Non-alcoholic fatty liver disease (NAFLD) affects about 24% of the world's population. Progression of early stages of NAFLD can lead to the more advanced form non-alcoholic steatohepatitis (NASH), and ultimately to cirrhosis or liver cancer. The current gold standard for diagnosis and assessment of NAFLD/NASH is liver biopsy followed by microscopic analysis by a pathologist. The Kleiner score is frequently used for a semi-quantitative assessment of disease progression. In this scoring system the features of active injury (steatosis, inflammation, and ballooning) and a separated fibrosis score are quantified. The procedure is time consuming for pathologists, scores have limited resolution and are subject to variation. We developed an automated deep learning method that provides full reproducibility and higher resolution. The system was established with 296 human liver biopsies and tested on 171 human liver biopsies with pathologist ground truth scores. The method is inspired by the way pathologist's analyze liver biopsies. First, the biopsies are analyzed microscopically for the relevant histopathological features. Subsequently, histopathological features are aggregated to a per-biopsy score. Scores are in the identical numeric range as the pathologist’s ballooning, inflammation, steatosis, and fibrosis scores, but on a continuous scale. Resulting scores followed a pathologist's ground truth (quadratic weighted Cohen’s κ on the test set: for steatosis 0.66, for inflammation 0.24, for ballooning 0.43, for fibrosis 0.62, and for the NAFLD activity score (NAS) 0.52. Mean absolute errors on a test set: for steatosis 0.29, for inflammation 0.53, for ballooning 0.61, for fibrosis 0.78, and for the NAS 0.77).

No abstract

No abstract

The liver's extraordinary ability to regenerate has been known since the myth of Prometheus, but the mechanisms involved are still being discovered. Various small animal models have been used in this quest. Two of the most popular include partial hepatectomy (PHx), in which two-thirds of the liver mass is surgically removed to evoke a massive, immediate stimulus for regeneration, and prolonged exposure to toxins that kill liver cells more gradually, provoking chronic regenerative activity. In either case, multiple types of cells must interact effectively to repopulate the organ with functional mature hepatocytes and thus assure ultimate restoration of healthy liver structure and function. This complexity has confounded efforts to distinguish specific changes that occur in cells that repopulate the hepatocyte compartment from changes in other cell populations, including subpopulations of hepatocytes or hepatocyte precursors that do not become regenerative. In the current issue of the JCI, Wang et al. used translating ribosome affinity purification followed by high-throughput RNA sequencing (TRAP-seq) to isolate mRNAs from repopulating hepatocytes in order to profile gene expression specifically in the hepatocytes that regenerate the liver following toxic injury imposed by inherent byproducts of tyrosine metabolism. This innovative methodology can potentially be used to design therapeutic strategies for liver regeneration.