Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

The adverse effects of obesity on male reproductive function are mainly manifested as the abnormal development of the reproductive system, decrease of testosterone level, decline of sperm quality, and impact on the health of offspring, while its regulatory mechanism is far from being clarified. This paper expounds the influence of obesity on the male reproductive system in the aspects of population epidemiology and animal experiments, presents an overview on the action mechanisms of obesity from the perspectives of the hypothalamus-pituitary-testis axis, blood-testis barrier, inflammatory reaction, oxidative stress, testicular germ cell apoptosis, and impact of paternal obesity on the health of offspring, aiming to shed some light on the clinical treatment and prevention of obesity-related male reproductive dysfunction.
Male ; Obesity/physiopathology* ; Humans ; Animals ; Hypothalamo-Hypophyseal System ; Testis/physiopathology* ; Oxidative Stress ; Infertility, Male/etiology* ; Reproduction ; Blood-Testis Barrier

OBJECTIVE To evaluate the effects of different functional dyspepsia(FD)modeling methods and explore the thera-peutic effect and potential mechanism of Banxia Xiexin Decoction on FD.METHODS BALB/c mice were randomly divided into the blank group,iodoacetamide group,loperamide group,tail clamp group and vinegar group.After 1 week of intervention,the status of mice in each group was observed and their gastrointestinal motility,hormone levels and pathological changes were detected.A more i-deal FD modeling method was evaluated and determined.After modeling,different doses of Banxia Xiexin Decoction were given to in-tervene,and the changes in the gastrointestinal function of mice were observed.The expression of related proteins was studied by im-munohistochemistry,ELISA,Western Blot and other experimental methods.RESULTS Comparing the four modeling methods,it was found that the mice in the iodoacetamide group,loperamide group,and vinegar group showed weight loss compared to the blank group;the gastric emptying rate and small intestinal propulsion rate of mice in the iodoacetamide group and vinegar group decreased;changes in gastrointestinal hormones were found in the serum of mice in the tail clip group and vinegar group.Finally,the iodoacetamide meth-od was evaluated as the optimal FD modeling method.The administration results showed that Banxia Xiexin Decoction had no signifi-cant effect on the food intake and body weight of FD mice,while medium and high doses could improve the physical condition of FD mice,increase their gastric emptying rate and small intestine propulsion rate.The experimental results of immunohistochemistry,West-ern blotting,and ELISA confirmed that medium and high doses of Banxia Xiexin Decoction can significantly reduce the expression lev-els of TNF-α and IL-6 in the duodenum and serum of FD mice.CONCLUSION The iodoacetamide method is a better FD modeling method.Banxia Xiexin Decoction can improve the condition of FD mice,increase gastrointestinal motility,reduce the secretion of in-flammatory factor,thereby achieving the therapeutic effect of treating FD.

Objective:To explore the characteristic of dynamic function network connectivity (dFNC) of resting brain networks in internet gaming disorder (IGD) adolescents.Methods:Forty-four adolescent IGD subjects (IGD group, male/female: 38/6) and fifty healthy controls (HC group, male/female: 40/10) were collected, and the subjects completed demographic questionnaires, Young internet addiction scale(YIAS), Chinese adolescents' maladaptive cognitions scale(CAMAS), and functional magnetic resonance imaging (fMRI) tests. The fMRI data were preprocessed on the Matlab platform, and the preprocessed data was divided into 64 components for group level independent component analysis.The dynamic functional connectivity of obtained 18 effective independent components was analyzed by sliding time window technique, and the difference of dynamic functional connectivity of brain triple network between the IGD group and HC group was compared using SPSS 22.0 software.Results:Four repeated dFNC states were identified through cluster analysis.Each state indicated that different functional networks had different connection strengths.State 3, the most frequent state, had been indicated that the whole brain network of the subject was in a state of weak functional connectivity.The second frequent state was state 1, which indicated enhanced functional connectivity within the subject's central executive network (CEN).State 2 had been indicated enhanced functional connectivity within the subject's salience network (SN).State 4 had been indicated generally enhanced functional connectivity in the subjects' brain networks, and this state was the least frequent.The results of non-parametric permutation test on the time attribute showed that compared with the HC group, the IGD group had a longer time score (IGD group: 0.24±0.19, HC group: 0.13±0.15, t=1.19, P<0.05, non-parametric substitution test) for state 1 with strong connectivity within the CEN, which was positively correlated with the YIAS score and game time ( r=0.418, P=0.003; r=0.515, P=0.004).Compared with HC group, the functional connectivity of ICD group between the internal insula of the SN and the dorsal anterior cingulate cortex was enhanced ( P<0.05, FDR corrected), while the average residence time in weakly connected state 3 was longer ( Z=2.09, P<0.05, nonparametric substitution test). Conclusions:The difference in dynamic functional connectivity of the triple network in the brain of IGD adolescents under resting state is mainly manifested by strong connections in CEN, functional connections between insula and dorsal anterior cingulate cortex in SN is enhanced, and weakening of overall functional connections, which may play an important role in the pathological mechanism of IGD.

