Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Depressive disorder is a kind of mental disorder characterized by persistent and significant depressed mood, with complex etiology and high recurrence rate. At present, more precise and effective diagnostic and therapeutic approaches are still required. Increasing evidence suggests that hypoxia inducible factor-1 (HIF-1) and related pathways are involved in regulating the development and recovery of depression. HIF-1 enhances neuroplasticity, mitigates neuroinflammatory responses, alleviates oxidative stress, and modulates brain energy metabolism by influencing specific molecules associated with depression. This paper reviews pertinent domestic and international studies, examine the potential mechanisms of HIF-1 in the pathogenesis and progression of depression, and explore antidepressant treatment strategies targeting the HIF-1 signaling pathway. This article provides novel insights into elucidating the pathogenesis of depression and developing innovative therapeutic approaches.

Objective:To study the mechanism of arsenic trioxide(ATO)combined with metformin(MET)in promoting apoptosis of leukemia cells.Methods:CCK-8 method was used to detect the viability of leukemia cell line KG1a,K562,and THP1 cells treated by ATO monotherapy,MET monotherapy,and ATO combined with MET.Flow cytometry was used to detect cell cycle and apoptosis.RT-qPCR was used to detect the mRNA expression of PI3K/Akt and LKB1/AMPK pathway-related genes.Western blot was used to detect the expression of PI3K/Akt and LKB1/AMPK pathway-related proteins and autophagy-related protein LC3B and P62.Results:Compared with the ATO monotherapy group,ATO combined with MET significantly inhibited the growth of KG1a,K562 and THP1 cells,and the difference in KG1a cells was more statistically significant.The combination of the two drugs induced KG1a cell cycle arrest,promoted apoptosis,increased the expression of autophagy-related protein LC3B and P62,up-regulated the mRNA expression levels of PI3K/Akt pathway and LKB1/AMPK pathway-related genes,as well as the expression of LKB1/AMPK pathway-related proteins,and down-regulated the expression of PI3K/Akt pathway-related proteins.Conclusion:ATO combined with MET promotes apoptosis by up-regulating LKB1/AMPK and down-regulating PI3K/Akt signaling pathway to regulate the autophagy of leukemia cells.

Objective To investigate the effects of TEK receptor tyrosine kinase(Tie2)L914F mutation on the biological behavior of vascular endothelial cells and the changes of related signaling pathways in patients with venous malformations.Methods Gene sequen-cing was used to detect Tie2-L914F mutations in venous malformations.HE staining and immunohistochemical staining were used to de-tect the expression of platelet derived growth factor subunit B gene(PDGFB)and α-smooth muscle actin(α-SMA)in venous malfor-mations caused by the mutation.Lentivirus overexpressing Tie2-wild type(Tie2-WT),Tie2-L914F and Tie2-GFP(green fluorescent protein,GFP)infected human umbilical vein endothelial cells(HUVECs).Real-time fluorescence quantitative PCR was used to detect the expression level of Tie2 in endothelial cells expressing exogenous Tie2 and Flag-tagged protein Tie2 protein was detected by western blotting to verify transfection efficiency.The proliferation,apoptosis,migration and tube-forming ability of the cells were determined by CCK-8,flow cytometry,Transwell migration assay and Matrigel matrix gel tube-forming assay.Western blotting was used to detect the expression levels of Protein Kinase B(PKB/AKT),FOXO1 and their phosphorylation,and the expression level of PDGFB was detec-ted by ELISA.Results The number of patients with venous malformations with L914F mutations was about 33.3％.HE and immuno-histochemical staining showed that the expressions of PDGFB and α-SMA were significantly down-regulated in venous malformation tis-sues with Tie2-L914F mutation,and were positively correlated with the decrease in cell coverage of the tube wall.Compared with Tie2-WT endothelial cells,the apoptosis number of Tie2-L914F endothelial cells was significantly reduced,while the proliferation and mi-gration ability was significantly increased,and the tube-forming ability was significantly decreased.Western blotting and ELISA showed that the phosphorylation levels of AKT and FOXO1 downstream of Tie2 signaling pathway in endothelial cells expressing Tie2-L914F were significantly increased,and the expression level of PDGFB was significantly decreased.Conclusion In venous malformations,Tie2-L914F mutation may downregulate the expression of PDGFB through AKT signaling pathway,which affects the biological behavior of vascular endothelial cells.

