Novel Hydrogen Sulfide Hybrid Derivatives of Keap1-Nrf2 Protein-Protein Interaction Inhibitor Alleviate Inflammation and Oxidative Stress in Acute Experimental Colitis.

Ulcerative colitis (UC) is an idiopathic inflammatory disease of unknown etiology possibly associated with intestinal inflammation and oxidative stress. Molecular hybridization by combining two drug fragments to achieve a common pharmacological goal represents a novel strategy. The Kelch-like ECH-associated protein 1 (Keap1)-nuclear factor erythroid 2-related factor 2 (Nrf2) pathway provides an effective defense mechanism for UC therapy, and hydrogen sulfide (H2S) shows similar and relevant biological functions as well. In this work, a series of hybrid derivatives were synthesized by connecting an inhibitor of Keap1-Nrf2 protein-protein interaction with two well-established H2S-donor moieties, respectively, via an ester linker, to find a drug candidate more effective for the UC treatment. Subsequently, the cytoprotective effects of hybrids derivatives were investigated, and DDO-1901 was identified as a candidate showing the best efficacy and used for further investigation on therapeutic effect on dextran sulfate sodium (DSS)-induced colitis in vitro and in vivo. Experimental results indicated that DDO-1901 could effectively alleviate DSS-induced colitis by improving the defense against oxidative stress and reducing inflammation, more potent than parent drugs. Compared with either drug alone, such molecular hybridization may offer an attractive strategy for the treatment of multifactorial inflammatory disease.

Recent applications of CBT-Cys click reaction in biological systems.

Click chemistry is a hot topic in many research fields. A biocompatible reaction from fireflies has attracted increasing attention since 2009. Herein, we focus on the firefly-sourced click reaction between cysteine (Cys) and 2-cyanobenzothiazole (2-CBT). This reaction has many excellent properties, such as rapidity, simplicity and high selectivity, which make it successfully applied in protein labeling, molecular imaging, drug discovery and other fields. Meanwhile, its unique ability to form nanoparticles expands its applications in biological systems. We review its principle, development, and latest applications in the past 5 years and hope this review provides more profound and comprehensive insights to its further application.

Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching.

The question answering link in the traditional teaching method is analyzed to optimize the shortcomings and deficiencies of the existing question-and-answer (Q&A) machines and solve the problems of financial students' difficulty in answering questions. Firstly, the difficulties and needs of students in answering questions are understood. Secondly, the traditional algorithm principle by the Q&A system is introduced and analyzed, and the problems and defects existing in the traditional Q&A system are summarized. On this basis, deep learning algorithms are introduced, the long short-term memory (LSTM) neural network and convolutional neural network (CNN) are combined, and a Q&A system by long short-term memory-convolutional neural network (LSTM-CNN) is proposed, the gated recurrent unit (GRU) attention mechanism is introduced, and the algorithm is optimized. Finally, the design experiments to determine the nearest parameters of the neural network algorithm and verify the effectiveness of the algorithm are carried out. The results show that for the LSTM-CNN, the effect is the best when dropout = 0.5. After introducing the attention mechanism optimization, the effect is the best when dropout = 0.6. The test results of the comparison between the recommended algorithm and the traditional Q&A model algorithm show that the LSTM-CNN algorithm maintains the ability of the LSTM algorithm to arrange information in chronological order. After being combined with the CNN algorithm, the language features of the sentence can be extracted more deeply, the semantic feature information can be captured more accurately from the sentence, and better performance can be maintained when processing more complex sentences. The introduction of a BANet can simultaneously obtain the past and future information so that the algorithm can more appropriately combine it with the context to retrieve the semantic features, and the effectiveness of the model has been greatly improved. The research results have played an optimizing role in improving the Q&A effect of finance and economics teaching and provided a reference for research in related fields.