We aimed to determine the prebiotic impact of Mushroom Bulgaria inquinans (BI) on the host immune response and gut microbiota. Male C57BL/6 mice were fed a diet supplemented with 0, 1, or 2% BI for 4 wks. Compared to mice fed with a control diet (0% BI), mice fed with 1 or 2% BI had an increase of T cell proliferation from the spleen, but such change was not found between 1 and 2% BI treated mice. Also, BI at 2% increased the production of IL-2 of splenocytes stimulated with T-cell mitogens, but BI at 1 and 2% did not affect productions of other splenic-T cell cytokines including IL-4, IL-10, and IFN-γ. Interestingly, BI at 1 or 2% inhibited T cell proliferation of mesenteric lymph node (mLN) but this effect was not found between 1 and 2% BI treated mice. Furthermore, BI inhibited the production of IL-2 in anti-CD3/CD28-stimulated T cells from mLN in a dose-dependent manner. Meanwhile, BI at 2%, not 1% inhibited the production of IL-4, IL-10, and IFN-γ of mLN. Since BI at 2% produced a more significant effect on the immune response, we further used BI at 2% to evaluate the effect of BI on gut microbiota. Of note, BI reduced the diversity of gut microbiota and resulted in an increase of Faecalibaculum and Parabacteroides abundance and the decrease of Allobaculum, Candidatus_Saccharimonas, and Rikenella abundance at the genus level. Finally, the correlation was observed between specific bacteria genera and the productions of T-cell cytokines from mesenteric lymphocytes: Rikenella and Candidatus_Saccharimonas correlated positively with IL-2, IL-4, IL-10, and IFN-γ; Bacteroides and Parabacteroides correlated negatively with IL-2 and IL-4; Faecalibaculum correlated negatively with IFN-γ and IL-4 and Bacteroides and Bifidobacterium correlated negatively with IFN-γ. The specific role of each intestinal microbiota observed is still unclear, but BI might exert a prebiotic effect on gut microbiota by increasing the abundance of potentially beneficial bacteria (Faecalibaculum). This is helpful for further demonstrating the healthy-promotion mechanism of B. inquinans.

Postpartum hemorrhage (PPH) is an obstetric emergency instigated by excessive blood loss which occurs frequently after the delivery. The PPH can result in volume depletion, hypovolemic shock, and anemia. This is particular condition is considered a major cause of maternal deaths around the globe. Presently, physicians utilize visual examination for calculating blood and fluid loss during delivery. Since the classical methods depend on expert knowledge and are inaccurate, automated machine learning based PPH diagnosis models are essential. In regard to this aspect, this study introduces an efficient oppositional binary crow search algorithm (OBCSA) with an optimal stacked auto encoder (OSAE) model, called OBCSA-OSAE for PPH prediction. The goal of the proposed OBCSA-OSAE technique is to detect and classify the presence or absence of PPH. The OBCSA-OSAE technique involves the design of OBCSA based feature selection (FS) methods to elect an optimum feature subset. Additionally, the OSAE based classification model is developed to include an effective parameter adjustment process utilizing Equilibrium Optimizer (EO). The performance validation of the OBCSA-OSAE technique is performed using the benchmark dataset. The experimental values pointed out the benefits of the OBCSA-OSAE approach in recent methods.