Clinical teaching managers are the designers, implementers, and supervisors of clinical medical education. Their competence level directly affects the quality of hospital teaching management and clinical medical education. The competence evaluation systems for medical education managers in countries such as the United States and the United Kingdom are well-established, which provides a reference for the competence evaluation of clinical teaching managers in China. This research systematically reviews the construction process and current situation of the competence evaluation systems for medical education managers in the world, and summarizes the basis, methods, and dimensions of competence evaluation. According to the actual situation of clinical teaching management, suggestions were put forward, including developing systematic scientific evaluation tools, carrying out competence-oriented training and assessment, focusing on student-centered education, and creating a career path of sustainable development.

Objective To develop and validate a transgenic mouse model enabling specific and inducible optogenetic activation of oligodendrocyte precursor cells(OPCs).Methods A conditional allele for the photosensitive opsin chicken opsin 5(cOpn5)(Rosa26-LSL-cOpn5)was generated using CRISPR/Cas9 technology.These mice were subsequently crossed with NG2-CreERT transgenic mice to produce NG2-CreERT;cOpn5 animals.In this model,tamoxifen administration induces Cre-mediated recombination,leading to specific expression of cOpn5 in NG2-positive OPCs.The specificity and efficiency of cOpn5 expression in OPCs were confirmed by immunofluorescent staining.Functional validation of light-induced OPC activation was performed by using calcium imaging in acute brain slices after stimulation with 470 nm blue light.Results Immunofluorescence analysis confirmed robust and specific expression of cOpn5 within NG2-positive OPCs in the brains of tamoxifen-treated NG2-CreERT;cOpn5 mice.Crucially,calcium imaging of acute brain slices from these mice demonstrated a significant increase in intracellular calcium levels in cOpn5-expressing OPCs upon stimulation with 470 nm blue light,indicating successful optogenetic activation.Conclusion We have successfully generated and validated a novel transgenic mouse model(NG2-CreERT;cOpn5)that permits specific and inducible optogenetic activation of OPCs.This model provides a novel tool for subsequent in vivo studies of the role and regulating mechanisms of OPCs in the central nervous system.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the dose-effect relationship between sedentary time and sarcopenia in maintenance hemodialysis (MHD) patients, and to inform decision-making to improve muscle health in MHD patients.Methods:A retrospective cohort study was conducted. MHD patients undergoing dialysis in Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2021 to December 2023 were selected by convenience sampling method. Data on general information, prevalence of sarcopenia and sedentary time, etc. were collected. Based on the quartile of sedentary time, MHD patients were categorized into group Q1 (sedentary time<3.40 h), group Q2 (3.40 h≤sedentary time<5.20 h), group Q3 (5.20 h≤sedentary time<9.33 h) and group Q4 (sedentary time≥9.33 h). Restricted cubic spline plots were used to analyze the dose-effect relationship between sedentary time and the risk of sarcopenia. Logistic regression was used to analyze the relationship between sedentary time and the risk of sarcopenia with trend analysis. Subgroups were grouped according to age, gender, and age on dialysis, and subgroup analyses were performed using the interaction test.Results:A total of 576 MHD patients were enrolled, 272 males and 304 females, age (59.69 ± 11.38) years, the sedentary time was 5.20 (3.40, 9.33) h, and the prevalence of sarcopenia was 31.60% (182/576). Restricted cubic spline plots analysis showed that there was a trend of linear association between sedentary time and the risk of sarcopenia ( Pnon-linear=0.226), with a positive correlation ( Poverall<0.01). Logistic analysis showed that when correcting for all confounding factors, compared with group Q1, the risk of sarcopenia increased 1.557 times in group Q3 ( OR=2.557, 95% CI 1.255-5.334, P<0.01) and 7.721 times in group Q4 ( OR=8.721, 95% CI 4.328-18.323, P<0.01). And the OR values of sarcopenia in sedentary time of group Q1, Q2, Q3, and Q4 showed an increasing trend ( Ptrend<0.01). Subgroup analysis showed that the relationship between the risk of sarcopenia and sedentary time was basically the same in MHD patients of different age, gender, and dialysis age subgroups ( OR values were 1.807-3.090, all P<0.05), and there was no interaction between sedentary time and age, gender, and dialysis age (all Pinteraction>0.05). Conclusions:The longer the sedentary time, the higher the risk of sarcopenia in MHD patients. And the risk of sarcopenia was higher for sedentary time ≥ 5.20 h. Medical staffs should encourage MHD patients to improve their living habits, especially those who sit for more than 5.20 h a day, to prevent sarcopenia.