Drug-induced liver injury (DILI) is one of the common adverse drug reactions and is the main cause of withdrawal of drugs after marketing, which has attracted more and more attention of the public, and herb-induced liver injury (HILI) is a special type of DILI. In recent years, the frequent occurrence of HILI not only seriously endangers the health of patients, but also causes the controversy over the safety of traditional Chinese medicine. Therefore, this article reviews the potential risk factors for HILI from the three aspects of "patient", "drug", and "use", so as to provide a basis for the objective identification, prevention, and control of HILI and a reference for the construction of traditional Chinese medicine pharmacovigilance system represented by liver injury.

Inflammasomes play an important role in the innate immunity of the liver; however, the excessive activation of inflammasomes can lead to liver inflammation and injury. The mechanism of nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome-mediated liver injury has been extensively studied. Related studies have shown that the development of various liver diseases may be associated with the excessive activation of inflammasomes, especially NLRP3 inflammasome. This article reviews inflammasomes, the activation mechanism of NLRP3 inflammasome, and the role of NLRP3 inflammasome in different liver diseases, so as to provide a reference for the treatment targets of liver diseases from the perspective of NLRP3 inflammasome.

Objective To establish the intelligent identification method for the big data of liver injury-related adverse drug reaction (ADR) based on the construction of text database. Methods With the keywords including "drug-induced liver injury" and "abnormal liver function" and a search time of January 1, 2012 to December 31, 2016, 5% (4152 cases) of the case reports of liver injury-related ADR were retrieved and extracted from the China Adverse Drug Reaction Monitoring System, and then based on clinical reevaluation by physicians, these cases were classified into "negative cases", "suspected cases", and "confirmed cases". On this basis, key elements (including ADR name, biochemical parameter, and clinical symptoms) were identified. An intelligent identification method for liver injury-related ADR was established based on the correlation analysis between key elements and clinical reevaluation and the receiver operating characteristic (ROC) curve for determining cut-off values, and the method of cross validation was used to evaluate the performance of this intelligent identification method. Results The formula for the evaluation and identification of liver injury-related ADR was as follows: total score (M)=symptom score+index score+ADR name score. This formula showed the best discriminatory ability to distinguish "negative case" from "suspected case" or "confirmed case" at M=5 (area under the ROC curve [AUC]=0.97), with a sensitivity of 99.57% and a specificity of 84.61%, and it showed the best discriminatory ability to distinguish "confirmed case" from "suspected case" or "negative case" at M=12 (AUC=0.938), with a sensitivity of 87.93% and a specificity of 85.98%. Conclusion This method provides reference and basis for intelligent identification and evaluation of big data on liver injury-related ADR and is expected to effectively reduce the burden of manual processing of ADR big data and provide effective tools and methodological demonstration for early risk signal identification and warning of liver injury-related ADR.