Objective: To explore the efficacy of a novel injectable hydrogel (GelMA/P11/IL4@LIP) loaded with P11 bacteriophages and interleukin-4 (IL-4) liposomes (LIP) in preventing relapse after maxillary expansion in mice, providing experimental evidence for its clinical application. Methods: This study was approved by the experimental animal ethics committee of our hospital. First, 15 7-week-old C57BL/6 mice were used to establish a maxillary expansion model and divided into 5 groups (3 mice in each group): a control group, post expansion day 3 group (PED3 group), post expansion day 7 group (PED7 group), retention for 14 days group (RET group), and relapse for 7 days group (REL group). The mice in each group were sacrificed at their designated time points (day 0, 3, 7, 21, 28), and their maxilla and anterior cranial regions were collected. Bone parameters and the inter-crestal distance (ICD) of maxillary incisor mesial alveolar ridge were measured using micro-computed tomography (micro-CT). Histological staining was performed to evaluate bone formation and resorption, while immunohistochemistry (IHC) was performed for macrophage markers (CD86 and CD206), mesenchymal stem cell markers (glioma-associated oncogene homolog 1 [Gli1]), and osteogenic markers (Runt-related transcription factor 2 [Runx2] and Osterix [OSX]). Next, GelMA/P11/IL4@LIP was synthesized and administered to mouse models of maxillary expansion. A total of 24 7-week-old C57BL/6 mice were divided into 4 groups (6 mice in each group): a blank control group, GelMA group, GelMA/P11 group, and GelMA/P11/IL4@LIP group. All mice underwent palatal expansion. On PED7, the expanders of all 24 mice were cemented with resin to initiate the 14-day retention period. On day 1 of the retention phase, the mice in each group received injections of saline, GelMA, GelMA/P11, or GelMA/P11/IL4@LIP at the midpalatal suture. After the 14-day retention period, three mice in each group were randomly selected and sacrificed, while the other three had their expanders removed and underwent a 7-day relapse before being sacrificed on day 28 (REL). Micro-CT, histological staining, and IHC were performed to evaluate the preventive effect of GelMA/P11/IL4@LIP on post-expansion relapse. Results: The mice maxillary expansion model exhibited a decreased ICD at REL compared to RET in micro-CT analysis (P = 0.008). IHC analysis demonstrated prolonged M1 macrophage infiltration, scarce Gli1+ mesenchymal stem cells, and insufficient expression of osteogenic markers (RUNX2 and OSX) (P < 0.001). Compared to the blank control and GelMA groups, GelMA/P11/IL4@LIP hydrogel injection in the midpalatal suture led to increased ICD at REL, promoted the timely M2 polarization of macrophages, recruited Gli1+ mesenchymal stem cells, and upregulated the expression of RUNX2 and OSX (P < 0.05). Conclusion The mechanism of relapse after maxillary expansion involves the persistent infiltration of M1 macrophages, as well as the inadequate recruitment and insufficient osteogenic differentiation of MSCs in the midpalatal suture. The GelMA/P11/IL4@LIP composite enhanced orofacial mesenchymal stem cell recruitment and promoted the M2 polarization of macrophages, thereby enhancing osteogenesis in the midpalatal suture and preventing post-expansion relapse.

