Sodium arsenite induces hepatic stellate cells activation by m6A modification of TGF-ß1 during liver fibrosis.

The compound known as Sodium arsenite (NaAsO2), which is a prevalent type of inorganic arsenic found in the environment, has been strongly associated with liver fibrosis (LF), a key characteristic of nonalcoholic fatty liver disease (NAFLD), which has been demonstrated in our previous study. Our previous research has shown that exposure to NaAsO2 triggers the activation of hepatic stellate cells (HSCs), a crucial event in the development of LF. However, the molecular mechanism is still unknown. N6-methyladenosine (m6A) modification is the most crucial post-transcriptional modification in liver disease. Nevertheless, the precise function of m6A alteration in triggering HSCs and initiating LF caused by NaAsO2 remains unknown. Here, we found that NaAsO2 induced LF and HSCs activation through TGF-ß/Smad signaling, which could be reversed by TGF-ß1 knockdown. Furthermore, NaAsO2 treatment enhanced the m6A modification level both in vivo and in vitro. Significantly, NaAsO2 promoted the specific interaction of METTL14 and IGF2BP2 with TGF-ß1 and enhanced the TGF-ß1 mRNA stability. Notably, NaAsO2-induced TGF-ß/Smad pathway and HSC-t6 cells activation might be avoided by limiting METTL14/IGF2BP2-mediated m6A modification. Our findings showed that the NaAsO2-induced activation of HSCs and LF is made possible by the METTL14/IGF2BP2-mediated m6A methylation of TGF-ß1, which may open up new therapeutic options for LF brought on by environmental hazards.

Tell me your position: Distantly supervised biomedical entity relation extraction using entity position marker.

A significant amount of textual data has been produced in the biomedical area recently as a result of the advancement of biomedical technologies. Large-scale biomedical data can be automatically obtained with the help of distant supervision. However, the noisy data brought by distant supervision methods makes relation extraction tasks more difficult. Previous work has focused more on how to restore mislabeled relationships, but little attention has been paid to the importance of labeled entity locations for relationship extraction tasks. In this paper, we present a "four-stage" model based on BioBERT and Multi-Instance Learning by using entity position markers. Firstly, the sentence is marked with position. Secondly, BioBERT, a biomedical pre-trained language model, is used in the final sentence feature vector representation not only with the global position marker but also with the start and end marker of both the head and tail entity. Thirdly, the aggregation of sentence vectors in the bag is used as the vector feature of the bag by three aggregation methods, and the performance of different sentence feature vectors combined with different bag encoding methods is discussed. At last, relation classification is performed at the bag level. According to experimental results, the presented model significantly outperforms all baseline models and contributes to noise reduction. In addition, different bag encoding methods need to match corresponding sentence encoding representation to achieve the best performance.