The special features of liver sinusoidal endothelium (LSE) are crucial for normal liver physiology. Cirrhotic livers, especially in primary biliary cirrhosis (PBC), are characterized by transformation of the LSE into a continuous, vascular type. The transformation is important for disease progression and explains some of the pathological hallmarks of the cirrhotic liver. Here, we investigated the presence of liver sinusoidal endothelial cell (LSEC)-reactive autoantibodies (Abs) in the sera of patients with autoimmune liver diseases, and assessed the ability of these Abs to transform LSE into vascular endothelium. Compared to healthy individuals (9%), significantly higher numbers of patients with PBC (59%; P < 0.001) and autoimmune hepatitis (AIH) (32%; P

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Decellularized materials (DMs) are attracting more and more attention in tissue engineering because of their many unique advantages, and they could be further improved in some aspects through various means.

Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variablelength text. RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors, which form a matrix. And this matrix includes two dimensions: the time-step dimension and the feature vector dimension. Then most existing models usually utilize one-dimensional (1D) max pooling operation or attention-based operation only on the time-step dimension to obtain a fixed-length vector. However, the features on the feature vector dimension are not mutually independent, and simply applying 1D pooling operation over the time-step dimension independently may destroy the structure of the feature representation. On the other hand, applying two-dimensional (2D) pooling operation over the two dimensions may sample more meaningful features for sequence modeling tasks. To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text. This paper also utilizes 2D convolution to sample more meaningful information of the matrix. Experiments are conducted on six text classification tasks, including sentiment analysis, question classification, subjectivity classification and newsgroup classification. Compared with the state-of-the-art models, the proposed models achieve excellent performance on 4 out of 6 tasks. Specifically, one of the proposed models achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks.

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Since identifying relations between chemicals and diseases (CDR) are important for biomedical research and healthcare, the challenge proposed by BioCreative V requires automatically mining causal relationships between chemicals and diseases which may span sentence boundaries. Although most systems explore feature engineering and knowledge bases to recognize document level CDR relations, feature learning automatically is limited only in a sentence. In this work, we proposed an effective model that automatically learns document level semantic representations to extract chemical-induced disease (CID) relations from articles by combining advantages of convolutional neural network and recurrent neural network. First, to purposefully collect contexts, candidate entities existing in multiple sentences of an article were masked to make the model have ability to discern candidate entities and general terms. Next, considering the contiguity and temporality among associated sentences as well as the topic of an article, a hierarchical network architecture was designed at the document level to capture semantic information of different types of text segments in an article. Finally, a softmax classifier performed the CID recognition. Experimental results on the CDR corpus show that the proposed model achieves a good overall performance compared with other state-of-the-art methods. Although only using two types of embedding vectors, our approach can perform well for recognizing not only intra-sentential but also inter-sentential CID relations.