https://www.selleckchem.com/products/as2863619.html These include stem cell therapy, anticoagulation therapy, selectin inhibition, inflammatory pathway modulation, superoxide and peroxynitrite decomposition, selective nitric oxide synthase inhibition, hydrogen sulfide, HMG-CoA reductase inhibition, proton pump inhibition, and targeted nanotherapies. While each of these approaches shows a potential therapeutic benefit to treating inhalation injury in animal models, further research including mortality benefit is needed to ensure safety and efficacy in humans. CONCLUSIONS Multiple novel therapies currently under active investigation to treat smoke inhalation injury show promising results. Much research remains to be conducted before these emerging therapies can be translated to the clinical arena.BACKGROUND As the most common form of lymphoma, diffuse large B-cell lymphoma (DLBCL) is a clinical highly heterogeneous disease with variability in therapeutic outcomes and biological features. It is a challenge to identify of clinically meaningful tools for outcome prediction. In this study, we developed a prognosis model fused clinical characteristics with drug resistance pharmacogenomic signature to identify DLBCL prognostic subgroups for CHOP-based treatment. METHODS The expression microarray data and clinical characteristics of 791 DLBCL patients from two Gene Expression Omnibus (GEO) databases were used to establish and validate this model. By using univariate Cox regression, eight clinical or genetic signatures were analyzed. The elastic net-regulated Cox regression analysis was used to select the best prognosis related factors into the predictive model. To estimate the prognostic capability of the model, Kaplan-Meier curve and the area under receiver operating characteristic (ROC) curve (AUC) were performed. RESULTS A predictive model comprising 4 clinical factors and 2 pharmacogenomic gene signatures was established after 1000 times cross validation in the training