Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Quercetin can mediate the activation of mitogen-activated protein kinase(MAPK)signaling pathways to prevent osteoporosis(OP).This paper comprehensively discusses the interrelationship between MAPK and osteoporosis-related cells based on the latest domestic and international research.Additionally,it elucidates the research progress of quercetin in mediating the MAPK signaling pathway for OP prevention.The aim is to provide an effective foundation for the clinical prevention and treatment of OP and the in-depth development of quercetin.

ObjectiveTo explore the molecular mechanism of Sanhuang Xiexintang （SHXXT） in protecting stress gastric ulcer （SGU） in rats through network pharmacology， molecular docking， and animal experiments. MethodThe active ingredients and corresponding targets in SHXXT were collected and screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP）， Traditional Chinese Medicine Information Database （TCMID）， Bioinformation Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine （BATMAN-TCM）， and Swiss Target Prediction database. SGU-related targets were screened from the Online Mendelian Inheritance in Man （OMIM）， Therapeutic Target Database （TTD）， GeneCards database， and PharmGKB database. Herbal-ingredient-target （H-C-T） network was constructed by using Cytoscape 3.9.1 software. Protein-protein interaction （PPI） of drug and disease intersection targets was analyzed by using the Protein Interaction Platform （STRING） database. Gene ontology （GO） enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analysis were conducted through the Database for Annotation Visualization and Integrated Discovery （DAVID）. The active ingredients and key targets were validated using AutodockVina 1.2.2 molecular docking software， and the experimental results were further validated through animal experiments. ResultThe 55 active ingredients were screened， and 255 potential target genes for SHXXT treatment of SGU were predicted. The PPI analysis showed that protein kinase B （Akt）， phosphatase and tensin homolog deleted on chromosome ten （PTEN）， tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and cyclooxygenase-2 （COX-2） are the core targets of SHXXT for protecting SGU. GO and KEGG analyses showed that SHXXT may affect the development of SGU by regulating various biological processes such as the phosphoinositide 3-kinase （PI3K）/Akt signaling pathway and inflammatory processes. The molecular docking results showed that both the active ingredients and key targets had good binding ability. Animal experiments showed that compared with the blank group， the ulcer index （UI） of the model group was significantly increased （P<0.01）， and the serum levels of TNF-α and IL-1β significantly increased （P<0.01）. The phosphorylation level of PTEN in gastric mucosal tissue was significantly down-regulated （P<0.05）. The phosphorylation levels of PI3K， Akt， and nuclear factor kappa-B （NF-κB） were significantly up-regulated （P<0.05）. Compared with the model group， the UI of the treatment group was significantly reduced （P<0.01）， and the serum levels of TNF-α and IL-1β were significantly reduced （P<0.01）. The phosphorylation level of PTEN in gastric mucosal tissue was significantly up-regulated （P<0.01）， and the phosphorylation levels of PI3K， Akt， and NF-κB were significantly downregulated （P<0.01）. ConclusionThe application of network pharmacology prediction， molecular docking simulation， and animal experimental validation confirms that SHXXT regulates the PI3K/Akt/NF-κB signaling pathway to regulate the inflammatory response of rats and thus protects the gastric mucosa of SGU rats.

