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.

Objective:To investigate the effect of Qideng Mingmu capsule on the formation and remodeling of retinal neovascularization in mice with oxygen-induced retinopathy (OIR).Methods:Thirty-six postnatal day 7 (P7)SPF grade C57BL/6J pups were divided into normal group, OIR group, Qideng Mingmu capsule group and apatinib group by random number table method, with 9 mice in each group.The mice in the normal group were raised in normal environment.The mice in the other three groups were fed in hyperoxic environment of (75±2)% oxygen concentration for 5 days from P7 to P12 and then were fed in normal environment for 5 days from P12 to P17 to establish the OIR model.From P12, mice in Qideng Mingmu capsule group and apatinib group were given intragastric administration of Qideng Mingmu capsule (900 mg/kg) and vascular endothelial growth factor receptor 2 inhibitor apatinib (70 mg/kg) respectively, once a day for 5 consecutive days.On P17, paraffin sections of mouse eyeballs were made and stained with hematoxylin-eosin to count the number of vascular endothelial cells that broke through the internal limiting membrane.The retinal slices were prepared and stained with FITC-dextran to quantify the retinal non-perfusion area, neovascularization density and total vascular density.The distribution and fluorescence intensity of retinal vascular endothelial cell marker CD31 and pericyte marker α-smooth muscle actin (α-SMA) were observed by double immunofluorescence staining.Immunohistochemical staining was used to detect the expression and distribution of retinal hypoxia inducible factor-1α (HIF-1α) and vascular endothelial cadherin (VE-cadherin).The use and care of animals were in accordance with the Regulations on the Management of Laboratory Animals issued by the Ministry of Science and Technology.This study was approved by the Animal Ethics Committee of Chengdu University of Traditional Chinese Medicine (No.2019-30).Results:The number of vascular endothelial cells breaking through the internal limiting membrane in normal group, OIR group, Qideng Mingmu capsule group and apatinib group were (2.83±4.40), (37.33±5.43), (23.83±6.79) and (14.00±9.34), respectively, with a statistically significant overall difference ( F=28.313, P<0.001).There were more vascular endothelial cells breaking through internal limiting membrane in OIR group than in normal group, Qideng Mingmu capsule group and apatinib group, showing statistically significant differences (all at P<0.05).In the observation of mouse retinal slices, there were large non-perfusion areas, neovascularization buds and disordered distribution of blood vessels in OIR group.The distribution of blood vessels was more uniform and the areas of non-perfusion and neovascularization were smaller in Qideng Mingmu capsule group and apatinib group than in OIR group.The relative area of central retinal non-perfusion area and neovascularization density were significantly lower in normal group, Qideng Mingmu capsule group and apatinib group than in OIR group (all at P<0.05).The immunofluorescence intensity of CD31 and the absorbance value of HIF-1α were significantly lower, and the immunofluorescence intensity of α-SMA and the absorbance value of VE-cadherin were significantly higher in normal group, Qideng Mingmu capsule group and apatinib group than in OIR group (all at P<0.05). Conclusions:Qideng Mingmu capsule can inhibit retinal neovascularization formation, increase vascular pericyte coverage, relieve retinal hypoxia and increase vascular integrity in OIR mice.It can protect the retinal vessels of OIR mice.

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: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.

Hepatocellular carcinoma (HCC) is characterized by high postoperative recurrence and mortality rates. In recent years, researchers have identified a significant correlation between microvascular invasion (MVI) and early postoperative recurrence and metastasis of HCC, making it a focal point of HCC research. Accurate preoperative prediction of MVI occurrence and the implementation of relevant interventions (such as expanded resection) could provide substantial benefits to patients. This study analyzes global research over the past decade on MVI predictive indicators based on tumor biological characteristics, genetic measurements, imaging examinations, and tumor markers. The aim is to use these predictive indicators to objectively forecast the occurrence of MVI, thereby aiding in preoperative individual assessments and enhancing treatment plans.

Objective:To evaluate the significance of copy number variation (CNV) and metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) in the diagnosis of meningeal carcinomatosis (MC).Methods:Ten patients with MC diagnosed in the Department of Neurology of Peking Union Medical College Hospital from March 2022 to June 2022 were consecutively enrolled in this study. The patients were diagnosed according to the criteria of the Chinese expert consensus on the diagnosis of MC by the Chinese Society of Infectious Diseases and Cerebrospinal Fluid Cytology, and the diagnosis of MC was confirmed by CSF cytology. The control group included 10 patients who were diagnosed as autoimmune encephalitis or viral encephalitis. CSF mNGS and CNV analysis were performed simultaneously in all the patients.Results:Of the 10 patients with MC, 6 had lung adenocarcinoma, 4 had breast cancer. CSF mNGS and CNV analysis detected large CNV in 8 of 10 patients with MC, including 4 patients with breast cancer and 4 patients with lung cancer. The results of pathogenic microorganism analysis of CSF mNGS in all the patients were negative. Meanwhile, large CNV was not detected in the control group.Conclusions:CSF CNV can serve as a diagnostic marker for MC. The combination of mNGS and CNV analysis has demonstrated a high positive rate in the diagnosis of MC. The dual-omics analysis of pathogenic microorganisms and CNV has been proposed as a potential strategy to further expand the clinical utility of CSF mNGS in the realm of auxiliary diagnosis.

