Systemic inflammatory response (SIR) evoked by cardiopulmonary bypass (CPB) is still one of the major causes of postoperative multiple organs injuries. Since the concentrations of circulating inflammatory factors are positively associated with postoperative adverse events, removal or inhibition of inflammatory factors are considered as effective treatments to improve outcomes. After more than 20 years of research, however, the results are disappointed as neither neutralization nor removal of circulating inflammatory factors could reduce adverse events. Therefore, the role of circulating inflammatory factors in CPB-related organs injuries should be reconsidered in order to find effective therapies. Here we reviewed the association between circulating inflammatory factors and the outcomes, as well as the current therapies, including antibody and hemadsorption. Most importantly, the role of circulating inflammatory factors in SIR was reviewed, which may be helpful to develop new measures to prevent and treat CPB-related organs injuries.

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.

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

Objective:To explore the benefits of transradial diagnostic cerebral angiography in elderly patients and its correlation with morphometric parameters of the aortic arch.Methods:Clinical data and aortic arch CTA imaging parameters of patients who underwent cerebral angiography at the Department of Neurosurgery, Beijing Hospital, between May 2022 and April 2023 were retrospectively analyzed.The study aimed to compare the time taken for angiography via radial artery access in elderly patients versus younger patients, as well as via femoral artery access, and to evaluate the associated aortic arch morphology parameters.Results:A total of 101 patients' data were analyzed, with 67 males(66.3%)and an average age of 63.4±12.0 years.Among them, 69 patients(68.3%)were aged 60 and above.The arterial approach for 44 patients(43.6%)was radial, while 57 cases(56.4%)used the femoral artery approach.In the elderly group, 14 cases(20.6%), 31 cases(45.6%), and 23 cases(33.8%)had type Ⅲ aortic arch, respectively.For younger patients, 17 cases(53.1%), 12 cases(37.5%), and 3 cases(9.4%)fell into these categories.The distribution difference was statistically significant( χ2=12.765, P=0.002).Elderly patients had a larger aortic arch width angle compared to younger patients(106°±12°and 100°±12°, t=2.334, P=0.022).The time for whole-brain angiography via radial artery was shorter for elderly patients than via femoral artery(39.8±29.5 minutes and 52.2±28.4 minutes, respectively, t=1.845, P=0.070).In young patients, there was no significant time difference between the two approaches(42.3±30.4 minutes for radial artery and 34.6±11.2 minutes for femoral artery, t=1.026, P=0.313).In the type Ⅱ aortic arch group, the average times for transradial and transfemoral approaches were 38.1±21.7 minutes and 46.7±32.2 minutes, respectively( t=1.020, P=0.314).The average times for the type Ⅲ aortic arch group were 41.9±37.3 minutes and 48.9±20.7 minutes, respectively.Correlation analysis revealed a significant negative correlation between the duration of radial artery access and the distance from the origin of the innominate artery to the left subclavian artery(Pearson correlation coefficien( r=-0.372, P=0.014). Conclusions:In elderly patients, particularly those with type Ⅱ or Ⅲ aortic arch or a wide aortic arch, diagnostic cerebral angiography using transradial access is preferable to femoral access.The distance between the innominate artery and the left subclavian artery origin could impact the duration of the procedure.

Objective To investigate the effect of different radiation doses on the accuracy of iodine quantification and CT value of virtual monochromatic image with a muti-spectral CT iodine phantom using second-generation dual-layer detector spectral CT(DLCT).Methods The second-generation DLCT scan abdominal phantom was used,which consisted of six different concentrations of iodine rods at 4,8 and 12 g/L.The scan parameters were set at 100,120 and 140 kVp tube voltages,and six radiation exposure sequences were scanned at each tube voltage:5,10,15,20,25 and 30 mGy.Measure the iodine concentration value and the CT value at the single level of 50,70 and 100 keV,and calculate the absolute error value of iodine concentration and CT value under different radiation exposure.SPSS 22.0 software single factor analysis of variance was used to compare the difference between the io-dine concentration and the absolute error of CT value under different Radiation exposure,and LSD method was used to compare the two groups.Results The absolute error of CT value of 4 g/L iodine rod with 10～30 mGy scanning at 100 keV energy level was significantly higher than that of 12 g/L and 8 g/L(P＜0.05),and the absolute error of iodine concentration measurement of other iodine rods with different concentrations at different Radiation exposure had no statistical difference.Conclusions The second-generation double-layer detector spectral CT has high meas-urement stability for iodine concentration quantification and CT value accuracy under conditions of radiation dose greater than 5 mGy.

ObjectiveTo explore the risk factors of stroke， and to provide reference for the prevention and health management of stroke. MethodsFrom February 2022 to March 2022， four community residents over 60 years old with stroke in Shanghai were randomly selected as the case group （n=100）， and non-stroke residents were selected as the control group （n=100）. The survey was in the form of questionnaires to record and compare the age， body mass index （BMI）， blood lipids， blood pressure-related indicators， family history of other diseases， living habits， mood and sleep conditions of all subjects. The value of predicting the incidence of stroke among the elderly in Shanghai community was analyzed by receiver operating characteristic （ROC）， and the influencing factors of stroke were analyzed by logistic model. ResultsBMI， hypertension， heart disease， diabetes， transient ischemic attack （TIA）， dyslipidemia， family history of stroke， smoking， lack of exercise or only light physical labor， SBP， DBP， TG levels were significantly higher in the case group （P<0.05）. The level of HDL-C was significantly lower than that in the control group （P<0.05）. BMI， SBP， DBP， TG， HDL-C predicted the incidence of stroke by ROC analysis （P<0.05）. Logistic regression analysis showed that BMI≥23.820 kg·m^-2， heart disease， diabetes， TIA， dyslipidemia， family history of stroke， smoking， lack of exercise or only light physical labor， SBP≥139.535 mmHg， DBP≥89.605 mmHg， TG≥1.565 mmol·L^-1 and HDL-C≤1.105 mmol·L^-1 were risk factors for stroke （P<0.05）. ConclusionPhysical health status including blood lipids and blood pressure， family history of certain diseases， and living habits could be important risk factors for stroke in the elderly in Shanghai community. Preventive intervention measures for the above factors have important clinical significance.