Gastric ulcer is a common digestive system disease,and the long-term use of non-steroidal anti-inflammatory drugs(NSAIDs)is the second most important cause.NSAID-induced gastric ulcer animal models are key experimental tools for studying the pathogenesis,corresponding treatment method,and effective mechanisms of NSAID-induced gastrointestinal injury.However,there are currently a lack of reviews on NSAID-induced gastric ulcer animal models.This review summarizes and compares the relevant literature on animal research into indomethacin-and aspirin-induced gastric ulcers in the past 10 years,including the selection of experimental animals,drug solvents,and specific modeling method.The limitations of current models,such as the cumbersome modeling method,incomplete modeling details,inadequate models for clinical use,and lack of comparative drug research,are discussed.Feasible solutions are proposed with the aim of providing an effective reference for research in this field.

Objective To investigate the research hotspots and trend in pain management with virtual reality from 2013 to 2023 therefore to provide nursing administrators and researchers with insights into leveraging information technology and to improve nursing quality.Methods Literatures in virtual reality and pain management were retrieved from the Web of Science core collection and China National Knowledge Infrastructure databases.Bibliometric analysis using CiteSpace software were conducted to examine the trend of annual publications,countries and institutions of the authors,leading authors,cited journals and cluster keywords.The researched time was from January 2013 to September 2023.Results A total of 2 503 English and 328 Chinese articles were included.It was found that the publications in relevant topics were on the rise annually in number and peaked twice in 2014 and 2020.Some authors from different institutions conducted certain level of collaborations in the researches.The journal Pain received the highest citations.Eight cluster keywords were identified:rehabilitation,virtual screening,distraction,quality of life,surgery,phantom limb pain,social pain and virtual reality.Four research hotspots were summarised,including current application of VR technology in pain management,application effect and research in rehabilitation,research in innovation of VR technology and personalised mental health interventions.Conclusions Research about VR technology in pain management is growing with technological advancements and policy supports.However,collaborations should be further improved between the scholars from different institutions.The domestic research emerged relatively behind the foreign institutions.Future studies should focus on intervention trials of virtual reality,particularly its impact on different participants groups and its role in rehabilitation.Innovative technical methods and personalised and accurate pain management strategies are crucial for promotion of the advanced VR technology in pain management.

In recent years,with the increase of social pressure,the incidence of"psycho-cardiological disease"increases.Western medicine mainly uses conventional cardiovascular drugs combined with antidepressant or anxiety drugs.TCM has the characteristics of multi-pathway,multi-target,multi-mechanism,integration and low toxicity.It has unique advantages in the intervention of"psycho-cardiological disease"and has significant clinical efficacy.However,the pathogenesis,clinical diagnosis,syndrome differentiation and treatment of this disease are not the same.This article summarized relevant literature and reviewed the etiology,pathogenesis,experience in syndrome differentiation and treatment,and clinical research of this disease from the perspective of TCM,in order to provide reference for diagnosis of disease,precision treatment and improvement of curative efficacy of"psycho-cardiological disease".

