Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective:To construct a machine-learning model for predicting the progression of pancreatic cystic lesions (PCLs) based on clinical and CT features, and to evaluate its predictive performance in internal/external testing cohorts.Methods:Baseline clinical and radiological data of 200 PCLs in 177 patients undergoing abdominal thin slice enhanced CT examination at Peking Union Medical College Hospital from July 2014 to December 2022 were retrospectively collected. PCLs were divided into progressive and non-progressive groups according to whether the signs indicated for surgery by the guidelines of the European study group on PCLs were present during three-year follow-up. 200 PCLs were randomly divided into training (150 PCLs) and internal testing cohorts (50 PCLs) at the ratio of 1∶3. 15 PCLs in 14 patients at Jinling Affiliated Hospital of Medical School of Nanjing University from October 2011 to May 2020 were enrolled as external testing cohort. The clinical and CT radiological features were recorded. Multiple feature selection methods and machine-learning models were implemented and combined to identify the optimal machine-learning model based on the 10-fold cross-validation method. Receiver operating characteristics (ROC) curve was drawn and area under curve (AUC) was calculated. The model with the highest AUC was determined as the optimal model. The optimal model's predictive performance was evaluated on testing cohort by calculating AUC, sensitivity, specificity and accuracy. Permutation importance was used to assess the importance of optimal model features. Calibration curves of the optimal model were established to evaluate the model's clinical applicability by Hosmer-Lemeshow test.Results:In training and internal testing cohorts, the progressive and non-progressive groups were significantly different on history of pancreatitis, lesions size, main pancreatic duct diameter and dilation, thick cyst wall, presence of septation and thick septation (all P value <0.05) In internal testing cohort, the two groups were significantly different on gender, lesion calcification and pancreatic atrophy (all P value <0.05). In external testing cohort, the two groups were significantly different on lesions size and pancreatic duct dilation (both P<0.05). The support vector machine (SVM) model based on five features selected by F test (lesion size, thick cyst wall, history of pancreatitis, main pancreatic duct diameter and dilation) achieved the highest AUC of 0.899 during cross-validation. SVM model for predicting the progression of PCLs demonstrated an AUC of 0.909, sensitivity of 82.4%, specificity of 72.7%, and accuracy of 76.0% in the internal testing cohort, and 0.944, 100%, 77.8%, and 86.7% in the external testing cohort. Calibration curved showed that the predicted probability by the model was comparable to the real progression of PCLs. Hosmer-Lemeshow goodness-of-fit test affirmed the model's consistency with actual PCLs progression in testing cohorts. Conclusions:The SVM model based on clinical and CT features can help doctors predict the PCLs progression within three-year follow-up, thus achieving efficient patient management and rational allocation of medical resource.

Objective To investigate the effect of asiaticoside on blood pressure and relaxation of thoracic aorta in rats and explore the underlying mechanism.Methods SD rats treated with 50 and 100 mg/kg asiaticoside by daily gavage for 2 weeks were monitored for systolic blood pressure changes,and histological changes of the thoracic aorta were evaluated using HE staining.In isolated rat endothelium-intact and endothelium-denuded thoracic aorta rings,the effects of asiaticoside on relaxation of the aortic rings were tested at baseline and following norepinephrine(NE)-and KCl-induced constriction.The vascular relaxation effect of asiaticoside was further observed in NE-stimulated endothelium-intact rat aortic rings pretreated with L-nitroarginine methyl ester,indomethacin,zinc protoporphyrin Ⅸ,tetraethyl ammonium chloride,glibenclamide,barium chloride,Iberiotoxin,4-aminopyridine,or TASK-1-IN-1.The aortic rings were treated with KCl and NE followed by increasing concentrations of CaCl2 to investigate the effect of asiaticoside on vasoconstriction induced by external calcium influx and internal calcium release.Results Asiaticoside at 50 and 100 mg/kg significantly lowered systolic blood pressure in rats without affecting the thoracic aorta histomorphology.While not obviously affecting resting aortic rings with intact endothelium,asiaticoside at 100 mg/kg induced significant relaxation of the rings constricted by KCl and NE,but its effects differed between endothelium-intact and endothelium-denuded rings.In endothelium-intact aortic rings pretreated with indomethacin,ZnPP Ⅸ,barium chloride,glyburide,TASK-1-IN-1 and 4-aminopyridine,asiaticoside did not produce significant effect on NE-induced vasoconstriction,and tetraethylammonium,Iberiotoxin and L-nitroarginine methyl ester all inhibited the relaxation effect of asiaticoside.In KCl-and NE-treated rings,asiaticoside obviously inhibited CaCl2-induced vascular contraction.Conclusion Asiaticoside induces thoracic aorta relaxation by mediating high-conductance calcium-activated potassium channel opening,promoting nitric oxide release from endothelial cells and regulating Ca2+ influx and outflow,thereby reducing systolic blood pressure in rats.

