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.

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.

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.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Objective:To analyze the acupoint selection law in treating insomnia with warming-needle moxibustion using data mining technology.Methods:The clinical research literature on warming-needle moxibustion in treating insomnia was retrieved from the databases of CNKI, VIP, Wanfang Data, China Medical Journal Full-text Database, SinoMed, PubMed from the establishment of the databases to June 30, 2023. Acupuncture prescriptions were extracted and a database was established. Descriptive analysis was carried out on the intervention measures, the number of moxibustion numbers, the total effective rate, and the relevant information of acupoint selection. Clustering analysis and association rule analysis by using SPSS Statistics 26.0, SPSS Modeler 18.0 were performed on the high-frequency acupoints used in body acupuncture and warming acupuncture, respectively.Results:A total of 67 articles about warming-needle moxibustion for insomnia were included. The main intervention was single warming-needle moxibustion, followed by combined auricular acupuncture. Moxibustion with moxa sticks of 1 to 2 cm in length was often used. The total effective rate was mostly between 92% and 94%. A total of 85 prescriptions were obtained, involving 55 acupoints (569 times in frequency), among which Baihui (DU 20) (55 times), Shenmen (HT 7) (54 times) and Sanyinjiao (SP 6) (45 times) were widely used. The selected acupoints were mainly distributed on the head, face, back and waist, and the affiliated meridians were mainly bladder meridian, Governor Vessel and heart meridian. The specific acupoints were mainly crossing points, followed by back-shu acupoints. Clustering analysis of high-frequency acupoints obtained three effective groups, including "Xinshu (BL 15)- Pishu (BL 20) - Shenshu (BL 23) - Ganshu (BL 18)", etc. Baihui (DU 20) and Shenmen (HT 7) were the most relevant acupoint combinations. A total of 44 acupoints (frequency 272 times) were involved in the selection of warming acupuncture, among which Sanyinjiao (SP 6) (32 times), Xinshu (BL 15) (23 times) and Pishu (BL 20) (23 times) were more frequently used. Clustering analysis of high-frequency acupoints obtained three effective groups, including "Xinshu (BL 15)- Pishu (BL 20) - Shenshu (BL 23) - Ganshu (BL 18)", etc. Pishu (BL 20) and Xinshu (BL 15) were the most relevant acupoint combinations.Conclusions:Warming-needle moxibustion for the treatment of insomnia focuses on the application of acupoints on the bladder meridian on the back and waist, cooperates with the selection of acupoints on the head and face, and attaches importance to the application of intersection points in specific acupoints. The selection of acupoints by warming-needle moxibustion is mainly based on the back-shu acupoints of zang-fu organs, which plays the role of balancing yin and yang, regulating zang-fu organs, tonifying deficiency and tranquilizing mind to help sleep.

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 The leptin receptor,encoded by the LEPR gene,is involved in tumorigenesis.A potential functional variant of LEPR,rs1137101(Gln223Arg),has been extensively investigated for its contribution to the risk of digestive system(DS)cancers,but results remain conflicting rather than conclusive.Here,we performed a case-control study and subsequent meta-analysis to examine the association between rs1137101 and DS cancer risk. Methods A total of 1,727 patients with cancer(gastric/liver/colorectal:460/480/787)and 800 healthy controls were recruited.Genotyping of rs1137101 was conducted using a polymerase chain reaction-restriction fragment length polymorphism(PCR-RFLP)assay and confirmed using Sanger sequencing.Twenty-four eligible studies were included in the meta-analysis. Results After Bonferroni correction,the case-control study revealed that rs1137101 was significantly associated with the risk of liver cancer in the Hubei Chinese population.The meta-analysis suggested that rs1137101 is significantly associated with the risk of overall DS,gastric,and liver cancer in the Chinese population. Conclusion The LEPR rs1137101 variant may be a genetic biomarker for susceptibility to DS cancers(especially liver and gastric cancer)in the Chinese population.

Objective:To understand the prevalence of major chronic diseases of diabetes, cardiovascular disease and malignant tumor in people living with HIV in Taizhou.Methods:The data were collected from China Information System for Disease Control and Prevention and Taizhou Chronic Disease Information Management System. A total of 5 126 people living HIV under follow-up in Taizhou from 1998 to 2022 were included in the analysis. Software SAS 9.4 was used for χ 2 test, trend analysis and logistic regression analysis. Results:In the 5 126 people living with HIV, the reported prevalence rates of diabetes,cardiovascular disease and malignant tumor were 10.28% (527/5 126),3.98% (204/5 126) and 6.01% (308/5 126), respectively. 37.00% (195/527) and 48.58% (256/527), 40.20% (82/204) and 48.53% (99/204), 37.66% (116/308) and 48.38% (149/308) were diagnosed as diabetes, cardiovascular disease and malignant tumor before and after confirmation of HIV infection. From 2013 to 2022, the proportion of HIV infected people diagnosed with diabetes, cardiovascular disease and malignant tumor after confirmation increased (trend χ2=79.98, P<0.001; trend χ2=17.44, P<0.001; trend χ2=32.06, P<0.001). Based on the analysis on the factors for complicated chronic diseases in people living with HIV, it was found that women under 60 years old (a OR=0.66, 95% CI: 0.50-0.86) and those with access to antiviral treatment for >5 years before 2016 (a OR=0.54,95% CI:0.37-0.78) were less likely to develop complicated chronic diseases, and those under 60 years old with initial CD4 +T lymphocytes counts <200 cells/μl (a OR=1.32, 95% CI: 1.02-1.70), those aged 40-49 and 50-59 years (a OR=2.88, 95% CI:2.20-3.79; a OR=5.43, 95% CI: 4.10-7.21) as well as those without a record of treatment medication use after 2016 (a OR=1.95,95% CI:1.20-3.16) were more likely to develop complicated chronic diseases. The probability of developing complicated chronic diseases might increase with age in people living with HIV. Conclusions:From 1998 to 2022, there was a certain proportion of complicated chronic diseases among HIV infected individuals in Taizhou, and the proportion of diagnosed cases increased after HIV infection was confirmed. It is necessary to conduct early chronic disease screening, behavior intervention and standardized management in people living with HIV.