Cost-effectiveness of integrating gut microbiota analysis into hospitalisation prediction in cirrhosis

Clinical applications of metagenomics next-generation sequencing in infectious diseases

Infectious diseases are a great threat to human health. Rapid and accurate detection of pathogens is important in the diagnosis and treatment of infectious diseases. Metagenomics next-generation sequencing (mNGS) is an unbiased and comprehensive approach for detecting all RNA and DNA in a sample. With the development of sequencing and bioinformatics technologies, mNGS is moving from research to clinical application, which opens a new avenue for pathogen detection. Numerous studies have revealed good potential for the clinical application of mNGS in infectious diseases, especially in difficult-to-detect, rare, and novel pathogens. However, there are several hurdles in the clinical application of mNGS, such as: (1) lack of universal workflow validation and quality assurance; (2) insensitivity to high-host background and low-biomass samples; and (3) lack of standardized instructions for mass data analysis and report interpretation. Therefore, a complete understanding of this new technology will help promote the clinical application of mNGS to infectious diseases. This review briefly introduces the history of next-generation sequencing, mainstream sequencing platforms, and mNGS workflow, and discusses the clinical applications of mNGS to infectious diseases and its advantages and disadvantages.

BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets

The relationship between the host and the microbiome, or the assemblage of microorganisms (including bacteria, archaea, fungi, and viruses), has been proven crucial for its health and disease development. The high dimensionality of microbiome datasets has often been addressed as a major difficulty for data analysis, such as the use of machine-learning (ML) and deep-learning (DL) models. Here, we present BiGAMi, a bi-objective genetic algorithm fitness function for feature selection in microbial datasets to train high-performing phenotype classifiers. The proposed fitness function allowed us to build classifiers that outperformed the baseline performance estimated by the original studies by using as few as 0.04% to 2.32% features of the original dataset. In 35 out of 42 performance comparisons between BiGAMi and other feature selection methods evaluated here (sequential forward selection, SelectKBest, and GARS), BiGAMi achieved its results by selecting 6–93% fewer features. This study showed that the application of a bi-objective GA fitness function against microbiome datasets succeeded in selecting small subsets of bacteria whose contribution to understood diseases and the host state was already experimentally proven. Applying this feature selection approach to novel diseases is expected to quickly reveal the microbes most relevant to a specific condition.

Chronic Liver Diseases and the Microbiome-Translating Our Knowledge of Gut Microbiota to Management of Chronic Liver Disease

Chronic liver disease is reaching epidemic proportions with the increasing prevalence of obesity, nonalcoholic liver disease, and alcohol overuse worldwide. Most patients are not candidates for liver transplantation even if they have end-stage liver disease. There is growing evidence of a gut microbial basis for many liver diseases, therefore, better diagnostic, prognostic, and therapeutic approaches based on knowledge of gut microbiota are needed. We review the questions that need to be answered to successfully translate our knowledge of the intestinal microbiome and the changes associated with liver disease into practice.

Microbiome: Emerging Concepts in Patients with Chronic Liver Disease

The gut microbiome is an exciting new area of research in chronic liver disease. It has shown promise in expanding our understanding of these complicated disease processes and has opened up new treatment modalities. The aim of this review is to increase understanding of the microbiome and explain the collection and analysis process in the context of liver disease. It also looks at our current understanding of the role of the microbiome in the wide spectrum of chronic liver diseases and how it is being used in current therapies and treatments.