Objective:To explore the status and influencing factors of stigma in patients with pituitary adenoma.Methods:From January 2020 to May 2021, a total of 106 patients with pituitary adenoma in the Affiliated Huai'an No.1 People 's Hospital of Nanjing Medical University were selected using the convenience sampling method. Internalized Stigma of Mental Illness, Medical Coping Style Questionnaire and Connor-Davidson Resilience Scale were used to investigate them. Binary Logistic regression analysis was used to evaluate the influencing factors of stigma in patients with pituitary adenoma. Results:Among 106 patients with pituitary adenoma, 87 (82.08%) had stigma. The stigma score of patients with pituitary adenoma was (2.58±0.61) . Binary Logistic regression analysis results showed that family monthly income, coping style and mental elasticity were the influencing factors of stigma in patients with pituitary adenoma ( P<0.05) . Conclusions:Stigma is common in patients with pituitary adenoma. Medical staff should take targeted intervention measures according to the influencing factors of stigma in patients with pituitary adenoma, so as to help patients reduce stigma.

Objective To observe the value of MRI texture features combined with apparent diffusion coefficient(ADC)for differentiating uterine sarcoma(US)and cellular uterine leiomyoma(CUL).Methods Pelvic MRI data of 27 US patients(US group)and 34 CUL patients(CUL group)were retrospectively analyzed.The texture features of lesions were extracted from T2WI and diffusion weighted imaging(DWI),the ADC value were measured,and the average ADC value(ADCmean),the minimum ADC value(ADCmin)and standard ADC value(ADCst)were recorded.Then logistic regression(LR)models were constructed based on ADC value,optimal texture features alone and their combination,respectively,including LRADC,LRtexture and LRADC+texture models.Receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the efficacy of each model for differentiating US and CUL.Results The ADCmean,ADCmin and ADCst in US group were all lower than those in CUL group(all P＜0.05).A total of 3 750 texture features were extracted from pelvic T2WI and DWI,5 optimal features were finally obtained,and the constructed LRADC+texture model and LRtexture model had similar efficacy of differentiating US and CUL(AUC=0.921,0.887;P＞0.05),which were both higher than that of LRADC model(AUC=0.696;both P＜0.05).The calibration curve of LRADC+texture model was basically consistent with the ideal curve,which had better clinical benefits than LRADC and LRtexture models.Conclusion MRI texture features combined with ADC value could improve efficacy for differentiating US and CUL.