Objective:To investigate the value of CT radiomic model based on analysis of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer in differentiating stage T1-2 from stage T3-4 gastric cancer.Methods:This study was a case-control study. Totally 465 patients with gastric cancer treated in Affiliated People′s Hospital of Jiangsu University from December 2011 to December 2019 were retrospectively collected. According to postoperative pathology, they were divided into 2 groups, one with 150 cases of T1-2 tumors and another with 315 cases of T3-4 tumors. The cases were divided into a training set (326 cases) and a test set (139 cases) by stratified sampling method at 7∶3. There were 104 cases of T1-2 stage and 222 cases of T3-4 stage in the training set, 46 cases of T1-2 stage and 93 cases of T3-4 stage in the test set. The axial CT images in the venous phase during one week before surgery were selected to delineate the region of interest (ROI) at the primary lesion and the extramural gastric adipose tissue adjacent to the cancer areas. The radiomic features of the ROIs were extracted by Pyradiomics software. The least absolute shrinkage and selection operator was used to screen features related to T stage to establish the radiomic models of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer. Independent sample t test or χ2 test were used to compare the differences in clinical features between T1-2 and T3-4 patients in the training set, and the features with statistical significance were combined to establish a clinical model. Two radiomic signatures and clinical features were combined to construct a clinical-radiomics model and generate a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of each model in differentiating stage T1-2 from stage T3-4 gastric cancer. The calibration curve was used to evaluate the consistency between the T stage predicted by the nomogram and the actual T stage of gastric cancer. And the decision curve analysis was used to evaluate the clinical net benefit of treatment guided by the nomogram and by the clinical model. Results:There were significant differences in CT-T stage and CT-N stage between T1-2 and T3-4 patients in the training set ( χ2=10.59, 15.92, P=0.014, 0.001) and the clinical model was established. After screening and dimensionality reduction, the 5 features from primary gastric cancer and the 6 features from the adipose tissue outside the gastric wall beside cancer established the radiomic models respectively. In the training set and the test set, the AUC values of the primary gastric cancer radiomic model were 0.864 (95% CI 0.820-0.908) and 0.836 (95% CI 0.762-0.910), and the adipose tissue outside the gastric wall beside cancer radiomic model were 0.782 (95% CI 0.731-0.833) and 0.784 (95% CI 0.702-0.866). The AUC values of the clinical model were 0.761 (95% CI 0.705-0.817) and 0.758 (95% CI 0.671-0.845), and the nomogram were 0.876 (95% CI 0.835-0.917) and 0.851 (95% CI 0.781-0.921). The calibration curve reflected that there was a high consistency between the T stage predicted by the nomogram and the actual T stage in the training set ( χ2=1.70, P=0.989). And the decision curve showed that at the risk threshold 0.01-0.74, a higher clinical net benefit could be obtained by using a nomogram to guide treatment. Conclusions:The CT radiomics features of primary gastric cancer lesions and the adipose tissue outside the gastric wall beside cancer can effectively distinguish T1-2 from T3-4 gastric cancer, and the combination of CT radiomic features and clinical features can further improve the prediction accuracy.

Objective:To investigate the effectiveness and feasibility of a 3D U-Net in conjunction with a three-phase CT image segmentation model in the automatic segmentation of GTVnx and GTVnd in nasopharyngeal carcinoma.Methods:A total of 645 sets of computed tomography (CT) images were retrospectively collected from 215 nasopharyngeal carcinoma cases, including three phases: plain scan (CT), contrast-enhanced CT (CTC), and delayed CT (CTD). The dataset was grouped into a training set consisting of 172 cases and a test set comprising 43 cases using the random number table method. Meanwhile, six experimental groups, A1, A2, A3, A4, B1, and B2, were established. Among them, the former four groups used only CT, only CTC, only CTD, and all three phases, respectively. The B1 and B2 groups used phase fine-tuning CTC models. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) served as quantitative evaluation indicators.Results:Compared to only monophasic CT (group A1/A2/A3), triphasic CT (group A4) yielded better result in the automatic segmentation of GTVnd (DSC: 0.67 vs. 0.61, 0.64, 0.64; t = 7.48, 3.27, 4.84, P < 0.01; HD95: 36.45 vs. 79.23, 59.55, 65.17; t = 5.24, 2.99, 3.89, P < 0.01), with statistically significant differences ( P < 0.01). However, triphasic CT (group A4) showed no significant enhancement in the automatic segmentation of GTVnx compared to monophasic CT (group A1/A2/A3) (DSC: 0.73 vs. 0.74, 0.74, 0.73; HD95: 14.17 mm vs. 8.06, 8.11, 8.10 mm), with no statistically significant difference ( P > 0.05). For the automatic segmentation of GTVnd, group B1/B2 showed higher automatic segmentation accuracy compared to group A1 (DSC: 0.63, 0.63 vs. 0.61, t = 4.10, 3.03, P<0.01; HD95: 58.11, 50.31 mm vs. 79.23 mm, t = 2.75, 3.10, P < 0.01). Conclusions:Triphasic CT scanning can improve the automatic segmentation of the GTVnd in nasopharyngeal carcinoma. Additionally, phase fine-tuning models can enhance the automatic segmentation accuracy of the GTVnd on plain CT images.

