Rigorous Donor Selection for Fecal Microbiota Transplantation in Active Ulcerative Colitis: Key Lessons From a Randomized Controlled Trial Halted for Futility.

Background & Aims: Rigorous donor preselection on microbiota level, strict anaerobic processing, and repeated fecal microbiota transplantation (FMT) administration were hypothesized to improve FMT induction of remission in ulcerative colitis (UC).

Methods: The RESTORE-UC trial was a multi-centric, double-blind, sham-controlled, randomized trial. Patients with moderate to severe UC (defined by total Mayo 4-10) were randomly allocated to receive 4 anaerobic-prepared allogenic or autologous donor FMTs.

Effects of fecal microbiota transplantation for recurrent Clostridium difficile infection in children on kidney replacement therapy: a pilot study.

Background: Recurrent Clostridium difficile infection (rCDI) is a rising problem in children with chronic diseases. Fecal microbiota transplantation (FMT) is a recent alternative for rCDI patients who do not respond to conventional treatment. FMT could have an additional positive effect on the intestinal dysbiosis and accumulation of uremic retention molecules (URM) associated with chronic kidney disease (CKD).

Diversity Analysis in Viral Metagenomes.

Viruses are the most abundant and diverse biological entity in the earth. Nowadays, there are several viral metagenomes from different ecological niches which have been used to characterize new viral particles and to determine their diversity. However, viral metagenomic data have the disadvantage to be high-dimensional compositional and sparse.

Fragmentation and Coverage Variation in Viral Metagenome Assemblies, and Their Effect in Diversity Calculations.

Metagenomic libraries consist of DNA fragments from diverse species, with varying genome size and abundance. High-throughput sequencing platforms produce large volumes of reads from these libraries, which may be assembled into contigs, ideally resembling the original larger genomic sequences. The uneven species distribution, along with the stochasticity in sample processing and sequencing bias, impacts the success of accurate sequence assembly.