The anti-inflammatory efficacy of Callicarpa nudiflora extract were evaluated upon lipopolysaccharide (LPS)-induced infective inflammation in rats. Pathological changes of lung and colon tissues observed with hematoxylin-eosin (H&E) staining, together with inflammatory factors in serum, including nitric oxide (NO), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), were applied to assess the mitigating effects of C. nudiflora water extract on model rats. The experimental protocol strictly adhered to the guidelines of the Ethics Committee of Animal Research of Guangdong Pharmaceutical University (Approval: gdpulac2022132). Quality markers with anti-inflammatory activities were recognized by network pharmacology analysis. Subsequently, a reliable method for simultaneously quantifying six components was established. As a result, the pathological injury on lung and colon tissues of model rats induced by LPS were improved by C. nudiflora water extract. Compared with model group, C. nudiflora extract had decreased the release of proinflammatory factors in serum, including TNF-α and IL-6, and reduced the NO (P < 0.01). Network pharmacological analysis obtained 83 chemical components, 466 component targets, 584 disease targets and 129 action targets, which were mainly concentrated in 624 biological processes such as inflammatory response and positive regulation of nitric oxide biosynthesis, as well as 162 pathways such as phosphatidylinositol-3-hydroxykinase-protein kinase (PI3K-Akt) signal pathway and Janus kinase-signal transducer and activator of transcription 3 (JAK-STAT3) signal pathway. Upon analyzing their specificity, effectiveness, detectability, and quantity transfer rule from herb to extract, 6-hydroxyluteolin 7-glucoside, cynaroside, caffeic acid, acteoside, isoacteoside, and forsythoaside B were identified as the quality markers. Their anti-inflammatory activities were verified with LPS-induced inflammation model of RAW264.7 cells. Then contents of these 6 quality markers in 16 batches of C. nudiflora were determined. Thereby, the anti-inflammatory efficacy of C. nudiflora extract were confirmed in vivo, 6-hydroxyluteolin 7-glucoside, cynaroside, caffeic acid, acteoside, isoacteoside, and forsythoaside B were identified as anti-inflammatory ingredients, which can be used as quality control indicators for the herb and related formulation.

Objective To investigate the risk factors for re-fracture after percutaneous kyphoplasty (PKP) in elderly patients with osteoporotic thoracolumbar compression fractures and to construct a line graph prediction model. Methods One hundred and eighty-two elderly patients with osteoporotic thoracolumbar compression fractures treated with PKP from January 2016 to November 2019 were selected for the study‚ and the patients were continuously followed up for 3 years after surgery. Clinical data were collected from both groups; Receiver operating characteristic (ROC) curve analysis was performed on the measures; Logistic regression analysis was performed to determine the independent risk factors affecting postoperative re-fracture in PKP; the R language software 4. 0 “rms” package was used to construct a predictive model for the line graph‚ and the calibration and decision curves were used to internally validate the predictive model for the line graph and for clinical evaluation of predictive performance. Results The differences between the two groups were statistically significant (P<0. 05) in terms of bone mineral density (BMD)‚ number of injured vertebrae‚ single-segment cement injection‚ type of cement distribution‚ cement leakage‚ difference in vertebral body height before and after PKP‚ and change in posterior convexity angle. The area under the curve (AUC) for BMD‚ number of injured vertebrae‚ single-segment cement injection volume‚ cement leakage‚ pre-and post-PKP vertebral height difference‚ and posterior convexity change were 0. 772‚ 0. 732‚ 0. 722‚ 0. 801‚ and 0. 813‚ respectively‚ and the best cutoff values were -3. 1‚ 2‚ 3. 9 ml‚ 0. 4 mm‚ and 8. 7°‚ respectively. BMD‚ number of injured vertebrae‚ single-segment cement injection volume‚ cement leakage‚ pre-and post-PKP vertebral height difference‚ and posterior convexity change were independent risk factors for re-fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fractures. The calibration curve of the column line graph prediction model was close to the original curve and the ideal curve with a C-index of 0. 818 (95% CI: 0. 762-0. 883)‚ and the model fit was good; the threshold value of the column line graph prediction model was >0. 22‚ which could provide a net clinical benefit‚ and the net clinical benefit was higher than the independent predictors. Conclusion BMD‚ number of injured vertebrae‚ single-segment cement injection‚ cement leakage‚ pre-and post-PKP vertebral height difference‚ and posterior convexity angle change are independent risk factors affecting the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture‚ and this study constructs a column line graph model to predict the recurrent fracture after PKP in elderly patients with osteoporotic thoracolumbar compression fracture as a predictor for clinical. This study provides an important reference for clinical prevention and treatment‚ and has clinical application value.

