The results of this analysis of well-matched transplant recipients show that CIT and DGF are the most important predictors of poor short and long-term graft survival. Therefore, in order to improve the long-term survival of renal allografts efforts should focus on limiting CIT and the damage that occurs during this period and on improving our understanding of DGF.

Studies of embryonic stem cells (ESCs) reveal that these cell lines can be derived from differing stages of embryonic development. We analyzed common changes in the expression of microRNAs (miRNAs) and mRNAs in 9 different human ESC (hESC) lines during early commitment and further examined the expression of key ESCenriched miRNAs in earlier developmental states in several species. We show that several previously defi ned hESC-enriched miRNA groups (the miR-302, -17, and -515 families, and the miR-371-373 cluster) and several other hESC-enriched miRNAs are down-regulated rapidly in response to differentiation. We further found that mRNAs up-regulated upon differentiation are enriched in potential target sites for these hESC-enriched miRNAs. Interestingly, we also observed that the expression of ESC-enriched miRNAs bearing identical seed sequences changed dynamically while the cells transitioned through early embryonic states. In human and monkey ESCs, as well as human-induced pluripotent stem cells (iPSCs), the miR-371-373 cluster was consistently up-regulated, while the miR-302 family was mildly down-regulated when the cells were chemically treated to regress to an earlier developmental state. Similarly, miR-302b, but not mmu-miR-295, was expressed at higher levels in murine epiblast stem cells (mEpiSC) as compared with an earlier developmental state, mouse ESCs. These results raise the possibility that the relative expression of related miRNAs might serve as diagnostic indicators in defi ning the developmental state of embryonic cells and other stem cell lines, such as iPSCs. These data also raise the possibility that miRNAs bearing identical seed sequences could have specifi c functions during separable stages of early embryonic development.

No abstract

No abstract

ObjectiveAccurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD) and future AAA growth. The feasibility of predicting future AAA growth was explored in individual patients using a set of benchmark machine learning techniques.MethodsThe Oxford Abdominal Aortic Aneurysm Study (OxAAA) prospectively recruited AAA patients undergoing the routine NHS management pathway. In addition to the AAA diameter, FMD was systemically measured in these patients. A benchmark machine learning technique (non-linear Kernel support vector regression) was applied to predict future AAA growth in individual patients, using their baseline FMD and AAA diameter as input variables.ResultsProspective growth data were recorded at 12 months (360 ± 49 days) in 94 patients. Of these, growth data were further recorded at 24 months (718 ± 81 days) in 79 patients. The average growth in AAA diameter was 3.4% at 12 months, and 2.8% per year at 24 months. The algorithm predicted the individual's AAA diameter to within 2 mm error in 85% and 71% of patients at 12 and 24 months.ConclusionsThe data highlight the utility of FMD as a biomarker for AAA and the value of machine learning techniques for AAA research in the new era of precision medicine.