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

Objective:To construct a combined prediction model based on dual-layer detector spectral CT radiomics features and quantitative parameters, and to evaluate its value in preoperative prediction of serosal invasion in gastric cancer.Methods:This case-control study retrospectively analyzed data from 253 gastric cancer patients confirmed by postoperative pathology at the First Affiliated Hospital of Soochow University (Center 1) and Changshu No.2 People′s Hospital (Center 2) from January 2022 to December 2023. Patients from Center 1 ( n=157) were randomly divided into training set ( n=110) and test set ( n=47) in a 7∶3 ratio, while patients from Center 2 ( n=96) served as an external validation set. Based on postoperative pathological serosal invasion status, patients were classified into serosal invasion group ( n=164) and non-serosal invasion group ( n=89), with distributions of 70/40, 30/17, and 64/32 in the training, test, and external validation sets, respectively. Spectral CT quantitative parameters, including arterial and venous phase iodine concentration (IC), normalized iodine concentration (NIC), arterial-venous IC differences, arterial-venous NIC differences (NIC pa), arterial enhancement fraction (AEF), and effective atomic number (Z eff), were measured. Radiomics features were extracted from venous-phase 40 keV monochromatic images. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. The logistic regression classifier (LR-LASSO) was applied to construct the radiomics model. Univariate and multivariate logistic regression analyses identified independent risk factors for serosal invasion, including the radiomics signature (RadScore) and quantitative parameters. A clinical model was built using significant quantitative parameters, and a combined model integrated RadScore. An artificial model was based on cT4 staging assessed by two radiologists using venous-phase CT. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results:A total of six radiomics features were selected to establish the radiomics model. RadScore ( OR=7.598, 95% CI 2.259-25.562, P=0.001) and NIC pa ( OR=4.598, 95% CI 1.404-15.050, P=0.012) served as independent risk factors. The NIC pa served as the clinical model. The AUCs (95% CI) of the combined model in the training, test, and external validation sets were 0.984 (0.969-1.000), 0.855 (0.728-0.982), and 0.773 (0.665-0.882), respectively. The AUCs of the artificial model were 0.741, 0.670, 0.644; of the clinical model were 0.709, 0.633, 0.626. The AUCs of the radiomics model were 0.963, 0.824, 0.741, respectively. Calibration curves showed good agreement between predicted probability and observed probability. The DCA confirmed higher clinical net benefits for the combined model. Conclusion:The combined model integrating dual-layer detector spectral CT radiomics features and quantitative parameters exhibits high efficacy for preoperative prediction of gastric cancer serosal invasion.

China emerges as a major country in nuclear energy development and the application of nuclear and radiologic technology. The diagnosis and treatment of acute radiation syndrom (ARS) caused by external irradiation represent a core function in the country′s medical rescue of nuclear and radiological emergencies. Clinically, ARS manifests hematopoietic, gastrointestinal, cutaneous, and central nervous system syndromes, with specific clinical manifestations, signs, severity, and prognosis strongly correlated with radiation dose. China has established a number of national and provincial centers for treating radiation-induced damage. Nevertheless, most medical staff have limited experience in ARS treatment. This consensus presents a summary of recent experience in treating ARS of China. In combination with recommendations from international organizations such as the World Health Organization (WHO), this consensus proposes key evidence of critical clinical issues of ARS, covering all links in the rescue of external irradiation-induced ARS. Initially, clinical diagnosis, syndromes, and severe degrees should be determined based on clinical symptoms and dose estimates. It is necessary to normalize clinical treatment measures for hematopoietic recovery, gastrointestinal injury treatment, infection control, symptomatic treatment, and multi-organ function preservation. To this end, this consensus offers cautions. This consensus provides principles of treatment with traditional Chinese medicine, psychological intervention, and follow-up. Additionally, it highlights multidisciplinary collaboration. It is recommended that this consensus be applied in relevant treatment centers.

Objective:To investigate the situation of computed tomography(CT)equipment and socio economic development of various grades and classifications of medical institutions in Jiangsu Province,and to analyze the interrelationship between them.Methods:From October 20,2023 to November 20,2023,a questionnaire survey was conducted on imported and domestic CT equipment of various grades and classifications of medical institutions in 13 prefecture-level cities in Jiangsu Province through online questionnaire survey of"questionnaire star".The gross domestic product(GDP)of each prefecture-level city in Jiangsu province was used as an indicator of measuring the level of socio-economic development in the region.The CT number,the row number of detector,quality of image,the total examination case number of daily average and the daily average case number of single machine examination of medical institutions of various grades and classifications of 13 prefecture-level cities were used to conduct correlation analysis with GDP values of various districts.Results:A total of 910 questionnaires were distributed,and 512 questionnaires were retrieved.There were 482 valid questionnaires in the 512 questionnaires,and the valid rate was 94.15,which involved to 482 various grade and classification institutions of 13 prefecture-level cities in Jiangsu province.The number of imported and domestic CT equipment in the 13 prefecture-level cities of Jiangsu Province positively correlated with the GDP of each city(r=0.882,0.880,P<0.001).The number of imported CT equipment with 64 rows and more than 64 rows,and with less than 64 rows,and the number of domestic CT equipment with 64 rows and more than 64 rows,and with less than 64 rows all positively correlated with the GDP of each city(r=0.880,0.881,0.736,0.897,P<0.05).The total number of daily average of examinations for imported and domestic CT equipment of 13 prefecture-level cities positively correlated with the GDP of each city(r=0.774,0.814,P<0.05).There were no significant difference in the image quality and the daily average case number of single machine examination between the domestic CT equipment and the imported CT equipment(P>0.05).Conclusion:The medical institutions of city with high level of economic development have more quantitative and more high-end CT equipment,and the quality of image of domestic CT equipment is equal to that of imported CT equipment regardless of the high and low level of economic development.