Objective To construct a survival prediction model for the patients with extramedullary plasmacytoma (EMP),and to evaluate its application value.Methods The data of the patients diagnosed as EMP from 2000 to 2019 were collected from the National Cancer Institute's Surveillance,Epidemiology,and End Results (SEER) database.Random forest algorithm was used to screen variables,multivariate Cox pro-portional hazards regression model was used to determine the independent risk factors of EMP prognosis,and a nomogram model was constructed based on these factors.The total score of the patients was calculated ac-cording to the nomogram model,and the total samples were divided into the low,medium and high risk groups according to the optimal cutoff value by using X-tile software,and the Kaplan-Meier survival curves of the three groups were analyzed.The predictive performance of the model was evaluated using area under the curve (AUC) of time-dependent receiver operating characteristic (t-ROC) curve,AUC after X years N repeated K-fold cross-validation,calibration curve,and decision curve analysis (DCA).Results A total of 1458 patients with EMP were included,including 796 cases of deletions and 662 cases of death.In the deletion and deaths groups,the majority were aged 60-<75 years old (35.4％,41.2％),male (63.1％,66.3％),white race peo-ple (81.3％,80.4％),and married (68.2％,59.4％).Finally,age (45-<60 years old/60-<75 years old/≥ 75 years old),marital status (single/others),non-primary malignant tumor,and without surgery and radio-therapy were determined to be the independent risk factors for the survival of EPM patients (P<0.05).Based on the above independent risk factors,a survival prediction model was constructed,and a nomogram was drawn.According to the optimal cutoff value,the total sample was divided into the score of 0-96 group (low risk group),score of 98-135 group (medium risk group) and score of 139-191 group (high risk group).The Kaplan-Meier survival analysis revealed that there was statistically significant difference in the survival time a-mong the three groups (P<0.0001).AUC of t-ROC curves for 1,3,5 years and AUC after cross-validation all were>0.7,indicating that the model had a good degree of differentiation.The calibration curves suggested a good consistency between the prediction and practical over survival rate,and DCA indicated that the model could improve the clinical benefits.Conclusion The constructed survival prediction model for EMP patients based on the SEER database could help the clinicians to identify the prognostic risk factors and predict the o-verall survival rate of the patients.

Objective:To evaluate the diagnostic performance of the Prostate Imaging Reporting and Data System version 2.1(PI-RADS v2.1)for clinically significant prostate cancer(CSPCa)in the peripheral zone(PZ),transitional zone(TZ)and multiple zones(MZs).Methods:We retrospectively studied the clinical data on 108 patients undergoing multiparametric magnetic resonance imaging(mpMRI)and transperineal prostate biopsy in our hospital from January 2021 to January 2023.Using PI-RADS v2.1,we ex-amined the MR images of the patients with suspected PCa,compared the PI-RADS v2.1 scores with the results of prostate biopsy,and analyzed the correlation of the PI-RADS v2.1 scores with CSPCa.We calculated the area under the receiver operating characteristic(ROC)curve(AUC),and described the diagnostic performance of PI-RADS v2.1 for CSPCa in the PZ,TZ and MZs.Results:Transperineal prostate puncture biopsy was successfully completed in all the patients,which revealed 66(61.11％)cases of CSPCa with Gleason score(GS)7-10.Suspected CSPCa was observed in 45(95.74％)of the 47 PZ lesions,8(47.06％)of the 17 TZ le-sions,and 40(90.91％)of the 44 MZ lesions.The PZ,TZ and MZ lesions diagnosed by PI-RADS v2.1 were significantly correlated with CSPCa(r=0.492,P＜0.001).The AUCs of PI-RADS v2.1 for PZ,TZ and MZs were 0.644,0.732 and 0.811,with specificities of 66.8％,57.6％and 62.1％,and sensitivities of 57.2％,78.4％and 93.2％,respectively.The negative predictive values were 46.5％,85.7％and 79.2％,and the positive predictive values 76.2％,43.4％and 84.8％,respectively.Conclusion:The PI-RADS v2.1 score has a high diagnostic value for CSPCa in the PZ,TZ and MZs,with the best performance for that in the MZs.