Yikang decoction facilitates embryo implantation in mice with implantation dysfunction via upregulation of LIF expression.

OBJECTIVE: The present study investigates the effects and mechanisms of Yikang decoction on embryo implantation in mice. METHODS: Totally, mature female mice were randomly divided into four groups: normal, implantation failure (mifepristone treatment), Yikang decoction treatment (mifepristone and Yikang decoction treatment), and control (mifepristone and physiological saline treatment) groups. The efficacy of Yikang decoction was evaluated by the adhesion of uterine endometrial cells. The expression of leukemia inhibitory factor (LIF) and integrin αvß3 in the endometrium were detected by real-time quantitative PCR and western blot. In addition, mouse endometrial cells were transfected with LIF specific siRNA-1, and the expression of LIF and αvß3 were detected. RESULTS: The number of embryos markedly decreased after mifepristone treatment. The adhesion of endometrial cells in the Yikang decoction treatment group significantly increased, when compared to the control group. The expression of LIF and integrin αvß3 was significantly reduced by mifepristone, but the attenuated expression of LIF and αvß3 was markedly reversed by treatment with Yikang decoction. In addition, after LIF siRNA-1 transfection, the expression of integrin αvß3 significantly decreased (P < 0.01). The correlation analysis revealed a significant positive correlation between LIF and αvß3 protein expression. CONCLUSION: Yikang decoction can regulate the expression of αvß3 and increase cell adhesion by upregulating the expression of LIF, thereby improving embryo implantation in mice. These data suggest that Yikang decoction may have therapeutic effect in treating infertility.

A Fuzzy Expert System to Predict the Risk of Postpartum Hemorrhage.

INTRODUCTION: The American College of Obstetricians and Gynecologists (ACOG) defines postpartum hemorrhage (PPH) as a blood loss of >500mL following vaginal delivery or >1000mL following cesarean section. PPH is widely recognized as a common cause of maternal death. However, there is currently no effective method to predict its risk of occurrence. AIM: To develop a fuzzy expert system to predict the risk of developing PPH and to evaluate its performance in the clinical setting. METHODS: This system was developed using MATLAB software. Mamdani inference was used to simulate reasoning of experts in the field. To evaluate the performance of the system, a dataset of 1705 patients admitted at the Labor and Delivery ward of The Second Affiliated Hospital of Nanjing Medical University from 2017-10 to 2018-04, was considered. RESULTS: The Negative Predictive value (NPV), Positive Predictive value PPV), Specificity and Sensitivity were calculated and were 99.72%, 18.50%, 87.48% and 92.16% respectively. CONCLUSIONS: Our findings suggest that the fuzzy expert system can be used to predict PPH in clinical settings and thus decrease maternal mortality rate due to hemorrhage.