Cryptosporidiosis among solid organ transplant recipient attendees at a summer camp.

We report a cluster of pediatric cryptosporidiosis infections among solid organ transplant recipients at a summer camp in Georgia, USA. A retrospective cohort study was conducted to investigate the risk factors for infection. A total of 118 campers attended the camp during July 23-28, 2017. The overall attack rate among campers during the outbreak was 11% (13/118). Sanger-based amplicon sequencing of stool specimens from 7 (80%) campers identified Cryptosporidium hominis as the suspected etiologic agent. All infected campers were heart or kidney transplant recipients receiving immunosuppressive therapy. The median reported symptom duration was 12 days (range 6-18 days) and 9 (69.2%) were hospitalized for at least one night (median length of stay 5 days, range 2-16 days). There were no deaths or acute rejection events attributed to infection. The results of the epidemiologic and environmental investigation suggest a recreational pool as the presumed source, although there was no direct evidence to support this. Many long-term interventions were implemented, and there have been no further outbreaks at the camp in the following two years. This outbreak demonstrates that cryptosporidiosis may be associated with notable burden in pediatric transplant recipients, and illustrates the challenges associated with source identification and containment.

Identifying accurate metagenome and amplicon software via a meta-analysis of sequence to taxonomy benchmarking studies.

Metagenomic and meta-barcode DNA sequencing has rapidly become a widely-used technique for investigating a range of questions, particularly related to health and environmental monitoring. There has also been a proliferation of bioinformatic tools for analysing metagenomic and amplicon datasets, which makes selecting adequate tools a significant challenge. A number of benchmark studies have been undertaken; however, these can present conflicting results. In order to address this issue we have applied a robust Z-score ranking procedure and a network meta-analysis method to identify software tools that are consistently accurate for mapping DNA sequences to taxonomic hierarchies. Based upon these results we have identified some tools and computational strategies that produce robust predictions.