating IGF1R.