A new generation of non-invasive tests of liver fibrosis with improved accuracy in MASLD.

Background & Aims: The accuracy of non-invasive tests (NITs) should be ≥80% (EASL recommendation). We aimed to compare the accuracies of the recommended NITs for advanced fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD) and improve NIT accuracy.

Methods: A total of 1,051 patients with MASLD were allocated to derivation (n = 637) and validation (n = 414) sets.

Cross-linkage between bacterial taxonomy and gene functions: a study of metagenome-assembled genomes of gut microbiota in adult non-alcoholic fatty liver disease.

Background: The reconstruction of metagenome-assembled genomes (MAGs) has emerged as a powerful approach for combining the taxonomic and functional content of microbial populations.

Aim: To use this new approach to highlight mechanisms linking gut microbiota to NAFLD severity METHODS: Stool samples were collected from 96 NAFLD patients on the day of liver biopsy. Shotgun DNA sequencing of the gut microbiota was performed on an Illumina HiSeq3000 system.

Reliability Criteria of Two-Dimensional Shear Wave Elastography: Analysis of 4277 Measurements in 788 Patients.

Background & Aims: Two-dimensional shear wave elastography (2D-SWE) is an accurate method for the non-invasive evaluation of liver fibrosis. We aimed to determine the reliability criteria and the number of necessary reliable measurements for 2D-SWE.

Methods: 788 patients with chronic liver disease underwent liver biopsy and 2D-SWE examination in three centers.

A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors.

Phenotyping biotic stresses in plant-pathogen interactions studies is often hindered by phenotypes that can hardly be discriminated by visual assessment. Particularly, single gene mutants in virulence factors could lack visible phenotypes. Chlorophyll fluorescence (CF) imaging is a valuable tool to monitor plant-pathogen interactions.

Dataset's chemical diversity limits the generalizability of machine learning predictions.

The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML molecular predictions have been recently published with an accuracy on par with Density Functional Theory calculations.