Objective To analyze the factors affecting the prevalence of hyperuricemia(HUA)in an island troop.Methods A total of 1 113 soldiers stationed on an island from December 2021 to December 2022 were selected as research objects by cluster sampling.Their lifestyle and health information were collected.Physical examination and laboratory detection were conducted.Multivariate logistic regression was used to analyze the influencing factors of HUA.Results The prevalence rate of HUA was 21.02%(234/1 113).There were significant differences in the body mass index(BMI),waist-to-hip ratio,triglyceride,alanine aminotransferase,and creatinine between the soldiers with hyperuricemia and the soldiers with normal blood uric acid(P<0.05).Multivariate logistic regression analysis showed that BMI≥24(OR=1.49,95%CI:1.09-2.05),abnormal liver function(OR=2.26,95%CI:1.31-3.92),and dyslipidemia(OR=1.46,95%CI:1.01-2.12)were positively correlated with hyperuricemia;age>30 years old(OR=0.59,95%CI:0.37-0.93)and exercise time>1 h per week(OR=0.46,95%CI:0.22-0.97)were negatively correlated with HUA.Conclusion The prevalence rate of hyperuricemia is at a high level in an island troop.BMI≥24,age≤30 years old,exercise time≤1 h per week,abnormal liver function,and dyslipidemia are the risk factors for HUA.Prevention and control measures should be taken as early as possible for the soldiers with these risk factors.

The underlying cause of low response rates to existing immunotherapies is that tumor cells dominate tumor immune escape through surface antigen deficiency and inducing tumor immunosuppressive microenvironment (TIME). Here, we proposed an in situ tumor cell engineering strategy to disrupt tumor immune escape at the root by restoring tumor cell MHC-I/tumor-specific antigen complex (MHC-I/TSA) expression to promote T-cell recognition and by silencing tumor cell CD55 to increase the ICOSL⁺ B-cell proportion and reverse the TIME. A doxorubicin (DOX) and dual-gene plasmid (MAC pDNA, encoding both MHC-I/ASMTNMELM and CD55-shRNA) coloaded drug delivery system (LCPN@ACD) with tumor targeting and charge/size dual-conversion properties was prepared. LCPN@ACD-induced ICD promoted DC maturation and enhanced T-cell activation and infiltration. LCPN@ACD enabled effective expression of MHC-I/TSA on tumor cells, increasing the ability of tumor cell recognition and killing. LCPN@ACD downregulated tumor cell CD55 expression, increased the proportion of ICOSL⁺ B cells and CTLs, and reversed the TIME, thus greatly improving the efficacy of αPD-1 and CAR-T therapies. The application of this in situ tumor cell engineering strategy eliminated the source of tumor immune escape, providing new ideas for solving the challenges of clinical immunotherapy.

G protein-coupled receptor kinase 2 (GRK2) participates in the phosphorylation and desensitization of G protein-coupled receptor (GPCR), impacting various biological processes such as inflammation and cell proliferation. Dysregulated expression and activity of GRK2 have been reported in multiple cells in rheumatoid arthritis (RA). However, whether and how GRK2 regulates synovial hyperplasia and fibroblast-like synoviocytes (FLSs) proliferation is poorly understood. In this study, we investigated the regulation of GRK2 and its biological function in RA. We found that GRK2 transmembrane activity was increased in FLSs of RA patients and collagen-induced arthritis (CIA) rats. Additionally, we noted a positive correlation between high GRK2 expression on the cell membrane and serological markers associated with RA and CIA. Immunoprecipitation-mass spectrometry and pull-down analyses revealed tumor necrosis factor receptor-associated factor 2 (TRAF2) as a novel substrate of GRK2. Furthermore, surface plasmon resonance (SPR) and molecular docking assays determined that the C-terminus of GRK2 binds to the C-terminus of TRAF2 at the Gln340 residue. GRK2 knockdown and the GRK2 inhibitor CP-25 attenuated synovial hyperplasia and FLS proliferation in CIA both in vitro and in vivo by decreasing GRK2 membrane expression and activity. Mechanistically, increased GRK2 transmembrane activity contributed to the recruitment of TRAF2 on the cell membrane, promoting GRK2-TRAF2 interactions that facilitate the recruitment of the E3 ubiquitin ligase TRIM47 to TRAF2. This enhanced TRAF2 Lys63 polyubiquitylation and induced nuclear factor (NF)-κB activation, leading to synovial hyperplasia and abnormal proliferation of FLSs. Our study provides a mechanistic and preclinical rationale for further evaluation of GRK2 as a therapeutic target for RA.

