Crohn's disease (CD) is a chronic progressive inflammatory bowel disease. With the introduction of the "treat-to-target (T2T) " concept, the treatment goals for CD have become clearer and more specific. Traditional surgical treatment for CD typically follows a "complication-driven" approach, in which surgery is usually performed only after severe complications, such as bowel obstruction, fistulas, perforation, or cancer have occurred. The emergence of the treat-to-target strategy and the concept of disease clearance has transformed the surgical treatment of CD from a "passive rescue" model to an "active intervention" approach. Treatment goals have shifted from merely addressing complications and improving symptoms to achieving both short and long-term therapeutic objectives within the framework of treat-to-target. Achieving these goals helps to prevent CD-related complications, delay disease progression, reduce the risk of recurrence and malignancy, and improve the quality of life.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

ObjectiveTo observe the clinical effectiveness and safety of Xiaozhong Zhitong Mixture （消肿止痛合剂） combined with antibiotic bone cement in the treatment of diabetic foot ulcer （DFU） with damp-heat obstructing syndrome. MethodsA total of 72 DFU patients with damp-heat obstructing syndrome were randomly assigned to treatment group （36 cases） and the control group （36 cases）. Both groups received standard treatment and topical antibiotic bone cement for ulcer wounds， while the treatment group received oral Xiaozhong Zhitong Mixture （50 ml per time， three times daily） in additionally. Both groups underwent daily wound dressing changes for 21 consecutive days. Ulcer healing rate， serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1 beta （IL-1β）， malondialdehyde （MDA）， superoxide dismutase （SOD）， C-reactive protein （CRP）， and white blood cell （WBC） count were observed before and after treatment， and visual analog scale （VAS） scores for wound pain， traditional Chinese medicine （TCM） syndrome scores， and the DFU Healing Scale （DMIST scale） were also compared. Liver and kidney function were evaluated before and after treatment， and adverse events such as allergic reactions， worsening ulcer pain were recorded. ResultsTotally 35 patients in the treatment group and 33 in the control group were included in the final analysis. The ulcer healing rate in the treatment group was （87.93±9.34）%， significantly higher than （81.82±12.02）% in the control group （P = 0.035）. Compared to pre-treatment levels， both groups showed significant reductions in serum CRP， WBC， MDA， IL-1β， and TNF-α levels， with an increase in SOD level （P＜0.05）. TCM syndrome scores， VAS， and DMIST scores also significantly decreased in both groups （P＜0.05）， with greater improvements in the treatment group （P＜0.05）. No significant adverse reactions were observed in either group during treatment. ConclusionXiaozhong Zhitong Mixture combined with antibiotic bone cement has significant advantages in promoting DFU healing， reducing inflammatory response， and alleviating oxidative stress in DFU patients with damp-heat obstructing syndrome， with good safety for DFU patients with damp-heat obstructing syndrome.

AIM: To predict diabetic retinopathy(DR)progression through macular layer thickness in diabetic patients detected by optical coherence tomography(OCT).METHODS: Retrospective study. The clinical data of 100 cases(200 eyes)of diabetic patients admitted to our hospital from January 2023 to September 2024 were collected. According to the international clinical DR classification, they were divided into the non-diabetic retinopathy(NDR)group with 32 cases(64 eyes), the non-proliferative diabetic retinopathy(NPDR)group with 38 cases(76 eyes), and the proliferative diabetic retinopathy(PDR)group with 30 cases(60 eyes). At the same time, 49 cases(98 eyes)of healthy controls whose age and gender were matched with those of the diabetic patients were collected as the normal group. All patients underwent OCT examination. The thickness changes of the retinal nerve fiber layer(RNFL), ganglion cell layer(GCL), inner plexiform layer(IPL), outer nuclear layer(ONL), photoreceptor cell layer and total retinal thickness(RT)in the subregions of the macular area were compared among the groups. The Eta coefficient was used to analyze the correlation between them and the severity of DR.RESULTS: The thickness of RNFL, GCL, IPL, ONL and photoreceptor cell layer in each sub-region and the average of macular area in the PDR group was significantly lower than that in the NDR and normal groups, while the average RT thickness was significantly higher than that in the NPDR, NDR and normal groups(all P<0.05). The thickness of RNFL(central area, upper inner and outer rings and lower inner and outer rings and average), GCL(upper inner and outer rings and lower inner and outer rings and average), IPL(upper inner ring), ONL(central, upper inner ring and lower inner ring)and photoreceptor cell layer(upper inner and outer rings and lower inner and outer rings and average)in macular area of the PDR group was significantly thicker than that in the NPDR group(all P<0.05). The thickness of RNFL, GCL, IPL, ONL and photoreceptor cell layer in each sub-region and the average of macular area in the NPDR group was significantly lower than that in the NDR and normal groups, while the average RT thickness was significantly thicker than that in the NDR and normal groups(all P<0.05). There was no statistically significant difference in the above indicators between the NDR group and the normal group(all P>0.05). The severity of DR was significantly correlated with the average thickness of RNFL, GCL, IPL, ONL, photoreceptor cell layer and RT in macular area(all P<0.001).CONCLUSION: OCT measurement of the thickness of RNFL, GCL, IPL, ONL, photoreceptor cell layer and RT in the macular area in the diabetic patients can evaluate the progression of DR.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

Crohn's disease (CD) is a chronic progressive inflammatory bowel disease. With the introduction of the "treat-to-target (T2T) " concept, the treatment goals for CD have become clearer and more specific. Traditional surgical treatment for CD typically follows a "complication-driven" approach, in which surgery is usually performed only after severe complications, such as bowel obstruction, fistulas, perforation, or cancer have occurred. The emergence of the treat-to-target strategy and the concept of disease clearance has transformed the surgical treatment of CD from a "passive rescue" model to an "active intervention" approach. Treatment goals have shifted from merely addressing complications and improving symptoms to achieving both short and long-term therapeutic objectives within the framework of treat-to-target. Achieving these goals helps to prevent CD-related complications, delay disease progression, reduce the risk of recurrence and malignancy, and improve the quality of life.

