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 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.

Objective:To explore the value of Astograph challenge test and small airway functional parameters to diagnose asthma.Methods:86 asthmatics were enrolled along with 40 non-asthmatic patients as a control group from January 2018 to March 2019 in People′s Hospital of Nanhai Economic Development Zone, Foshan Chancheng District Central Hospital and Affiliated Hospital of Guangdong Medical University. All subjects were required to complete data acquisition, impulse oscillometry (IOS), pulmonary function test, and Astograph challenge test. The routine pulmonary function indexes, pulse oscillation indexes and reaction threshold (Dmin) were compared between asthma group and non asthma group, and the correlation between each index was analyzed.Results:The Dmin, max expiratory at 50% forced vital capacity (MEF 50%), max expiratory at 75% forced vital capacity (MEF 25%), maximal mid expiratory flow (MMEF) of asthma group were significantly lower than those of non-asthmatic group ( P<0.05). The IOS parameters such as respiratory impedance (Zrs), resistance at 5 Hz (R 5), resistance at 20 Hz (R 20), peripheral resistance (R 5-R 20), reactance at 5 Hz (X 5), the resonance frequency (Fres) showed no statistical significance between the asthmatics and the non-asthmatics ( P>0.05). Response threshold (Dmin) was positively correlated to MEF 50, MEF 25 and MMEF respectively (0.295, 0.306, 0.381, all P<0.05). A receiver operating characteristic (ROC) analysis indicated the area under ROC curve (AUC) of Dmin in diagnosis of asthma was 0.978 (95% CI: 0.950-1.000), the best cutoff point of Dmin was 13.609 Units, with sensitivity in diagnosing asthma 94.6%, specifity 96.8%. The AUC for MEF 50, MEF 25, MMEF were 0.674, 0.738, 0.683, with the best cutoffs 3.990 L, 1.685 L, 3.445 L, respectively. Conclusions:Astograph challenge test, which is fast, safe and precise, can assist in the diagnosis of asthma. MEF 50 and MEF 25 may be associated with the diagnosis of asthma and can predict airway hyper-responsiveness while IOS parameters could not, which make MMEF, MEF 50 and MEF 25 superior to IOS parameters in the diagnosis of asthma.

Objective : To compare the removal efficiency and the amounts of apically extruded debris using Twisted File (TF), Twisted File Adaptive (TFA), ProTaper, and ProTaper Next combined with ultrasonic irrigation and to provide an experimental basis for the selection of root canal instrumentation in the clinic. Methods: Forty mandibular premolars were randomly divided into 4 groups (n=10 teeth per group). The canals were cut using a Twisted File, Twisted File Adaptive, ProTaper, or ProTaper Next nickel-titanium instrument. The canals were irrigated with ultrasonic irrigation. The apically extruded debris were collected in preweighted Eppendorf tubes. The amount of dental tissue removed and extruded debris were assessed with an electronic balance. Results : The amount of tooth tissue removed in groups A, B, C and D was 20.5 ± 2.0 mg, 17.8 ± 4.2 mg, 20.8 ± 3.9 mg and 16.5 ± 2.2 mg, respectively. Combined with ultrasonic irrigation, the Twisted File and ProTaper had a better removal efficiency than the ProTaper Next(P < 0.05). There was no significant difference in the amount of extruded debris (χ2=4.057, P=0.255) among four groups. Conclusion The Twisted File and ProTaper had a better removal efficiency than the ProTaper Next combined with ultrasonic irrigation. There was no significant difference in the amount of extruded debris using four Nickel-titanium instruments combined with ultrasonic irrigation.

Stroke at the acute stage is a major cause of disability in adults, and is associated with dysfunction of brain networks. However, the mechanisms underlying changes in brain connectivity in stroke are far from fully elucidated. In the present study, we investigated brain metabolism and metabolic connectivity in a rat ischemic stroke model of middle cerebral artery occlusion (MCAO) at the acute stage using F-fluorodeoxyglucose positron emission tomography. Voxel-wise analysis showed decreased metabolism mainly in the ipsilesional hemisphere, and increased metabolism mainly in the contralesional cerebellum. We used further metabolic connectivity analysis to explore the brain metabolic network in MCAO. Compared to sham controls, rats with MCAO showed most significantly reduced nodal and local efficiency in the ipsilesional striatum. In addition, the MCAO group showed decreased metabolic central connection of the ipsilesional striatum with the ipsilesional cerebellum, ipsilesional hippocampus, and bilateral hypothalamus. Taken together, the present study demonstrated abnormal metabolic connectivity in rats at the acute stage of ischemic stroke, which might provide insight into clinical research.
Acute Disease ; Animals ; Brain ; diagnostic imaging ; metabolism ; Brain Mapping ; Disease Models, Animal ; Fluorodeoxyglucose F18 ; Glucose ; metabolism ; Infarction, Middle Cerebral Artery ; diagnostic imaging ; metabolism ; Male ; Neural Pathways ; diagnostic imaging ; metabolism ; Positron-Emission Tomography ; Radiopharmaceuticals ; Random Allocation ; Rats, Sprague-Dawley