Depression is a psychological disorder that imposes a tremendous social burden, with a high prevalence in China. In recent years, the number of diagnosed cases of depression in China has steadily increased, yet the recognition rate of the disease remains low. Depression demostrates a clear familial aggregation pattern. To improve its recognition, numberous studies have utilized the polygenic risk score for major depressive disorder (MDD-PRS) as a potential genetic marker to identify high-risk populations. This article reviews the latest progress and research on the use of MDD-PRS in depression, aiming to clarify its capacity to capture genetic variations associated with depression, its interaction with environmental factors, and their relationship to the onset of the disorder. Additionally, this review evaluates the predictive performance of existing risk models for depression and proposes potential directions for future research.

Depression is a psychological disorder that imposes a tremendous social burden, with a high prevalence in China. In recent years, the number of diagnosed cases of depression in China has steadily increased, yet the recognition rate of the disease remains low. Depression demostrates a clear familial aggregation pattern. To improve its recognition, numberous studies have utilized the polygenic risk score for major depressive disorder (MDD-PRS) as a potential genetic marker to identify high-risk populations. This article reviews the latest progress and research on the use of MDD-PRS in depression, aiming to clarify its capacity to capture genetic variations associated with depression, its interaction with environmental factors, and their relationship to the onset of the disorder. Additionally, this review evaluates the predictive performance of existing risk models for depression and proposes potential directions for future research.

Objective To observe the value of nomogram based on enhanced cortical phase CT Radscore combined with CT features for predicting synchronous distant metastasis(SDM)of renal cell carcinoma(RCC).Methods Totally 139 RCC patients from center A were retrospectively enrolled and divided into SDM group(n=46)and non-SDM group(n=93),also classified as training set(n=97)and test set(n=42)at a ratio of 7∶3.Additionally,20 RCC patients from center B were included as validation set(8 cases with SDM and 12 cases without SDM).Radiomics features were extracted and screened based on enhanced cortical phase CT images to calculate Radscore.Multivariate logistic regression analysis was performed to identify independent predictors of RCC SDM among clinical and CT features.Then a logistic regression model was constructed combining Radscore and independent predictors of RCC SDM and visualized as a nomogram.The receiver operating characteristic curve and the area under the curve(AUC)was used to assess the efficacy of the nomogram for predicting RCC SDM.Results The maximum tumor diameter,CT-T stage and perirenal adipose stranding were all independent predictors of RCC SDM(all P＜0.01).Radscore was calculated based on 5 optimal features.The nomogram was constructed based on perirenal adipose stranding,CT-T stage and Radscore.AUC of the model for predicting RCC SDM in training set,test set and validation set was 0.964,0.921 and 0.885,respectively.Conclusion Enhanced cortical phase CT Radscore combined with perirenal adipose stranding and CT-T stage could effectively predict RCC SDM.

Objective To observe the value of nomogram based on enhanced cortical phase CT Radscore combined with CT features for predicting synchronous distant metastasis(SDM)of renal cell carcinoma(RCC).Methods Totally 139 RCC patients from center A were retrospectively enrolled and divided into SDM group(n=46)and non-SDM group(n=93),also classified as training set(n=97)and test set(n=42)at a ratio of 7∶3.Additionally,20 RCC patients from center B were included as validation set(8 cases with SDM and 12 cases without SDM).Radiomics features were extracted and screened based on enhanced cortical phase CT images to calculate Radscore.Multivariate logistic regression analysis was performed to identify independent predictors of RCC SDM among clinical and CT features.Then a logistic regression model was constructed combining Radscore and independent predictors of RCC SDM and visualized as a nomogram.The receiver operating characteristic curve and the area under the curve(AUC)was used to assess the efficacy of the nomogram for predicting RCC SDM.Results The maximum tumor diameter,CT-T stage and perirenal adipose stranding were all independent predictors of RCC SDM(all P＜0.01).Radscore was calculated based on 5 optimal features.The nomogram was constructed based on perirenal adipose stranding,CT-T stage and Radscore.AUC of the model for predicting RCC SDM in training set,test set and validation set was 0.964,0.921 and 0.885,respectively.Conclusion Enhanced cortical phase CT Radscore combined with perirenal adipose stranding and CT-T stage could effectively predict RCC SDM.