Biomaterials have gained substantial attention in the field of regenerative medicine and shown respectable potential in clinical translation. However, the function of biomaterials is hindered by the fibrotic encapsulation of foreign body response (FBR) under some circumstances. FBR also lowers the expected service life of implanted devices and causes pain, distortion of tissue or even malignant diseases. Therefore, the mechanism of FBR and the modulating factors need to be further investigated to improve the tissue biocompatibility of biomaterials. This article reviewed the process of FBR, summarized the properties and modification of biomaterials related to FBR, and discussed strategies to mitigate this immune reaction.

Biomaterials have gained substantial attention in the field of regenerative medicine and shown respectable potential in clinical translation. However, the function of biomaterials is hindered by the fibrotic encapsulation of foreign body response (FBR) under some circumstances. FBR also lowers the expected service life of implanted devices and causes pain, distortion of tissue or even malignant diseases. Therefore, the mechanism of FBR and the modulating factors need to be further investigated to improve the tissue biocompatibility of biomaterials. This article reviewed the process of FBR, summarized the properties and modification of biomaterials related to FBR, and discussed strategies to mitigate this immune reaction.

Objective To observe the value of deep learning(DL)models for automatic classification of echocardiographic views.Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided into training set,validation set and test set at a ratio of 7∶2∶1.ResNet18,ResNet34,Swin Transformer and Swin Transformer V2 models were established based on 2D apical two chamber view,2D apical three chamber view,2D apical four chamber view,2D subcostal view,parasternal long-axis view of left ventricle,short-axis view of great arteries,short-axis view of apex of left ventricle,short-axis view of papillary muscle of left ventricle,short-axis view of mitral valve of left ventricle,also 3D and CDFI views of echocardiography.The accuracy,precision,recall,F1 score and confusion matrix were used to evaluate the performance of each model for automatically classifying echocardiographic views.The interactive interface was designed based on Qt Designer software and deployed on the desktop.Results The performance of models for automatically classifying echocardiographic views in test set were all good,with relatively poor performance for 2D short-axis view of left ventricle and superior performance for 3D and CDFI views.Swin Transformer V2 was the optimal model for automatically classifying echocardiographic views,with high accuracy,precision,recall and F1 score was 92.56％,89.01％,89.97％and 89.31％,respectively,which also had the highest diagonal value in confusion matrix and showed the best classification effect on various views in t-SNE figure.Conclusion DL model had good performance for automatically classifying echocardiographic views,especially Swin Transformer V2 model had the best performance.Using interactive classification interface could improve the interpretability of prediction results to some extent.

Objective:To investigate the value of intratumoral and peritumoral radiomics models based on 18F-FDG PET-CT in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma and interpret peritumoral radiomics features. Methods:This study was a cross-sectional study. Patients with lung adenocarcinoma who underwent 18F-FDG PET-CT at the Third Affiliated Hospital of Soochow University between January 2018 and April 2022 were retrospectively collected and samplied into a training set (309 cases) and a test set (206 cases) in a 6∶4 ratio randomly. Radiomics features were extracted from the intratumoral and peritumoral regions of interest based on PET and CT images, respectively, and the optimal feature sets were selected. Radiomics models were established using the XGBoost algorithm, and radiomics scores (intratumoral CT label, peritumoral CT label, intratumoral PET label, peritumoral PET label) were calculated. Logistic regression analysis was used to construct a clinical model and a combined model (incorporating PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features). The predictive performance of the models was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Unsupervised clustering, Spearman correlation analysis, and visualization methods were used for the interpretability of peritumoral radiomics features. Results:In both the training and test sets, the AUC value of CT peritumoral labels was greater than that of CT intratumoral labels for predicting EGFR mutation status in lung adenocarcinoma (training set: Z=3.84, P<0.001; test set: Z=1.99, P=0.046). In the test set, the AUC value of PET intratumoral labels (0.684) was slightly higher than that of PET peritumoral labels (0.672) for predicting EGFR mutation status, but the difference was not statistically significant ( P>0.05). The combined model had the highest AUC value for predicting EGFR mutation status of lung adenocarcinoma in both the training and test sets and was significantly better than the clinical model (training set: Z=6.52, P<0.001; test set: Z=2.31, P=0.021). Interpretability analysis revealed that CT peritumoral radiomics features were correlated with CT shape features, and there were significant differences in CT peritumoral features between different EGFR mutation statuses. Conclusions:The value of CT peritumoral labels is superior to that of CT intratumoral labels in predicting EGFR mutation status in lung adenocarcinoma. The predictive performance of the model can be improved by combining PET-CT intratumoral and peritumoral radiomics, clinical features, and CT semantic features.