Oxidative stress and excessive inflammatory responses are the two critical mechanisms of hepatic ischemia-reperfusion injury (HIRI) encountered in many clinical settings, including following hepatectomy and liver transplantation. Effective anti-inflammatory and anti-oxidative pharmacological interventions are urgently needed to counter HIRI. The present study showed that a biocompatible Prussian blue (PB) scavenger with reactive oxygen species (ROS) scavenging and anti-inflammatory properties might be used a promising treatment for HIRI. Following intravenous administration, PB scavenger was mainly distributed in the liver, where it showed excellent ability to alleviate apoptosis, tissue injury and organ dysfunction after HIRI. PB scavenger was found to protect liver tissue by scavenging ROS, reducing neutrophil infiltration and promoting macrophage M2 polarization. In addition, PB scavenger significantly reduced oxidative stress in primary hepatocytes, restoring cell viability under oxidative stress condition. PB scavenger effectively reduced lipopolysaccharide-stimulated inflammation in RAW 264.7 cells. These findings indicate that PB scavenger may be a potential therapeutic agent for the treatment of HIRI, providing an alternative treatment for ROS-associated and inflammatory liver diseases.

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Prussian Blue Scavenger Ameliorates Hepatic Ischemia-Reperfusion Injury by Inhibiting Inflammation and Reducing Oxidative Stress

Hepatic ischemia–reperfusion injury (IRI) is one of the main factors for early allograft dysfunction (EAD), which may lead to graft rejection, graft loss, or shortened graft life in liver transplantation. Hepatic IRI appears to be inevitable during the majority of liver procurement and transportation of donor organs, resulting in a cascade of biological changes. The activation of signaling pathways during IRI results in the up- and downregulation of genes and microRNAs (miRNAs). miRNAs are ~21 nucleotides in length and well-characterized for their role in gene regulations; they have recently been used for therapeutic approaches in addition to their role as biomarkers for many diseases. miRNAs that are associated with hepatic IRI in in vitro and in vivo animal models are comprehensively summarized in this review. In those studies, the manipulation of miRNAs has been shown for the inhibition of aggravated immune response, reduction of apoptosis, stimulation of tissue repair, and enhancement of cell recovery to attenuate liver damage. Therefore, the utilization of liver-specific miRNA holds great potential as a therapeutic agent to improve early allograft dysfunction, hepatic injury, and patient outcome.

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MicroRNAs: Novel Targets in Hepatic Ischemia–Reperfusion Injury

The m6A methyltransferase Mettl3 deficiency attenuates hepatic stellate cell activation and liver fibrosis.

Abstract Mesenchymal stem cells (MSCs), coming from a wide range of sources, have multi-directional differentiation ability. MSCs play vital roles in immunomodulation, hematopoiesis and tissue repair. The microenvironment of cells often refers to the intercellular matrix, other cells, cytokines and humoral components. It is also the place for cells’ interaction. The stability of the microenvironment is pivotal for maintaining cell proliferation, differentiation, metabolism and functional activities. Abnormal changes in microenvironment components can interfere cell functions. In some diseases, MSCs can interact with the microenvironment and accelerate disease progression. This review will discuss the characteristics of MSCs and their microenvironment, as well as the interaction between MSCs and microenvironment in disease. 

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Mesenchymal stem cells and their microenvironment

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Liver transplantation is the ultimate method for treating end-stage liver disease. With the increasing prevalence of obesity, the number of patients with non-alcoholic fatty liver, a common cause of chronic liver disease, is on the rise and may become the main cause of liver transplantation in the future. With the increasing gap between the number of donor livers and patients waiting for liver transplantation and the increasing prevalence of non-alcoholic fatty liver, the proportion of steatosis livers among non-standard donor organs is also increasing. Ischemia-reperfusion injury has historically been the focus of attention in the liver transplantation process, and severe ischemia-reperfusion injury leads to adverse outcomes of liver transplantation. Studies have shown that the production of reactive oxygen species and subsequent oxidative stress play a key role in the pathogenesis of hepatic ischemia and reperfusion injury and non-alcoholic fatty liver. Furthermore, the sensitivity of fatty liver transplantation to ischemia-reperfusion injury has been suggested to be related to the production of reactive oxygen species (ROS) and oxidative stress. In ischemia-reperfusion injury, Kupffer cell and macrophage activation along with mitochondrial damage and the xanthine/xanthine oxidase system promote marked reactive oxygen species production and the inflammatory response and apoptosis, resulting in liver tissue injury. The increased levels of ROS and lipid peroxidation products, vicious circle of ROS and oxidative stress along with mitochondrial dysfunction promoted the progress of non-alcoholic fatty liver. In contrast to the non-fatty liver, a non-alcoholic fatty liver produces more reactive oxygen species and suffers more serious oxidative stress when subjected to ischemia-reperfusion injury. We herein review the effects of reactive oxygen species on ischemia-reperfusion injury and non-alcoholic fatty liver injury as well as highlight several treatment approaches.