OBJECTIVES: We propose a novel deep learning segmentation algorithm (DSRF) based on multi-scale supervision and residual feedback strategy for precise segmentation of the optic chiasm and optic nerves in CT images of nasopharyngeal carcinoma (NPC) patients. METHODS: We collected 212 NPC CT images and their ground truth labels from SegRap2023, StructSeg2019 and HaN-Seg2023 datasets. Based on a hybrid pooling strategy, we designed a decoder (HPS) to reduce small organ feature loss during pooling in convolutional neural networks. This decoder uses adaptive and average pooling to refine high-level semantic features, which are integrated with primary semantic features to enable network learning of finer feature details. We employed multi-scale deep supervision layers to learn rich multi-scale and multi-level semantic features under deep supervision, thereby enhancing boundary identification of the optic chiasm and optic nerves. A residual feedback module that enables multiple iterations of the network was designed for contrast enhancement of the optic chiasm and optic nerves in CT images by utilizing information from fuzzy boundaries and easily confused regions to iteratively refine segmentation results under supervision. The entire segmentation framework was optimized with the loss from each iteration to enhance segmentation accuracy and boundary clarity. Ablation experiments and comparative experiments were conducted to evaluate the effectiveness of each component and the performance of the proposed model. RESULTS: The DSRF algorithm could effectively enhance feature representation of small organs to achieve accurate segmentation of the optic chiasm and optic nerves with an average DSC of 0.837 and an ASSD of 0.351. Ablation experiments further verified the contributions of each component in the DSRF method. CONCLUSIONS The proposed deep learning segmentation algorithm can effectively enhance feature representation to achieve accurate segmentation of the optic chiasm and optic nerves in CT images of NPC.
Humans ; Tomography, X-Ray Computed/methods* ; Optic Chiasm/diagnostic imaging* ; Optic Nerve/diagnostic imaging* ; Algorithms ; Nasopharyngeal Carcinoma ; Deep Learning ; Nasopharyngeal Neoplasms/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods*

Objective:To explore effect and potential mechanism of disulfiram on necrotizing apoptosis of renal podocytes and macrophages.Methods:Mouse renal podocyte MPC-5 and macrophages J774A.1 and BMDM cells were cultured in vitro and treated with TNF-α,Smac mimetic LCL-161 and pan-caspase inhibitor IDN-6556(TSI)to induce necroptosis.Cell necrosis was detected by propi-dium iodide staining.Western blot was used to detect protein levels of necroptosis markers MLKL,RIPK3 and RIPK1.Immuno-fluorescence microscopy was used to observe the subcellular distribution of RIPK3 and p-MLKL in MPC-5 cells.Results:TSI treat-ment induced significant necroptosis in both MPC-5 cells and macrophages.Disulfiram was able to inhibit necroptosis in these cells in a dose-dependent manner.Moreover,disulfiram markedly blocked the phosphorylation of RIPK1,RIPK3 and MLKL.The aggregation of RIPK3 and p-MLKL were also suppressed by disulfiram.Conclusion:Disulfiram inhibits necroptosis in podocytes and macrophages by suppressing RIPK1/RIPK3/MLKL signaling pathway.

Objective:To explore effect and potential mechanism of disulfiram on necrotizing apoptosis of renal podocytes and macrophages.Methods:Mouse renal podocyte MPC-5 and macrophages J774A.1 and BMDM cells were cultured in vitro and treated with TNF-α,Smac mimetic LCL-161 and pan-caspase inhibitor IDN-6556(TSI)to induce necroptosis.Cell necrosis was detected by propi-dium iodide staining.Western blot was used to detect protein levels of necroptosis markers MLKL,RIPK3 and RIPK1.Immuno-fluorescence microscopy was used to observe the subcellular distribution of RIPK3 and p-MLKL in MPC-5 cells.Results:TSI treat-ment induced significant necroptosis in both MPC-5 cells and macrophages.Disulfiram was able to inhibit necroptosis in these cells in a dose-dependent manner.Moreover,disulfiram markedly blocked the phosphorylation of RIPK1,RIPK3 and MLKL.The aggregation of RIPK3 and p-MLKL were also suppressed by disulfiram.Conclusion:Disulfiram inhibits necroptosis in podocytes and macrophages by suppressing RIPK1/RIPK3/MLKL signaling pathway.