To summarize the nursing care of a very low birth weight premature infant with severe type Ⅱbronchopulmonary dysplasia(BPD)during the transition period from hospitalization to home.The care of the infant was provided one-on-one by a BPD specialist nurse throughout the period.The key points of transitional care from hospitalization to home include:implementing tracheotomy and mechanical ventilation care to ensure stable blood oxygen saturation of the infant;providing nutritional support to improve the nutritional status of the infant;implementing step-by-step rehabilitation measures to improve the neuromotor development of the infant;implementing family integrated care to promote the primary caregivers of the infant to master nursing knowledge and skills;conducting personalized discharge follow-up with a multidisciplinary team to improve the quality of home care for this infant.After being hospitalized for 106 days,the infant was successfully discharged with a tracheotomy tube.At the age of 2 years and 6 months,a tracheotomy closure surgery was performed.After the surgery,the infant was able to breathe autonomously without symptoms of breathing difficulties and returned to normal family life.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

ObjectiveTo summarize the research progress of mammalian target of rapamycin （mTOR） pathway regulated by traditional Chinese medicine（TCM） and provide reference for visualization and quantitative analysis of related research based on multiple software linkage. MethodLiterature related to TCM regulation of mTOR pathway in Web of Science was taken as the research object. Citespace，VOSviewer，and carrort² were used for biliometric analysis and visualization of the literature. ResultA total of 245 papers that met the requirements were retrieved，and the visual analysis showed that the papers presented a fluctuating increase year by year after 2010，and numerous research results emerged in 2018. China had the most publications. Institutions with a large number of publications were mainly in Beijing and Shanghai，and most of the regional cooperation was centered in Beijing and Nanjing. According to the research direction and focus，it was found that the intervention of TCM in mTOR pathway in recent years mainly concentrated on the anti-tumor，anti-apoptotic and anti-inflammatory aspects， and TCM interfered with mTOR pathway to regulate cell apoptosis，autophagy，proliferation，and death. AMP-activated protein kinase （AMPK） and phosphatidylinositol 3-kinases（PI3K）/protein kinase B（Akt）/mTOR pathway were the current and future research hotspots. ConclusionResearch on the regulation of mTOR pathway by TCM had a good prospect，and the in-depth study might provide new ideas and guidance for the treatment of cardiovascular diseases，tumor and other major diseases.

Objective:To analyze the intervention effect of rehabilitation platform-based online psycho-education on patients with bipolar disorder (BD) in remission stage.Methods:In this randomized controlled study, 91 patients with BD in remission stage who attended the community health center in Xicheng District, Beijing from July to August 2021 were randomly divided into a test group (46 cases) and a control group (45 cases) according to a 1∶1 ratio using the random number table. Baseline data were collected from both groups, and the control group received conventional medication and community telephone follow-up, while the test group was given online mental health education in the form of a WeChat subscription number on this basis, including BD mental health education course push (twice a week) and disease self-management (daily recording of mood, sleep, medication, exercise and gratitude diary), and the intervention period was 6 months in both groups. During the intervention, one patient in the test group was admitted to hospital due to exacerbation of mental illness and the trial was terminated. A total of 90 cases were included in the study. The scores of Medication Adherence Rating Scale (MARS), Hamilton Depression Scale (HAMD), Young Mania Rating Scale (YMRS) and Perceived Devaluation-Discrimination Scale (PDD) were assessed at baseline, after 3 months and 6 months of intervention in both groups, respectively. And the differences in baseline data between the two groups were compared using two independent samples t test and χ2 test, and the repeated-measures ANOVA was used to compare the differences in MARS, HAMD, YMRS, and PDD scores between the two groups before and after the intervention, and to analyze the intervention effects of network mental health education based on the rehabilitation platform on patients in remission stage of BD. Results:After 6 months of intervention, MARS scores in the test group was significantly higher than that in the control group [(8.47±1.75) vs (7.47±1.85)], and was significantly higher than that at baseline (7.36±2.13) and after 3 months of intervention (8.04±1.68) (all P<0.05). YMRS and PDD scores in the test group were significantly lower than those at baseline after 3 and 6 months of intervention [YMRS, 2.0(1.0,4.0),2.0(0,3.0) vs 3.0(1.0,5.5); PDD, (31.18±4.65), (30.13±4.76) vs (32.51±4.51)] (all P<0.05); the differences in YMRS and PDD scores in the control group were not statistically significant (all P>0.05). There was no statistically significant difference in HAMD scores between the two groups before and after the intervention (all P>0.05). Conclusion:Combining mental health education based on rehabilitation platform with conventional medication and community management can significantly improve the medication compliance of patients with BD in remission stage, and improve their manic symptoms and reduce the stigma of the disease.