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Reactive Oxygen Species Induce Fatty Liver and Ischemia-Reperfusion Injury by Promoting Inflammation and Cell Death

Liver fibrosis refers to the process underlying the development of chronic liver diseases, wherein liver cells are repeatedly destroyed and regenerated, which leads to an excessive deposition and abnormal distribution of the extracellular matrix such as collagen, glycoprotein and proteoglycan in the liver. Liver fibrosis thus constitutes the pathological repair response of the liver to chronic injury. Hepatic fibrosis is a key step in the progression of chronic liver disease to cirrhosis and an important factor affecting the prognosis of chronic liver disease. Further development of liver fibrosis may lead to structural disorders of the liver, nodular regeneration of hepatocytes and the formation of cirrhosis. Hepatic fibrosis is histologically reversible if treated aggressively during this period, but when fibrosis progresses to the stage of cirrhosis, reversal is very difficult, resulting in a poor prognosis. There are many causes of liver fibrosis, including liver injury caused by drugs, viral hepatitis, alcoholic liver, fatty liver and autoimmune disease. The mechanism underlying hepatic fibrosis differs among etiologies. The establishment of an appropriate animal model of liver fibrosis is not only an important basis for the in-depth study of the pathogenesis of liver fibrosis but also an important means for clinical experts to select drugs for the prevention and treatment of liver fibrosis. The present study focused on the modeling methods and fibrosis characteristics of different animal models of liver fibrosis, such as a chemical-induced liver fibrosis model, autoimmune liver fibrosis model, cholestatic liver fibrosis model, alcoholic liver fibrosis model and non-alcoholic liver fibrosis model. In addition, we also summarize the research and application prospects concerning new organoids in liver fibrosis models proposed in recent years. A suitable animal model of liver fibrosis and organoid fibrosis model that closely resemble the physiological state of the human body will provide bases for the in-depth study of the pathogenesis of liver fibrosis and the development of therapeutic drugs.

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Animal and Organoid Models of Liver Fibrosis.

Hepatic ischemia reperfusion injury (IRI), a fascinating topic that has drawn a lot of interest in the last few years, is a major complication caused by a variety of clinical situations, such as liver transplantation, severe trauma, vascular surgery, and hemorrhagic shock. The IRI process involves a series of complex events, including mitochondrial deenergization, metabolic acidosis, adenosine-5'-triphosphate depletion, Kupffer cell activation, calcium overload, oxidative stress, and the upregulation of pro-inflammatory cytokine signal transduction. A number of protective strategies have been reported to ameliorate IRI, including pharmacological therapy, ischemic pre-conditioning, ischemic post-conditioning, and machine reperfusion. However, most of these strategies are only at the stage of animal model research at present, and the potential mechanisms and exact therapeutic targets have yet to be clarified. IRI remains a main cause of postoperative liver dysfunction, often leading to postoperative morbidity or even mortality. Very recently, it was reported that the activation of peroxisome proliferator-activated receptor γ (PPARγ), a member of a superfamily of nuclear transcription factors activated by agonists, can attenuate IRI in the liver, and FAM3A has been confirmed to mediate the protective effect of PPARγ in hepatic IRI. In addition, non-coding RNAs, like LncRNAs and miRNAs, have also been reported to play a pivotal role in the liver IRI process. In this review, we presented an overview of the latest advances of treatment strategies and proposed potential mechanisms behind liver IRI. We also highlighted the role of several important molecules (PPARγ, FAM3A, and non-coding RNAs) in protecting against hepatic IRI. Only after achieving a comprehensive understanding of potential mechanisms and targets behind IRI can we effectively ameliorate IRI in the liver and achieve better therapeutic effects.