Monoclonal antibody drugs that inhibit programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1) have been widely used in esophageal cancer (EC) and yielded significant therapeutic responses. However, only a few patients obtain lasting clinical benefits due to primary or acquired drug resistance, and new treatment schemes are urgently needed. The tumor immune microenvironment is the main factor that affects patients' response to immunosuppressive agents. This article will discuss the role of immunosuppressive cells and non-cellular components in the immune process to provide ideas for the next research direction of EC.

ObjectiveTo evaluate the Chinese guidelines and consensus of rehabilitation medicine published in the medical journals in 2022 using Scientific, Transparent and Applicable Rankings (STAR). MethodsGuidelines and consensus which were developed by Chinese institutions or led by Chinese scholars were retrieved in databases of CNKI, Wanfang Data, CBM, Chinese Medical Journal Network, PubMed and Web of Science, in 2022, followed by screening for rehabilitation medicine field. The literature were rated with STAR. ResultsSeven guidelines and eleven consensuses were included. The STAR scores ranged from 11.7 to 69.6, with a median score of 25.9 and mean score of 28.3. There was a significant difference in the total score between guidelines and consensus (U = 12.000, P = 0.014). The score ratio was high in the domains of recommendations (73.6%), evidence (39.5%) and others (33.3%), while it was low in the domains of protocol (1.4%), clinical questions (12.5%) and conflicts of interest (13.9%). The score ratio was high in the items of listing the institutional affiliations of all individuals involved in developing the guideline (94.4%), identifying the references for evidence supporting the main recommendations (94.4%), indicating the considerations (e.g., adverse effects) in clinical practice when implementing the recommendations (88.9%), and making the recommendations clearly identifiable, e.g., in a table, or using enlarged or bold fonts (75%); and it was low in the items of describing the role of funder(s) in the guideline development (0), indicating information about the evaluation and management of conflicts of interest (0), providing tailored editions of the guidelines for different groups of target users (0), presenting the guideline or recommendations visually, such as with figures or videos (0), providing details of the guideline protocol (2.8%), assessing the risk of bias or methodological quality of the included studies (2.8%), describing the responsibilities of all individuals or sub-groups involved in developing the guideline (5.6%), indicating how the clinical questions were selected and sorted (5.6%), formating clinical questions in PICO or other formats (5.6%), making the guideline accessible through multiple platforms (5.6%), and declaring that the funder(s) did not influence the guideline's recommendations (8.3%). ConclusionThe quality of current clinical practice guidelines and consensus of rehabilitation medicine is poor, which should be developed in accordance with the relevant standards.

This paper explored the feasibility of using ultrasonic Nakagami statistic parameter imaging to evaluate the thermal lesion induced by microwave ablation (MWA) in porcine models. In this paper, thermal lesions were induced in livers and kidneys in 5 swines using a clinical MWA system. During this treatment progress, ultrasonic radiofrequency (RF) data were collected. The dynamic changes of Nakagami parameter in the thermal lesion were calculated, and the ultrasonic B-mode images and Nakagami images were reconstructed simultaneously. The contrast-to-noise ratio (CNR) between the thermal lesion and the surrounding normal tissue was calculated over the MWA procedure. After MWA, a bright hyperechoic region appeared in the ultrasonic Nakagami image as an indicator of the thermal lesion and this bright spot enlarged with lesion development during MWA exposure. The mean value of Nakagami parameter in the liver and kidney increased from 0.78 and 0.79 before treatment to 0.91 and 0.92 after treatment, respectively. During MWA exposure, the mean values of CNR calculated from the Nakagami parameter increased from 0.49 to 1.13 in the porcine liver and increased from 0.51 to 0.85 in the kidney, which were both higher than those calculated from the B-mode images. This study on porcine models suggested that the ultrasonic Nakagami imaging may provide an alternative modality for monitoring MWA treatment.
Animals ; Kidney ; diagnostic imaging ; Liver ; diagnostic imaging ; Microwaves ; Radio Waves ; Radiofrequency Ablation ; Swine ; Ultrasonography