Objective:To investigate the situation of computed tomography(CT)equipment and socio economic development of various grades and classifications of medical institutions in Jiangsu Province,and to analyze the interrelationship between them.Methods:From October 20,2023 to November 20,2023,a questionnaire survey was conducted on imported and domestic CT equipment of various grades and classifications of medical institutions in 13 prefecture-level cities in Jiangsu Province through online questionnaire survey of"questionnaire star".The gross domestic product(GDP)of each prefecture-level city in Jiangsu province was used as an indicator of measuring the level of socio-economic development in the region.The CT number,the row number of detector,quality of image,the total examination case number of daily average and the daily average case number of single machine examination of medical institutions of various grades and classifications of 13 prefecture-level cities were used to conduct correlation analysis with GDP values of various districts.Results:A total of 910 questionnaires were distributed,and 512 questionnaires were retrieved.There were 482 valid questionnaires in the 512 questionnaires,and the valid rate was 94.15,which involved to 482 various grade and classification institutions of 13 prefecture-level cities in Jiangsu province.The number of imported and domestic CT equipment in the 13 prefecture-level cities of Jiangsu Province positively correlated with the GDP of each city(r=0.882,0.880,P<0.001).The number of imported CT equipment with 64 rows and more than 64 rows,and with less than 64 rows,and the number of domestic CT equipment with 64 rows and more than 64 rows,and with less than 64 rows all positively correlated with the GDP of each city(r=0.880,0.881,0.736,0.897,P<0.05).The total number of daily average of examinations for imported and domestic CT equipment of 13 prefecture-level cities positively correlated with the GDP of each city(r=0.774,0.814,P<0.05).There were no significant difference in the image quality and the daily average case number of single machine examination between the domestic CT equipment and the imported CT equipment(P>0.05).Conclusion:The medical institutions of city with high level of economic development have more quantitative and more high-end CT equipment,and the quality of image of domestic CT equipment is equal to that of imported CT equipment regardless of the high and low level of economic development.

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

Objective:To construct a combined prediction model based on dual-layer detector spectral CT radiomics features and quantitative parameters, and to evaluate its value in preoperative prediction of serosal invasion in gastric cancer.Methods:This case-control study retrospectively analyzed data from 253 gastric cancer patients confirmed by postoperative pathology at the First Affiliated Hospital of Soochow University (Center 1) and Changshu No.2 People′s Hospital (Center 2) from January 2022 to December 2023. Patients from Center 1 ( n=157) were randomly divided into training set ( n=110) and test set ( n=47) in a 7∶3 ratio, while patients from Center 2 ( n=96) served as an external validation set. Based on postoperative pathological serosal invasion status, patients were classified into serosal invasion group ( n=164) and non-serosal invasion group ( n=89), with distributions of 70/40, 30/17, and 64/32 in the training, test, and external validation sets, respectively. Spectral CT quantitative parameters, including arterial and venous phase iodine concentration (IC), normalized iodine concentration (NIC), arterial-venous IC differences, arterial-venous NIC differences (NIC pa), arterial enhancement fraction (AEF), and effective atomic number (Z eff), were measured. Radiomics features were extracted from venous-phase 40 keV monochromatic images. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. The logistic regression classifier (LR-LASSO) was applied to construct the radiomics model. Univariate and multivariate logistic regression analyses identified independent risk factors for serosal invasion, including the radiomics signature (RadScore) and quantitative parameters. A clinical model was built using significant quantitative parameters, and a combined model integrated RadScore. An artificial model was based on cT4 staging assessed by two radiologists using venous-phase CT. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results:A total of six radiomics features were selected to establish the radiomics model. RadScore ( OR=7.598, 95% CI 2.259-25.562, P=0.001) and NIC pa ( OR=4.598, 95% CI 1.404-15.050, P=0.012) served as independent risk factors. The NIC pa served as the clinical model. The AUCs (95% CI) of the combined model in the training, test, and external validation sets were 0.984 (0.969-1.000), 0.855 (0.728-0.982), and 0.773 (0.665-0.882), respectively. The AUCs of the artificial model were 0.741, 0.670, 0.644; of the clinical model were 0.709, 0.633, 0.626. The AUCs of the radiomics model were 0.963, 0.824, 0.741, respectively. Calibration curves showed good agreement between predicted probability and observed probability. The DCA confirmed higher clinical net benefits for the combined model. Conclusion:The combined model integrating dual-layer detector spectral CT radiomics features and quantitative parameters exhibits high efficacy for preoperative prediction of gastric cancer serosal invasion.