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Novel Targets and Therapeutic Strategies to Protect Against Hepatic Ischemia Reperfusion Injury

Background Multiple myeloma (MM) is a malignant hematopoietic disease that is usually incurable. RNA-binding proteins (RBPs) are involved in the development of many tumors, but their prognostic significance has not been systematically described in MM. Here, we developed a prognostic signature based on eight RBP-related genes to distinguish MM cohorts with different prognoses. Method After screening the differentially expressed RBPs, univariate Cox regression was performed to evaluate the prognostic relevance of each gene using The Cancer Genome Atlas (TCGA)-Multiple Myeloma Research Foundation (MMRF) dataset. Lasso and stepwise Cox regressions were used to establish a risk prediction model through the training set, and they were validated in three Gene Expression Omnibus (GEO) datasets. We developed a signature based on eight RBP-related genes, which could classify MM patients into high- and low-score groups. The predictive ability was evaluated using bioinformatics methods. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and gene set enrichment analyses were performed to identify potentially significant biological processes (BPs) in MM. Result The prognostic signature performed well in the TCGA-MMRF dataset. The signature includes eight hub genes: HNRNPC, RPLP2, SNRPB, EXOSC8, RARS2, MRPS31, ZC3H6, and DROSHA. Kaplan-Meier survival curves showed that the prognosis of the risk status showed significant differences. A nomogram was constructed with age; B2M, LDH, and ALB levels; and risk status as prognostic parameters. Receiver operating characteristic (ROC) curve, C-index, calibration analysis, and decision curve analysis (DCA) showed that the risk module and nomogram performed well in 1, 3, 5, and 7-year overall survival (OS). Functional analysis suggested that the spliceosome pathway may be a major pathway by which RBPs are involved in myeloma development. Moreover, our signature can improve on the R-International Staging System (ISS)/ISS scoring system (especially for stage II), which may have guiding significance for the future. Conclusion We constructed and verified the 8-RBP signature, which can effectively predict the prognosis of myeloma patients, and suggested that RBPs are promising biomarkers for MM.

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Identification and Validation of a Novel RNA-Binding Protein-Related Gene-Based Prognostic Model for Multiple Myeloma.

Convolutional neural networks in the field of artificial intelligence show great potential in image recognition. It assisted endoscopy to improve the detection rate of early gastric cancer. The 5-year survival rate for advanced gastric cancer is less than 30%, while the 5-year survival rate for early gastric cancer is more than 90%. Therefore, earlier screening for gastric cancer can lead to a better prognosis. However, the detection rate of early gastric cancer in China has been extremely low due to many factors, such as the presence of gastric cancer without obvious symptoms, difficulty identifying lesions by the naked eye, and a lack of experience among endoscopists. The introduction of artificial intelligence can help mitigate these shortcomings and greatly improve the accuracy of screening. According to relevant reports, the sensitivity and accuracy of artificial intelligence trained on deep cirrocumulus neural networks are better than those of endoscopists, and evaluations also take less time, which can greatly reduce the burden on endoscopists. In addition, artificial intelligence can also perform real-time detection and feedback on the inspection process of the endoscopist to standardize the operation of the endoscopist. AI has also shown great potential in training novice endoscopists. With the maturity of AI technology, AI has the ability to improve the detection rate of early gastric cancer in China and reduce the death rate of gastric cancer related diseases in China.

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Ya-Hong Chen

Papers

Reactive Oxygen Species Induce Fatty Liver and Ischemia-Reperfusion Injury by Promoting Inflammation and Cell Death

Animal and Organoid Models of Liver Fibrosis.

Novel Targets and Therapeutic Strategies to Protect Against Hepatic Ischemia Reperfusion Injury

Identification and Validation of a Novel RNA-Binding Protein-Related Gene-Based Prognostic Model for Multiple Myeloma.

The Feasibility of Applying Artificial Intelligence to Gastrointestinal Endoscopy to Improve the Detection Rate of Early Gastric Cancer Screening