Senescence in hepatic stellate cells as a mechanism of liver fibrosis reversal: a putative synergy between retinoic acid and PPAR-gamma signalings

Hepatic stellate cells (HSCs), also known as perisinusoidal cells, are pericytes found in the perisinusoidal space of the liver. HSCs are the major cell type involved in liver fibrosis, which is the formation of scar tissue in response to liver damage. When the liver is damaged, stellate cells can shift into an activated state, characterized by proliferation, contractility and chemotaxis. The activated HSCs secrete collagen scar tissue, which can lead to cirrhosis. Recent studies have shown that in vivo activation of HSCs by fibrogenic agents can eventually lead to senescence of these cells, which would contribute to reversal of fibrosis although it may also favor the insurgence of liver cancer. HSCs in their non-active form store huge amounts of retinoic acid derivatives in lipid droplets, which are progressively depleted upon cell activation in injured liver. Retinoic acid is a metabolite of vitamin A (retinol) that mediates the functions of vitamin A, generally required for growth and development. The precise function of retinoic acid and its alterations in HSCs has yet to be elucidated, and nonetheless in various cell types retinoic acid and its receptors (RAR and RXR) are known to act synergistically with peroxisome proliferator-activated receptor gamma (PPAR-gamma) signaling through the activity of transcriptional heterodimers. Here, we review the recent advancements in the understanding of how retinoic acid signaling modulates the fibrogenic potential of HSCs and proposes a synergistic combined action with PPAR-gamma in the reversal of liver fibrosis

Probabilistic inversions of electrical resistivity tomography data with a machine learning-based forward operator

Casting a geophysical inverse problem into a Bayesian setting is often discouraged by the computational workload needed to run many forward modeling evaluations. Here we present probabilistic inversions of electrical resistivity tomography data in which the forward operator is replaced by a trained residual neural network that learns the non-linear mapping between the resistivity model and the apparent resistivity values. The use of this specific architecture can provide some advantages over standard convolutional networks as it mitigates the vanishing gradient problem that might affect deep networks. The modeling error introduced by the network approximation is properly taken into account and propagated onto the estimated model uncertainties. One crucial aspect of any machine learning application is the definition of an appropriate training set. We draw the models forming the training and validation sets from previously defined prior distributions, while a finite element code provides the associated datasets. We apply the approach to two probabilistic inversion frameworks: a Markov Chain Monte Carlo algorithm is applied to synthetic data, while an ensemble-based algorithm is employed for the field measurements. For both the synthetic and field tests, the outcomes of the proposed method are benchmarked against the predictions obtained when the finite element code constitutes the forward operator. Our experiments illustrate that the network can effectively approximate the forward mapping even when a relatively small training set is created. The proposed strategy provides a forward operator three that is orders of magnitude faster than the accurate but computationally expensive finite element code. Our approach also yields most likely solutions and uncertainty quantifications comparable to those estimated when the finite element modeling is employed. The presented method allows solving the Bayesian electrical resistivity tomography with a reasonable computational cost and limited hardware resources

Immunopositivity for Histone MacroH2A1 Isoforms Marks Steatosis-Associated Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Prevention and risk reduction are important and the identification of specific biomarkers for early diagnosis of HCC represents an active field of research. Increasing evidence indicates that fat accumulation in the liver, defined as hepatosteatosis, is an independent and strong risk factor for developing an HCC. MacroH2A1, a histone protein generally associated with the repressed regions of chromosomes, is involved in hepatic lipid metabolism and is present in two alternative spliced isoforms, macroH2A1.1 and macroH2A1.2. These isoforms have been shown to predict lung and colon cancer recurrence but to our knowledge, their role in fatty-liver associated HCC has not been investigated previously