Extracellular matrix loss is one of the early manifestations of intervertebral disc degeneration. Stem cell-based tissue engineering creates an appropriate microenvironment for long term cell survival, promising for NP regeneration. We created a decellularized nucleus pulposus hydrogel (DNPH) from fresh bovine nucleus pulposus. Decellularization removed NP cells effectively, while highly preserving their structures and major biochemical components, such as glycosaminoglycan and collagen II. DNPH could be gelled as a uniform grid structure in situ at 37°C for 30 min. Adding adipose marrow-derived mesenchymal stem cells into the hydrogel for three-dimensional culture resulted in good bioactivity and biocompatibility in vitro. Meanwhile, NP-related gene expression significantly increased without the addition of exogenous biological factors. In summary, the thermosensitive and injectable hydrogel, which has low toxicity and inducible differentiation, could serve as a bio-scaffold, bio-carrier, and three-dimensional culture system. Therefore, DNPH has an outstanding potential for intervertebral disc regeneration.

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DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription factor-binding site, etc. Moreover, the related locus of disease (or trait) are usually enriched in the DHS regions. Therefore, the detection of DHS region is of great significance. In this study, we develop a deep learning-based algorithm to identify whether an unknown sequence region would be potential DHS. The proposed method showed high prediction performance on both training datasets and independent datasets in different cell types and developmental stages, demonstrating that the method has excellent superiority in the identification of DHSs. Furthermore, for the convenience of related wet-experimental researchers, the user-friendly web-server iDHS-Deep was established at http://lin-group.cn/server/iDHS-Deep/, by which users can easily distinguish DHS and non-DHS and obtain the corresponding developmental stage ofDHS.

4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in Geobacter pickeringii. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model.