Interplays of liver fibrosis-associated microRNAs:Molecular mechanisms and implications in diagnosis and therapy

microRNAs(miRNAs)are a class of non-coding functional small RNA composed of 21e23 nucleotides,having multiple associations with liver fibrosis.Fibrosis-associated miRNAs are roughly classified into pro-fibrosis or anti-fibrosis types.The former is capable of activating hepatic stellate cells(HSCs)by modulating pro-fibrotic signaling pathways,mainly including TGF-b/SMAD,WNT/b-catenin,and Hedgehog;the latter is responsible for maintenance of the quiescent phenotype of normal HSCs,phenotypic reversion of activated HSCs(aHSCs),inhibition of HSCs proliferation and suppression of the extracellular matrix-associated gene expression.Moreover,several miRNAs are involved in regulation of liver fibrosis via alternative mechanisms,such as interacting between hepatocytes and other liver cells via exosomes and increasing autophagy of aHSCs.Thus,understanding the role of these miRNAs may provide new avenues for the development of novel interventions against hepatic fibrosis.

Annotation and Joint Extraction of Scientific Entities and Relationships in NSFC Project Texts

Aiming at the lack of classification and good standard corpus in the task of joint entity and relationship extraction in the current Chinese academic field, this paper builds a dataset in management science that can be used for joint entity and relationship extraction, and establishes a deep learning model to extract entity and relationship information from scientific texts. With the definition of entity and relation classification, we build a Chinese scientific text corpus dataset based on the abstract texts of projects funded by the National Natural Science Foundation of China(NSFC) in 2018–2019. By combining the word2vec features with the clue word feature which is a kind of special style in scientific documents, we establish a joint entity relationship extraction model based on the Bi LSTM-CNN-CRF model for scientific information extraction. The dataset we constructed contains 13060 entities(not duplicated) and 9728 entity relation labels. In terms of entity prediction effect, the accuracy rate of the constructed model reaches 69.15%, the recall rate reaches 61.03%, and the F1 value reaches 64.83%. In terms of relationship prediction effect, the accuracy rate is higher than that of entity prediction, which reflects the effectiveness of the input mixed features and the integration of local features with CNN layer in the model.