Objective To investigate whether activation of mitochondrial acetal dehydrogenase 2(ALDH2)alleviates hypoxic pulmonary hypertension by regulating the SIRT1/PGC-1α signaling pathway.Methods Thirty 8-week-old C57 BL/6 mice were randomized into control,hypoxia,and hypoxia+Alda-1(an ALDH2 activator)group(n=10),and the mice in the latter two groups,along with 10 ALDH2 knockout(ALDH2-/-)mice,were exposed to hypoxia(10%O2,90%N2)with or without daily intraperitoneal injection of Alda-1 for 4 weeks.The changes in right ventricular function and pressure(RVSP)of the mice were evaluated by echocardiography and right ventricular catheter test,and pulmonary artery pressure was estimated based on RVSP.Pulmonary vascular remodeling,right ventricular injury,myocardial α-SMA expression,distal pulmonary arteriole muscle normalization,right ventricular cross-sectional area,myocardial cell hypertrophy,and right cardiac hypertrophy index were assessed with HE staining,immunofluorescence staining and WGA staining,and the expressions of ALDH2,SIRT1,PGC-1α,P16INK4A and P21CIP1 were detected.In pulmonary artery smooth muscle cells with hypoxic exposure,the effect of Alda-1 and EX527 on cell senescence and protein expressions was evaluated using β-galactose staining and Western blotting.Results The wild-type mice with hypoxic exposure showed significantly increased RVSP,right ventricular free wall thickness and myocardial expressions of P16INK4A and P21CIP1,which were effectively lowered by treatment with Alda-1 but further increased in ALDH2-/-mice.In cultured pulmonary artery smooth muscle cells,hypoxic exposure significantly increased senescent cell percentage and cellular expressions of P16INK4A and P21CIP1,which were all lowered by treatment with Alda-1,but its effect was obviously attenuated by EX527 treatment.Conclusion ALDH2 alleviates hypoxia-induced senescence of pulmonary artery smooth muscle cells by upregulating the SIRT1/PGC-1α signaling pathway to alleviate pulmonary hypertension in mice.

Objective:A gas chromatography analysis system for fatty acid compositions of hydrogenated palm glycerides was established to provide a reference for quality standards of hydrogenated palm glycerides.Methods:In this test,a DB-WAX(30 m ×0.53 mm,1.0 μm)strong polarity gas chromatographic column was employed with an injection volume of 1 μL and a split ratio of 120∶1.The initial column temperature was 70℃,sustained for 2 min,ramped up to 230 ℃ at a rate of 5 ℃ per minute,and maintained for 10 min,with the inlet temperature of 250 ℃ and the detector temperature of 250 ℃.Results:In this experiment,the gas chromatograph-ic method was established successfully to determine fatty acid compositions of hydrogenated palm glycerides.The validated results indicated that the method exhibited well-suited system applicability,specificity and precision,and the RSD(％)fulfilled the requirements.The limit of detection(LOD)and limit of quantification(LOQ)values of nine fatty acid methyl esters were defined,and fatty acid compositions of seven batches of samples were checked successfully.Conclusion:The GC methodology developed in this study is suitable for the analysis and determina-tion of fatty acid compositions in hydrogenated palm glycerides,which can serve as a reference for the quality control of this pharmaceutical excipient and the establishment of quality standards.

To establish a medical imaging teacher competency model and evaluate its application value in group teaching for undergraduates.