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.

Objective: To investigate the seroepidemiological characteristics of hepatitis B among outpatients in medical institutions in Jiaxing City, Zhejiang Province, so as to provide a reference for formulating region-specific hepatitis B prevention and control strategies. Methods: From April to June 2024, outpatients were selected as study subjects from sentinel medical institutions in Jiaxing City. Information such as gender and age was collected. Venous blood samples were obtained and serological markers including hepatitis B surface antigen (HBsAg), hepatitis B surface antibody (HBsAb), hepatitis B e antigen (HBeAg), hepatitis B e antibody (HBeAb), and hepatitis B core antibody (HBcAb) were tested. Positive rates of hepatitis B virus (HBV) serological markers were analyzed by genders and ages. Results: A total of 1 468 outpatients were included, among whom 721 were males (49.11%) and 747 were females (50.89%). The mean age was (46.41±19.66) years. The positive rates of HBsAg, HBsAb, HBeAg, HBeAb, and HBcAb were 7.29%, 44.75%, 1.84%, 23.50%, and 42.03%, respectively. The HBcAb positive rate in males was significantly higher than in females (46.05% vs. 38.15%, P<0.05), while no statistically significant gender differences were observed in the positive rates of other four HBV serological markers (all P>0.05). Except for HBsAb, the positive rates of the other four HBV serological markers showed statistically significant differences across different age groups (all P<0.05). Pairwise comparisons results showed that the HBsAg positive rates in age groups of 20-<40 years and 40-<60 years were 9.48% and 9.57%, respectively, which were higher than those in age groups of <20 years (1.43%) and ≥60 years (2.75%) (all P<0.05). A total of 17 HBV serological marker patterns were observed, among which the proportion of all markers negative was the highest, at 39.65%. The proportions of "small three positive" (HBsAg+, HBeAb+, HBcAb+) and "large three positive" (HBsAg+, HBeAg+, HBcAb+) patterns were 4.77% and 1.50%, respectively. Among HBsAg-positive individuals, the proportions of the "small three positive" pattern across age groups were 0, 45.45%, 90.00%, and 81.82%, while those of the "large three positive" were 0, 36.36%, 5.00%, and 0, with statistically significant differences across age groups (both P<0.05). Conclusions The positive rate of HBsAg among outpatients in medical institutions in Jiaxing City is relatively high, with a notable proportion of individuals showing either no immunity or non-response to vaccination. It is recommended to strengthen hepatitis B immunization efforts among the population aged 20-<60 years, and to enhance monitoring and interventional treatment for "small three positive" and "large three positive" patterns.

Establishing a natural population prospective cohort represents a sophisticated systematic project that spans multiple disciplines and research directions, requiring comprehensive consideration of diverse factors. Both international and domestic practices have accumulated substantial experience and achievements in this field. These prospective cohorts are typically developed through collaborative efforts among research institutions, generating systematic insights into construction backgrounds, objectives, design frameworks, implementation strategies, and management protocols. This study conducts an in-depth investigation into the current status of natural population prospective cohort development globally and domestically, systematically summarizing progress in development history, current status, and research advancements. The findings aim to inform strategic planning in China’s related domains, thereby facilitating the advancement of natural population prospective cohort research.

Objective: To explore the characteristic of obesity in adolescents with major depressive disorder and its relationship with inflammatory cytokines. Methods : One hundred and forty adolescents with major depressive disorder were enrolled. According to the classification standard of body mass index(BMI) for adolescents in China, the patients were classified into underweight group, normal group, overweight group and obese group. The center for epidemiologic studies depression scale(CES-D) was used to evaluate symptoms of depression in patients, and ultrasensitive multiplex electrochemiluminescence detection technology was used to measure the levels of plasma inflammatory cytokines including interleukin(IL)-6,IL-17A,IL-1β,IL-6,IL-8 and tumour necrosis factor(TNF)-α. One-way ANOVA or Kruskal-WallisHtest and chi-square test were used for comparison between groups. Binary Logistic regression was used to analyze the influencing factors of obesity in adolescent patients with major depressive disorder. Results : Among the 140 adolescent patients with major depressive disorder, wasting were 9.3%(13/140), overweight were 17.9%(25/140) and obesity were 6.4%(9/140) respectively. There were statistically significant differences in gender(χ2=8.301,P<0.05) and inflammatory cytokines IL-6(H=16.217,P<0.01), IL-8(H=10.926,P<0.05) and TNF-α(H=7.879,P<0.05) among the four groups. Analysis of covariance showed that the difference in levels of the inflammatory cytokine IL-6(F=4.486,P<0.01) remained statistically significant after controlling for age, gender and antidepressant use. The results of multiple comparisons showed that compared with the wasting group, the plasma IL-6(Z=-3.843,PBonferroni calibrate<0.01) were higher in the obese group; compared with the normal group, the obesity rate of males was higher than that of females(χ2=8.812,PBonferroni calibrate<0.01), and the level of IL-6 in the obese group(Z=-3.023,PBonferroni calibrate<0.05) was higher. Binary Logistic regression analysis showed that plasma IL-6(OR=2.500,P<0.01) and gender(OR=11.292,P<0.01) were independent influencing factors for obesity in patients with adolescent depressive disorders. Conclusion There are gender differences in obesity rates in adolescents with depressive disorders, and obesity is associated with elevated levels of inflammatory cytokines.