Evaluation of an automated genome interpretation model for rare disease routinely used in a clinical genetic laboratory.

Purpose: The analysis of exome and genome sequencing data for the diagnosis of rare diseases is challenging and time-consuming. In this study, we evaluated an artificial intelligence model, based on machine learning for automating variant prioritization for diagnosing rare genetic diseases in the Baylor Genetics clinical laboratory.

Methods: The automated analysis model was developed using a supervised learning approach based on thousands of manually curated variants.

Specific pathogens and microbial abundance within liver and kidney tissues of wild marine fish from the Eastern Mediterranean Sea.

This study is an initial description and discussion of the kidney and liver microbial communities of five common fish species sampled from four sites along the Eastern Mediterranean Sea shoreline. The goals of the present study were to establish a baseline dataset of microbial communities associated with the tissues of wild marine fish, in order to examine species-specific microbial characteristics and to screen for candidate pathogens. This issue is especially relevant due to the development of mariculture farms and the possible transmission of pathogens from wild to farmed fish and vice versa.

Prevalence of nervous necrosis virus (NNV) and Streptococcus species in wild marine fish and crustaceans from the Levantine Basin, Mediterranean Sea.

Infectious diseases in marine animals have ecological, socio-economic and environmental impacts. Nervous necrosis virus (NNV) and Streptococcus iniae have become major threats to marine aquaculture and have been detected in morbid marine organisms worldwide. However, despite their importance, there is a lack of knowledge regarding the prevalence of these pathogens in wild fish species.