Effect of TLR2 monoclonal antibody on new blood vessels and immune rejection response after keratoplasty

Purpose: To investigate the effect of TLR2 (toll-like receptor 2) monoclonal antibody on formation of new blood vessels and immune rejection response after keratoplasty.Methods: The rats were randomly divided into negative control group consisting of 16 rats from allogeneic corneal transplantation, and TLR2 monoclonal antibody group (study group) made up of 16 rats from allogeneic corneal transplantation treated with TLR2 monoclonal antibody. A group of 8 rats served as normal control. The study group was treated with 0.5 g·L-1 TLR2 monoclonal antibody through sub-conjunctival injection once daily for 5 days, while the normal control and negative control groups were given an equivalent volume of normal saline in place of TLR2. Corneal transparency and neovascularization were observed under slit lamp daily after operation, and scored using rejection index.Results: In the TLR2 monoclonal antibody group, the corneal structure was still clear, and only a few inflammatory cells infiltrated the stromal layer. There were trace amounts of TLR2 expression in the corneal epithelium of rats in normal control group, negative control group and TLR2 monoclonal antibody group. In the negative control group, the expression of TLR2 in the corneal epithelium and stromal cells significantly increased, especially in the stromal layer.Conclusion: TLR2 monoclonal antibody exerts a significant effect on neovascularization and immune rejection after corneal transplantation in rats. Thus, it may be clinically suitable for the prevention and treatment of rejection arising from corneal transplantation.Keywords: TLR2 monoclonal antibody, Corneal transplantation, Neovascularization, Immune rejectio

A Nested Attention Neural Hybrid Model for Grammatical Error Correction

Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information,and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography