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 relationship between carotid intraplaque hemorrhage (IPH) in CT angiography(CTA) and perivascular fat density (PFD).Methods:This study retrospectively analyzed 103 patients with carotid atherosclerosis, including 209 carotid plaques, who received carotid CTA and high-resolution magnetic resonance angiography in Shandong Provincial Hospital Affiliated to Shandong First Medical University from January 2018 to July 2023. According to the evaluation of IPH by MRI, the plaques were divided into IPH group ( n=85) and non IPH group ( n=124). CTA was used to evaluate the plaque characteristics (calcification, lumen stenosis, maximum plaque thickness and plaque density) and PFD. Univariate and multivariate logistic analysis was used to evaluate the relationship between PFD and IPH. The diagnostic efficacy of multiple features was performed by receiver operating characteristic curve analysis. Results:Finally, 103 patients with 209 carotid plaques were collected. Multivariate logistic regression analysis showed that PFD ( OR=1.183, 95% CI 1.104-1.267, P<0.001) still showed a significant association with IPH after adjusting for age, hyperlipidemia, degree of luminal stenosis, plaque calcification, maximum plaque thickness, and plaque density. The area under the curve values, sensitivity, specificity, and accuracy of combined PFD, maximum plaque thickness, and plaque density were 0.980, 88.24%, 98.39%, and 94.26%, respectively. Conclusion:The changes of PFD in the carotid artery are closely related to the presence of carotid IPH in MRI and may become a new imaging indicator for high-risk plaque identification and cerebrovascular risk assessment.

As a pivotal noninvasive diagnostic tool of coronary artery disease,coronary CT angiography(CCTA)has achieved remarkable advancements over the past four decades.From early electron beam CT to contemporary photon counting detector CT,CCTA technology has undergone continuous innovation,and its clinical applications expanded from pure anatomical assessment to a multidimensional comprehensive evaluation integrating anatomy,function and histology.The technological evolution clinical application status and future development directions of CCTA were reviewed in this article.

Over the past 40 years,cardiovascular CT and MRI have developed rapidly,achieving significant breakthroughs in technological innovation and clinical practice,and promoting the development of diagnosis and treatment of cardiovascular disease towards a precise and data-driven model.At present,cardiovascular CT has largely replaced invasive catheter-based angiography for diagnosing cardiovascular diseases,while cardiovascular MRI has gradually expanded from routine assessment of cardiac anatomy and function to analysis of myocardial microstructures and quantitation of histopathology and hemodynamics.The technological innovations,clinical application progresses and future development trends of cardiovascular CT and MRI were reviewed in this article.

Objective:To compare the clinical efficacy of autologous bone grafting or bioceramic bone grafting combined with locking compression plate (LCP) internal fixation in the treatment of Schatzker type II and III tibial plateau fractures.Methods:A retrospective analysis was conducted on 104 patients with Schatzker type II and III tibial plateau fractures who underwent surgical treatment at the Department of Orthopedics, PLA Central Theater Command General Hospital from January 2010 to December 2021. The cohort comprised 55 males and 49 females, with an average age of 49.13±13.80 years (range 18-73 years). All fractures were unilateral: 55 on the left and 49 on the right. According to the Schatzker classification, 59 were Type II and 45 were Type III. Causes of injury included traffic accidents (48 cases), falls from height (3 cases), sprains or falls (45 cases), and other causes (8 cases). During surgery, bioceramic material or allograft bone tissue was implanted into the collapsed tibial plateau region to restore articular surface flatness. Based on graft type, patients were divided into the allograft bone group (63 cases) and the bioceramic group (41 cases). All cases underwent proximal lateral tibial LCP internal fixation. The two groups were compared in terms of operative time, intraoperative blood loss, bone graft volume, length of hospital stay, fracture healing time, and postoperative complications. The Rasmussen radiographic collapse score was used to evaluate fracture reduction, and the Hospital for Special Surgery (HSS) score system was used to assess knee joint function.Results:All patients were followed up for 12(12, 13) months (range 12-16 months). The fracture healing time in the allograft bone group was 13.70±1.36 weeks (range 11-16 weeks), which was significantly shorter than that in the bioceramic group: 14.59±1.73 weeks (range 11-19 weeks) ( t=2.911, P=0.004). The time to full weight-bearing in the allograft group was 15.0(14.0, 17.0) weeks (range 13-23 weeks), which was shorter than the 16.0 (15.5, 18.5) weeks (range 12-24 weeks) in the bioceramic group, showing a statistically significant difference ( Z=-3.019, P=0.002). At 12 months postoperatively, the Rasmussen radiographic collapse score was 1(0, 1) in the bioceramic group, significantly lower than the 1(1, 2) score in the allograft group ( Z=-2.083, P=0.037). No statistically significant differences were observed between the two groups in bone graft volume, surgical duration, intraoperative blood loss, hospitalization duration, or HSS scores at 6 and 12 months postoperatively ( P>0.05). The complication rate was 6.3% (4/63) in the allograft group and 2.4% (1/41) in the bioceramic group, with no statistically significant difference (χ 2=0.830, P=0.362). Conclusions:For Schatzker type II and III tibial plateau fractures, the use of allograft bone or bioceramic combined with LCP internal fixation can achieve good results. Allograft bone has advantages in terms of fracture healing speed and early weight-bearing recovery, while bioceramic is more effective in maintaining joint surface stability.

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 develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

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.