Objective To investigate the efficacy and safety of camrelizumab monoclonal antibody combined with molecular-targeted therapy in elderly patients with unresectable or advanced hepatocellular carcinoma(HCC).Methods A retrospective analysis was performed for the patients with unresectable/advanced HCC who attended six hospitals from January 1,2019 to March 31,2021,and all patients received camrelizumab monoclonal antibody treatment,among whom 84.8%also received targeted therapy.According to the age of the patients,they were divided into elderly group(≥65 years)and non-elderly group(＜65 years).The two groups were assessed in terms of overall survival(OS),progression-free survival(PFS),objective response rate(ORR),disease control rate(DCR),and immune-related adverse events(irAE).The chi-square test or the Fisher's exact test was used for comparison of categorical data between groups;the independent samples t-test was used for comparison of normally distributed continuous data,and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups.The Kaplan-Meier method was used for survival analysis,and the log-rank test was used for comparison of survival curves.Univariate and multivariate Cox proportional hazards regression analyses were used to determine the independent influencing factors for PFS and DCR at 6 months.Results A total of 99 HCC patients were enrolled,with 27 in the elderly group and 72 in the non-elderly group.The elderly group had an OS rate of 67.8%,an ORR of 44.4%,and a DCR of 74.1%at 12 months and a median PFS of 6.4(95%confidence interval[CI]:3.0-12.4)months,with no significant differences compared with the non-elderly group(all P＞0.05).The median OS was unavailable for the elderly group,while the non-elderly group had an OS of 18.9(95%CI:13.0-24.8)months;there was no significant difference between the two groups(P=0.485).The univariate and multivariate Cox regression analyses showed that major vascular invasion(MVI)was an independent risk factor for PFS(hazard ratio[HR]=2.603,95%CI:1.136-5.964,P=0.024)and DCR(HR=3.963,95%CI:1.671-9.397,P=0.002)at 6 months,while age,sex,etiology of HBV infection,presence of extrahepatic metastasis,Child-Pugh class B,and alpha-fetoprotein＞400 ng/mL were not associated with PFS or DCR at 6 months.For the elderly group,the incidence rates of any irAE and grade 3/4 irAE were 51.9%and 25.9%,respectively,with no significant differences compared with the non-elderly group(P＞0.05),and skin disease was the most common irAE in both groups(39.4%).Conclusion Camrelizumab monoclonal antibody combined with molecular-targeted therapy has similar efficacy and safety in patients with unresectable/advanced HCC aged≥65 years and those aged＜65 years.MVI is associated with suboptimal response to immunotherapy and poor prognosis.

Cuproptosis is a new type of cell death that depends on intracellular copper accumulation to trigger the aggregation of mitochondrial lipoacylated protein and the degradation of iron-sulfur cluster protein,with a different mechanism of action from autophagy,ferroptosis,pyroptosis,and necroptosis.Cuproptosis is closely association with the development of liver cancer and resistance to antitumor drugs,as well as the progression of various liver diseases such as hereditary liver diseases,nonalcoholic fatty liver disease,viral hepatitis,and liver cirrhosis.This article summarizes the mechanism of cuproptosis and its role in liver diseases,in order to provide a reference for further research and treatment of liver diseases.

Long non-coding RNAs(lncRNAs)play an indispensable role in the occurrence and development of ovarian cancer(OC).However,the potential involvement of lncRNAs in the progression of OC is largely unknown.To investigate the detailed roles and mechanisms of RAD51 homolog B-antisense 1(RAD51B-AS1),a novel lncRNA in OC,reverse transcription-quantitative polymerase chain reaction(RT-qPCR)was performed to verify the expression of RAD51B-AS1.Cellular proliferation,metastasis,and apoptosis were detected using the cell counting kit-8(CCK-8),colony-formation,transwell,and flow cytometry assays.Mouse xenograft models were established for the detection of tumorigenesis.The results revealed that RAD51B-AS1 was significantly upregulated in a highly metastatic human OC cell line and OC tissues.RAD51B-AS1 significantly increased the proliferation and metastasis of OC cells and enhanced their resistance to anoikis.Biogenetics prediction analysis revealed that the only target gene of RAD51B-AS1 was RAD51B.Subsequent gene function experiments revealed that RAD51B exerts the same biological effects as RAD51B-AS1.Rescue experiments demonstrated that the malignant biological behaviors promoted by RAD51B-AS1 overexpression were partially or completely reversed by RAD51B silencing in vitro and in vivo.Thus,RAD51B-AS1 promotes the malignant biological behaviors of OC and activates the protein kinase B(Akt)/B cell lymphoma protein-2(Bcl-2)signaling pathway,and these effects may be associated with the positive regulation of RAD51B expression.RAD51B-AS1 is expected to serve as a novel molecular biomarker for the diagnosis and prediction of poor prognosis in OC,and as a potential therapeutic target for disease management.