Objective To establish a predictive model for survival outcomes of glioma patients based on both brain radiomics features from preoperative MRI multi-sequence images and clinical features.Methods We retrospectively analyzed the MRI images and clinical data of 388 glioma patients and extracted the radiomics features from the peritumoral edema zone,tumor core,and whole tumor on T1,T2,and T1-weighted contrast-enhanced(T1CE)and fluid attenuated inversion recovery(FLAIR)sequences.The cases were divided into a training set(271 cases)and a test set(117 cases).Random survival forest algorithms were used to select the radiomics features associated with overall survival(OS)in the training set to construct a radiomic score(Rad-score),based on which the patients were classified into high-and low-risk groups for Kaplan-Meier survival analysis.Cox proportional hazard regression models for the 3 different tumor zones were constructed,and their performance for predicting 1-and 3-year survival rates was evaluated using 5-fold cross-validation and AUC analysis followed by external validation using data from another 10 glioma patients.The best-performing model was used for constructing a nomogram for survival predictions.Results Five radiomics features from the tumor core,7 from the peritumoral edema zone,and 5 from the whole tumor were selected.In both the training and test sets,the high-and low-risk groups had significantly different OS(P<0.05),and age,IDH status and Rad-score were independent factors affecting OS.The combined model showed better performance than the Rad-score model with AUCs for 1-year and 3-year survival prediction of 0.750 and 0.778 in the training set,0.764 and 0.800 in the test set,and 0.938 and 0.917 in external validation,respectively.Conclusion The predictive model combining preoperative multi-modal MRI radiomics features and clinical features can effectively predict survival outcomes of glioma patients.

Objective To establish a predictive model for survival outcomes of glioma patients based on both brain radiomics features from preoperative MRI multi-sequence images and clinical features.Methods We retrospectively analyzed the MRI images and clinical data of 388 glioma patients and extracted the radiomics features from the peritumoral edema zone,tumor core,and whole tumor on T1,T2,and T1-weighted contrast-enhanced(T1CE)and fluid attenuated inversion recovery(FLAIR)sequences.The cases were divided into a training set(271 cases)and a test set(117 cases).Random survival forest algorithms were used to select the radiomics features associated with overall survival(OS)in the training set to construct a radiomic score(Rad-score),based on which the patients were classified into high-and low-risk groups for Kaplan-Meier survival analysis.Cox proportional hazard regression models for the 3 different tumor zones were constructed,and their performance for predicting 1-and 3-year survival rates was evaluated using 5-fold cross-validation and AUC analysis followed by external validation using data from another 10 glioma patients.The best-performing model was used for constructing a nomogram for survival predictions.Results Five radiomics features from the tumor core,7 from the peritumoral edema zone,and 5 from the whole tumor were selected.In both the training and test sets,the high-and low-risk groups had significantly different OS(P<0.05),and age,IDH status and Rad-score were independent factors affecting OS.The combined model showed better performance than the Rad-score model with AUCs for 1-year and 3-year survival prediction of 0.750 and 0.778 in the training set,0.764 and 0.800 in the test set,and 0.938 and 0.917 in external validation,respectively.Conclusion The predictive model combining preoperative multi-modal MRI radiomics features and clinical features can effectively predict survival outcomes of glioma patients.

Objective:To evaluate the application effect of medical visual resources assisted nursing rounds in pediatric clinical nursing teaching.Methods:A total of 63 nursing students who entered the First Affiliated Hospital of Xinxiang Medical University for internship in 2019 and 2020 were selected as research objects from May 2021 to May 2022 by the convenient sampling method. 33 nursing students who participated in the internship in 2019 were set as the control group, while 30 nursing students who participated in the internship in 2020 were set as the intervention group. The control group adopted traditional nursing rounds to organize teaching while the intervention group used medical visual resources to organize theoretical learning and nursing rounds. Before leaving the department, the case analysis ability, critical thinking ability and teaching effectiveness of nursing students were compared between two groups.Results:The score of case analysis ability of nursing students in the intervention group was (83.06±7.58), which was higher than (74.85±9.29) of the control group, and the difference was statistically significant ( t=3.750, P<0.001). At the time of enrollment, there was no statistically significant difference in the total score and scores of each dimension of critical thinking ability of nursing students between the two groups ( P>0.05). Before leaving the department, scores of analysis ability, systematic thinking ability and the total scores of nursing students in the intervention group were higher than those in the control group, and the differences were statistically significant ( P<0.05). Except for the dimension of teaching burden, the score of each dimension and the total score of teaching effectiveness for nursing students in the intervention group were higher than those in the control group, and the differences were statistically significant ( P<0.05) . Conclusions:Medical visual resources assisted nursing rounds can help nursing students deeply understand and master clinical knowledge, cultivate their critical thinking ability and improve the effectiveness of clinical teaching.

Abstract In order to achieve the goal of reducing the smoking rate among Chinese adults at ages of 15 years and above to below 20% by 2030, smoking cessation services require to be reinforced. The current Chinese smoking cessation service system includes short-term smoking cessation interventions and smoking cessation hotlines at the population level, and smoking cessation clinics at the individual level; however, these smoking cessation services suffer from problems of low awareness, low accessibility and low utilization rate. Based on the publications pertaining to smoking cessation services and released policy documents in China until June 2022, this review analyzes the current status, problems and causes of smoking cessation services, and proposes suggestions for improving the smoking cessation service system construction in China.