Objective To investigate the effect of butorphanol on lipopolysaccharide-induced chondrocyte injury by regulating the stromal cell-derived factor-1α(SDF-1α)/C-X-C chemokine receptor 4(CXCR4)pathway.Methods Human C28/12 chondrocytes were cultured in vitro and assigned to the following groups:control(normal culture),model(100 μmol/L lipopolysaccharide),model+low-dose butorphanol(100 μmol/L lipopolysaccharide+1μmol/L butorphanol),model+medium-dose butorphanol(100 μmol/L lipopolysaccharide+2 μmol/L butorphanol),model+high-dose butorphanol(100 μmol/L lipopolysaccharide+4 μmol/L butorphanol),and model+high-dose butorphanol+NUCC-390(100 μmol/L lipopolysaccharide+4 μmol/L butorphanol+500 nmol/L CXCR4 agonist NUCC-390).Cell viability,interleukin(IL)-6 and tumor necrosis factor-α(TNF-α)levels,apoptosis,and SDF-1α/CXCR4 pathway-related proteins were evaluated by MTT assay,enzyme-linked immunosorbent assay,flow cytometry,and Western blot,respectively.Results Chondrocyte survival rate and Bcl-2 protein expression were decreased while TNF-α,IL-6,apoptosis rate,Bax,Cleaved caspase-3,SDF-1α,and CXCR4 proteins were increased in the model group compared with the control group(P＜0.05).The above indicators were improved in the model+low-,medium-,and high-dose butorphanol groups compared with the model group,while the result for the model+high-dose butorphanol+NUCC-390 group were opposite to those of the model+high-dose butorphanol group.Conclusions Butorphanol may improve lipopolysaccharide-induced chondrocyte injury induced by inhibiting the SDF-1α/CXCR4 signaling pathway.

Objective To investigate the effect of butorphanol on lipopolysaccharide-induced chondrocyte injury by regulating the stromal cell-derived factor-1α(SDF-1α)/C-X-C chemokine receptor 4(CXCR4)pathway.Methods Human C28/12 chondrocytes were cultured in vitro and assigned to the following groups:control(normal culture),model(100 μmol/L lipopolysaccharide),model+low-dose butorphanol(100 μmol/L lipopolysaccharide+1μmol/L butorphanol),model+medium-dose butorphanol(100 μmol/L lipopolysaccharide+2 μmol/L butorphanol),model+high-dose butorphanol(100 μmol/L lipopolysaccharide+4 μmol/L butorphanol),and model+high-dose butorphanol+NUCC-390(100 μmol/L lipopolysaccharide+4 μmol/L butorphanol+500 nmol/L CXCR4 agonist NUCC-390).Cell viability,interleukin(IL)-6 and tumor necrosis factor-α(TNF-α)levels,apoptosis,and SDF-1α/CXCR4 pathway-related proteins were evaluated by MTT assay,enzyme-linked immunosorbent assay,flow cytometry,and Western blot,respectively.Results Chondrocyte survival rate and Bcl-2 protein expression were decreased while TNF-α,IL-6,apoptosis rate,Bax,Cleaved caspase-3,SDF-1α,and CXCR4 proteins were increased in the model group compared with the control group(P＜0.05).The above indicators were improved in the model+low-,medium-,and high-dose butorphanol groups compared with the model group,while the result for the model+high-dose butorphanol+NUCC-390 group were opposite to those of the model+high-dose butorphanol group.Conclusions Butorphanol may improve lipopolysaccharide-induced chondrocyte injury induced by inhibiting the SDF-1α/CXCR4 signaling pathway.