The alterations induced in gut microbiota by tacrolimus may affect immune function and organ transplantation. Mice were treated with high-dose tacrolimus for 14 days. The fecal microbiota were analyzed by pyrosequencing the 16S rRNA genes, and the effect on metabolism was predicted using the sequence data. The subgroups of T cells in the serum, gut-associated lymphoid tissue, and draining lymph nodes were determined by flow cytometry. Tacrolimus treatment significantly altered the relative abundance of Allobaculum, Bacteroides, and Lactobacillus and CD4 CD25 FoxP3 regulatory T cells in the colonic mucosa and the circulation. These were significantly increased after either tacrolimus treatment or treatment by fecal microbiota transfer from tacrolimus-treated donors. Further, treatment with low-dose tacrolimus plus fecal microbiota transfer from high-dose tacrolimus-altered mice increased skin allograft survival rate in a skin transplantation model. Thus, high-dose tacrolimus alters the compositions and taxa of the gut microbiota. Administration of these conditioned gut microbiota plus low-dose tacrolimus resulted in regulation of colonic and systemic immune responses and an increased allograft survival rate. This study demonstrated a new strategy for controlling allograft rejection by combining an immunosuppressive agent with gut microbiome transplantation.
Background: Prominent hypointense vessel sign (PHVS) is visualized on susceptibility weighted-imaging (SWI) in acute ischaemic stroke (AIS). We aim to test if PHVS is associated with stroke outcome. Methods: Forty patients with acute middle cerebral artery occlusion were recruited. The presence of PHVS, cortical vessel sign (CVS), brush sign (BS) and susceptibility-diffuse weighted imaging mismatch (S-D mismatch) and Alberta Stroke Program Early CT Score (ASPECTS) on SWI were compared between the good outcome group (90-day modified Rankin scale [mRS] of 0–2) and the poor outcome group (mRS of 3–6). The receiver operating characteristic curves (ROC) were used to evaluate the predictive ability to poor outcome of above imaging characteristics. Results: The presence of PHVS, CVS, BS and S-D mismatch was significantly higher in the poor outcome group (p < 0.001, p = 0.001, p = 0.013, p = 0.014, respectively). SWI-ASPECTS was significantly lower in the poor outcome group (p = 0.002). Regression analysis revealed SWI-ASPECTS; the presence of PHVS and CVS were independently associated with poor outcome (OR 0.347, p = 0.012; OR 55.77, p = 0.004; OR 58.05, p = 0.005). ROC analysis showed that PHVS had the highest predictive value for poor outcome (AUC 0.783). Conclusions: The presence of PHVS, CVS and SWI-ASPECTS were associated with poor outcome in AIS. The presence of PHVS was the most effective radiographic marker for predicting outcome.
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