Background Pneumoconiosis is the most serious occupational disease in China, among which silicosis accounts for more than 50%. microRNA (miRNA) plays an important role in the occurrence process of silicosis fibrosis, but the mechanism of it has not been fully clarified yet. Objective To explore the molecular mechanism by which miR-411-3p modulates the ubiquitination degradation of SMAD specific E3 ubiquitin protein ligase (SMURF) 2/Smad7, thereby suppressing epithelial-mesenchymal transition (EMT) in mouse alveolar type II epithelial cells and counteracting silica-induced pulmonary fibrosis. Methods Twenty-four 8-week-old SPF male C57BL/6J mice were randomly divided into four groups: Control group, silica group, silica +miR-411-3p agomir-NC group, and silica +miR-411-3p agomir group, with 6 mice in each group. Silicosis model was prepared by a one-time bronchial infusion of silicon dioxide (SiO₂) (200 mg·mL^-1, 50 μL). In vitro MLE-12 cells were divided into (1) control group and SiO₂ group, (2) SiO₂+negative control siRNA (siRNA-NC) group and SiO₂+Smurf2 gene silencing (si-Smurf2) group, (3) SiO₂+solvent (DMSO) group and SiO₂+protease inhibitor (MG132) group, (4) mutant sequence plasmid (Mut)+miR-411-3p mimic control (miR-NC) group, Mut+miR-411-3p mimic group, wild sequence plasmid (Wt)+miR-NC group, and Wt+miR-411-3p mimic group, (5) SiO₂+miR-NC group and SiO₂+miR-411-3p mimic group. The pathological morphology and collagen deposition of lung tissue were observed after staining. Detection of miR-411-3p and proteins was conducted by real-time fluorescent quantitative PCR and Western blot. The binding of SMURF2 to Smad7 protein and Smad7 to ubiquitin (Ub) were detected by co-immunoprecipitation (Co-IP) method. Dual-luciferase reporter gene assay was adopted to verify the regulatory effect of miR-411-3p on Smurf2. Results In the SiO₂-induced MLE-12 cells, compared to the control group, the SiO₂-treated group showed significantly upregulated expressions of N-cadherin (N-Cad), collagen I (CoL I), SMURF2, transforming growth factor-β1 (TGF-β1), and phosphorylated Smad2/3 (p-Smad2/3). In contrast, the expressions of E-cadherin (E-Cad), Smad7, and miR-411-3p were significantly downregulated (P<0.05). The dual-luciferase reporter gene assay revealed a regulatory effect of miR-411-3p on Smurf2 (P<0.05). Meanwhile, in the MLE-12 cells induced by SiO₂, the miR-411-3p mimic down-regulated the protein expressions of SMURF2, N-Cad, CoL I, TGF-β1, and p-Smad2/3, while up-regulated the protein expressions of E-Cad and Smad7 (P<0.05). The silenced Smurf2 gene inhibited the expressions of N-Cad, CoL I, and p-Smad2/3 proteins, while promoted the expressions of E-Cad and Smad7 proteins in the MLE-12 cells (P<0.05). The Co-IP results showed that the binding of SMURF2 to Smad7 was enhanced, and the ubiquitin binding ability of Smad7 was enhanced in the SiO₂ group. In the lung tissue of mice, the results of pathological observation with hematoxylin-eosin (HE) and sirius red (VG) staining showed that compared with the agomir-NC, the lesion was relieved in the lung tissue of the miR-411-3p agomir group. Meanwhile, the expressions of SMURF2, N-Cad, CoL I, TGF-β1, and p-Smad2/3 were significantly down-regulated, while the expressions of E-Cad and Smad7 were significantly up-regulated (P<0.05). Conclusion MiR-411-3p alleviates the EMT of alveolar type II epithelial cells and antagonizes silicosis fibrosis progression in mice by inhibiting SMURF2-mediated ubiquitination and degradation of Smad7.

Acute lung injury (ALI) is a severe disease caused by viral infection that triggers an uncontrolled inflammatory response. This study investigated the capacity of jasurolignoside (JO), a natural compound, to bind to Toll-like receptor 4 (TLR4) and treat ALI. The anti-inflammatory properties of JO were evaluated in vitro through Western blotting, enzyme-linked immunosorbent assay (ELISA), immunofluorescence staining, and co-immunoprecipitation. The investigation utilized a lipopolysaccharide (LPS)-induced ALI animal model to examine the therapeutic efficacy and mechanism of JO in vivo. JO attenuated inflammatory symptoms in infected cells and tissues by modulating the NOD-like receptor family pyrin domain containing protein 3 (NLRP3) inflammasome and the nuclear factor κB (NF-κB)/mitogen-activated protein kinase (MAPK) pathway. Molecular docking simulations revealed JO binding to TLR4 active sites, confirmed by cellular thermal shift assay. Surface plasmon resonance (SPR) demonstrated direct interaction between JO and TLR4 with a Kd value of 35.1 μmol·L^-1. Moreover, JO inhibited tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), and IL-6 secretion and reduced leukocyte, neutrophil, lymphocyte, and macrophage infiltration in ALI-affected mice. JO also enhanced lung function and reduced ALI-related mortality. Immunohistochemical staining demonstrated JO's ability to suppress TLR4 expression in ALI-affected mouse lung tissue. This study establishes that JO can bind to TLR4 and effectively treat ALI, indicating its potential as a therapeutic agent for clinical applications.
Toll-Like Receptor 4/chemistry* ; Animals ; Acute Lung Injury/chemically induced* ; Mice ; Humans ; Ilex/chemistry* ; Molecular Docking Simulation ; Male ; NF-kappa B/immunology* ; Mice, Inbred C57BL ; NLR Family, Pyrin Domain-Containing 3 Protein/immunology* ; Tumor Necrosis Factor-alpha/genetics* ; Interleukin-1beta/genetics* ; RAW 264.7 Cells ; Disease Models, Animal

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.

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 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: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: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: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.