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.

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.

Backgrounds:Colorectal cancer (CRC) with high incidence, has the third highest mortality of tumors. DNA damage and repair influence a variety of tumors. However, the role of these genes in colon cancer prognosis has been less systematically investigated. Here, we aim to establish a corresponding prognostic signature providing new therapeutic opportunities for CRC.Method:After related genes were collected from GSEA, univariate Cox regression was performed to evaluate each gene’s prognostic relevance through the TCGA-COAD dataset. Stepwise COX regression was used to establish a risk prediction model through the training sets randomly separated from the TCGA cohort and validated in the remaining testing sets and two GEO datasets (GSE17538 and GSE38832). A 12-DNA-damage-and-repair-related gene-based signature able to classify COAD patients into high and low-risk groups was developed. The predictive ability of the risk model or nomogram were evaluated by different bioinformatics‐ methods. Gene functional enrichment analysis was performed to analyze the co-expressed genes of the risk-based genes.Result:A 12-gene based prognostic signature established within 160 significant survival-related genes from DNA damage and repair related gene sets performed well with an AUC of ROC 0.80 for 5 years in the TCGA-CODA dataset. The signature includes CCNB3, ISY1, CDC25C, SMC1B, MC1R, LSP1P4, RIN2, TPM1, ELL3, POLG, CD36, and NEK4. Kaplan-Meier survival curves showed that the prognosis of the risk status owns more significant differences than T, M, N, and stage prognostic parameters. A nomogram was constructed by LASSO regression analysis with T, M, N, age, and risk as prognostic parameters. ROC curve, C-index, Calibration analysis, and Decision Curve Analysis showed the risk module and nomogram performed best in years 1, 3, and 5. KEGG, GO, and GSEA enrichment analyses suggest the risk involved in a variety of important biological processes and well-known cancer-related pathways. These differences may be the key factors affecting the final prognosis.Conclusion:The established gene signature for CRC prognosis provides a new molecular tool for clinical evaluation of prognosis, individualized diagnosis, and treatment. Therapies based on targeted DNA damage and repair mechanisms may formulate more sensitive and potential chemotherapy regimens, thereby expanding treatment options and potentially improving the clinical outcome of CRC patients.

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.