Liver cirrhosis is a common chronic progressive liver disease， and such patients often have coagulation disorders， which may lead to thrombotic and hemorrhagic events. While traditional anticoagulant therapies have various limitations， the emergence of novel oral anticoagulants （NOAC） provides new options for anticoagulation treatment in patients with liver cirrhosis. This article comprehensively reviews the application of NOAC in patients with liver cirrhosis， discusses their advantages and potential risks， analyzes their pharmacokinetic and pharmacodynamic characteristics， and evaluates their efficacy and safety in the prevention and treatment of cirrhosis-associated thrombosis based on clinical evidence， in order to provide a reference for clinical decision-making.

Chimeric antigen receptor T-cell(CAR-T)therapy,as a revolutionary immunotherapy,has achieved significant clinical efficacy in the treatment of hematologic malignancies in recent years.However,its clinical application remains limited by associated toxicities.Bridging therapy,as a supplementary antitumor treatment administered between leukapheresis and CAR-T cell infusion,reduces tumor burden and modulates the immune microenvironment through chemotherapy,radio-therapy,targeted therapy,and immunotherapy.It not only delays disease progression but also sig-nificantly influences the toxicities associated with subsequent CAR-T therapy.This paper reviews the main types of toxicities and management strategies of CAR-T therapy,and analyzes the role of bridging therapy in managing these toxicities,conducting an in-depth analysis of the potential mechanisms by which different bridging therapy modalities influence these toxicities,to provide scientific basis and practical guidance for the clinical application of CAR-T therapy.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective:To utilize single-cell RNA sequencing (scRNA-seq) to untangle the temporal expression profiles of molecules associated with congenital tooth agenesis and dental hard tissue formation during mouse molar development, and to construct a comprehensive cell atlas spanning the entire developmental period from E13.5 to P7.5, thereby providing new insights into the molecular mechanisms underlying abnormal tooth development.Methods:scRNA-seq data of murine mandibular molar tooth germs at five developmental stages (E13.5, E14.5, E16.5, P3.5, P7.5) were obtained from the GEO database (accession: GSE189381). The Seurat pipeline was employed for quality control, data normalization, dimensionality reduction, and Harmony-based batch effect correction. Cellular subpopulations were identified through uniform manifold approximation and projection dimensionality reduction, while developmental trajectories were reconstructed using Monocle for pseudotime analysis.Results:scRNA-seq analysis profiling identified 27 distinct cellular clusters, which were annotated into twelve major cell types including epithelial cells, mesenchymal cells, and endothelial cells. Msx1 exhibited a bimodal expression pattern. Pax9 reached its peak at E14.5 and then gradually decreased. Eda had a low expression level with a diffuse distribution. In contrast, Amelx and Enam were barely expressed during the embryonic stage and were activated at P3.5. Dspp was ectopically highly expressed in epithelial cells from P3.5 to P7.5, while Dmp1 was specifically upregulated in mesenchymal cells at P7.5.Conclusions:The temporal expression patterns of key regulatory genes for tooth agenesis (Msx1, Pax9, Eda), ameloblast differentiation (Amelx, Enam), and odontoblast development (Dspp, Dmp1) during mouse molar development. These findings provide a theoretical foundation and potential therapeutic targets for deciphering the molecular mechanisms underlying tooth agenesis and other developmental dental anomalies, paving the way for targeted clinical interventions.

Objective To investigate the correlation between glycemic variability metrics and the risk of diabetic foot(DF)in patients with type 2 diabetes mellitus(T2DM)utilizing flash glucose monitoring(FGM)tech-nology.Methods A retrospective analysis was conducted on 233 hospitalized patients with T2DM,with or without DF,who were treated in the Department of Endocrinology at Lianyungang First People's Hospital from January 2021 to May 2022 and monitored using FGM.Patients were categorized into a non-DF group(n=147)and a DF group(n=86)based on the presence of DF.The study compared general clinical characteristics,biochemical parameters,and glycemic variability metrics between the two groups and performed subgroup analyses.Binary logistic regression was employed to identify factors associated with the risk of DF,while receiver operating characteristic(ROC)curves were utilized to assess the predictive value of glycemic variability metrics for DF.Results Compared with the non-DF group,patients in the DF group exhibited significantly longer disease duration,higher body mass index(BMI),glycated hemoglobin(HbA1c),urinary albumin-to-creatinine ratio(UACR),alanine aminotransferase(ALT),serum uric acid(SUA),mean amplitude of glycemic excursions(MAGE),coefficient of variation(CV),mean of daily differences(MODD),and mean blood glucose(MBG),but lower fasting C-peptide(FCP),fasting insulin(FINS),high-density lipoprotein cholesterol(HDL-C),and time in range(TIR),with statistically signifi-cant differences(P<0.05).Subgroup analysis revealed that TIR was associated with the incidence of DF and diabetic retinopathy(DR).Binary logistic regression analysis identified HbA1c,MAGE,MODD,and MBG as risk factors for DF,while TIR was a protective factor(P<0.05).ROC curve analysis demonstrated that the area under the curve(AUC)for predicting DF using HbA1c,TIR,MAGE,MODD,MBG,and their combination were 0.646,0.850,0.868,0.764,0.619,and 0.967,respectively,indicating superior performance of the combined prediction model.Conclusions HbA1c,TIR,MAGE,MODD,and MBG are critical factors associated with the development of DF in patients with T2DM.Targeted early interventions aimed at optimizing these glycemic variability indicators may effectively reduce the incidence of DF.

