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INTRODUCTION: It is unknown whether steroid sensitivity and other putative risk factors collected at baseline can predict the disease course of idiopathic nephrotic syndrome in childhood. We determined whether demographic, clinical, and family reported factors at presentation can predict outcomes in idiopathic nephrotic syndrome. METHODS: An observational cohort of 631 children aged 1 to 18 years diagnosed with idiopathic nephrotic syndrome between 1993 and 2016 were followed up until clinic discharge, 18 years of age, end-stage kidney disease (ESKD), or the last clinic visit. Baseline characteristics were age, sex, ethnicity, and initial steroid sensitivity. Of these, 287 (38%) children also reported any family history of kidney disease, preceding infection, microscopic hematuria, and history of asthma/allergies. The outcomes were complete remission after initial steroid course, need for a second-line agent, frequently relapsing disease, and long-term remission. The discriminatory power of the models was described using the c-statistic. RESULTS: Overall, 25.7% of children had no further disease after their initial steroid course. In addition, 31.2% developed frequently relapsing disease; however, 77.7% were disease-free at 18 years of age. Furthermore, 1% of children progressed to ESKD. Logistic regression modeling using the different baseline exposures did not significantly improve the prediction of outcomes relative to the observed frequencies (maximum c-statistic, 0.63; 95% confidence interval [CI], 0.59-0.67). The addition of steroid sensitivity did not improve outcome prediction of long-term outcomes (c-statistic, 0.63; 95% CI, 0.54-0.70). CONCLUSIONS: Demographic, clinical, and family reported characteristics, specifically steroid sensitivity, are not useful in predicting relapse rates or long-term remission in idiopathic nephrotic syndrome. Further studies are needed to address factors that contribute to long-term health.
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The bacterial core genome is of intense interest and the volume of whole genome sequence data in the public domain available to investigate it has increased dramatically. The aim of our study was to develop a model to estimate the bacterial core genome from next-generation whole genome sequencing data and use this model to identify novel genes associated with important biological functions. Five bacterial datasets were analysed, comprising 2096 genomes in total. We developed a Bayesian decision model to estimate the number of core genes, calculated pairwise evolutionary distances (p-distances) based on nucleotide sequence diversity, and plotted the median p-distance for each core gene relative to its genome location. We designed visually-informative genome diagrams to depict areas of interest in genomes. Case studies demonstrated how the model could identify areas for further study, e.g. 25% of the core genes with higher sequence diversity in the Campylobacter jejuni and Neisseria meningitidis genomes encoded hypothetical proteins. The core gene with the highest p-distance value in C. jejuni was annotated in the reference genome as a putative hydrolase, but further work revealed that it shared sequence homology with beta-lactamase/metallo-beta-lactamases (enzymes that provide resistance to a range of broad-spectrum antibiotics) and thioredoxin reductase genes (which reduce oxidative stress and are essential for DNA replication) in other C. jejuni genomes. Our Bayesian model of estimating the core genome is principled, easy to use and can be applied to large genome datasets. This study also highlighted the lack of knowledge currently available for many core genes in bacterial genomes of significant global public health importance.