Objective To investigate the correlation between glycemic variability metrics and the risk of diabetic foot(DF)in patients with type 2 diabetes mellitus(T2DM)utilizing flash glucose monitoring(FGM)tech-nology.Methods A retrospective analysis was conducted on 233 hospitalized patients with T2DM,with or without DF,who were treated in the Department of Endocrinology at Lianyungang First People's Hospital from January 2021 to May 2022 and monitored using FGM.Patients were categorized into a non-DF group(n=147)and a DF group(n=86)based on the presence of DF.The study compared general clinical characteristics,biochemical parameters,and glycemic variability metrics between the two groups and performed subgroup analyses.Binary logistic regression was employed to identify factors associated with the risk of DF,while receiver operating characteristic(ROC)curves were utilized to assess the predictive value of glycemic variability metrics for DF.Results Compared with the non-DF group,patients in the DF group exhibited significantly longer disease duration,higher body mass index(BMI),glycated hemoglobin(HbA1c),urinary albumin-to-creatinine ratio(UACR),alanine aminotransferase(ALT),serum uric acid(SUA),mean amplitude of glycemic excursions(MAGE),coefficient of variation(CV),mean of daily differences(MODD),and mean blood glucose(MBG),but lower fasting C-peptide(FCP),fasting insulin(FINS),high-density lipoprotein cholesterol(HDL-C),and time in range(TIR),with statistically signifi-cant differences(P<0.05).Subgroup analysis revealed that TIR was associated with the incidence of DF and diabetic retinopathy(DR).Binary logistic regression analysis identified HbA1c,MAGE,MODD,and MBG as risk factors for DF,while TIR was a protective factor(P<0.05).ROC curve analysis demonstrated that the area under the curve(AUC)for predicting DF using HbA1c,TIR,MAGE,MODD,MBG,and their combination were 0.646,0.850,0.868,0.764,0.619,and 0.967,respectively,indicating superior performance of the combined prediction model.Conclusions HbA1c,TIR,MAGE,MODD,and MBG are critical factors associated with the development of DF in patients with T2DM.Targeted early interventions aimed at optimizing these glycemic variability indicators may effectively reduce the incidence of DF.

Chimeric antigen receptor T-cell(CAR-T)therapy,as a revolutionary immunotherapy,has achieved significant clinical efficacy in the treatment of hematologic malignancies in recent years.However,its clinical application remains limited by associated toxicities.Bridging therapy,as a supplementary antitumor treatment administered between leukapheresis and CAR-T cell infusion,reduces tumor burden and modulates the immune microenvironment through chemotherapy,radio-therapy,targeted therapy,and immunotherapy.It not only delays disease progression but also sig-nificantly influences the toxicities associated with subsequent CAR-T therapy.This paper reviews the main types of toxicities and management strategies of CAR-T therapy,and analyzes the role of bridging therapy in managing these toxicities,conducting an in-depth analysis of the potential mechanisms by which different bridging therapy modalities influence these toxicities,to provide scientific basis and practical guidance for the clinical application of CAR-T therapy.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective:To utilize single-cell RNA sequencing (scRNA-seq) to untangle the temporal expression profiles of molecules associated with congenital tooth agenesis and dental hard tissue formation during mouse molar development, and to construct a comprehensive cell atlas spanning the entire developmental period from E13.5 to P7.5, thereby providing new insights into the molecular mechanisms underlying abnormal tooth development.Methods:scRNA-seq data of murine mandibular molar tooth germs at five developmental stages (E13.5, E14.5, E16.5, P3.5, P7.5) were obtained from the GEO database (accession: GSE189381). The Seurat pipeline was employed for quality control, data normalization, dimensionality reduction, and Harmony-based batch effect correction. Cellular subpopulations were identified through uniform manifold approximation and projection dimensionality reduction, while developmental trajectories were reconstructed using Monocle for pseudotime analysis.Results:scRNA-seq analysis profiling identified 27 distinct cellular clusters, which were annotated into twelve major cell types including epithelial cells, mesenchymal cells, and endothelial cells. Msx1 exhibited a bimodal expression pattern. Pax9 reached its peak at E14.5 and then gradually decreased. Eda had a low expression level with a diffuse distribution. In contrast, Amelx and Enam were barely expressed during the embryonic stage and were activated at P3.5. Dspp was ectopically highly expressed in epithelial cells from P3.5 to P7.5, while Dmp1 was specifically upregulated in mesenchymal cells at P7.5.Conclusions:The temporal expression patterns of key regulatory genes for tooth agenesis (Msx1, Pax9, Eda), ameloblast differentiation (Amelx, Enam), and odontoblast development (Dspp, Dmp1) during mouse molar development. These findings provide a theoretical foundation and potential therapeutic targets for deciphering the molecular mechanisms underlying tooth agenesis and other developmental dental anomalies, paving the way for targeted clinical interventions.