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Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.Nipah virus (NiV) is a zoonotic virus of the paramyxovirus family that sporadically breaks out from livestock and spread in human through breathing resulting in indication of encephalitis syndrome. In the current study, T cell epitopes with the NiV W protein antigens were predicted. Modelling of unavailable 3D structure of W protein followed by docking studies of respective Human MHC - class I and MHC - class II alleles predicted was carried out for the highest binding rates. In the computational analysis, epitopes were assessed for immunogenicity, conservation, and toxicity analysis. T - cell based vaccine development against NiV was screened for eight epitopes of Indian - Asian origin. Two epitopes SPVIAEHYY, LVNDGLNII, have been screened and selected for further docking study based on toxicity and conservancy analyses. These epitopes showed a significant score of -1.19 kcal/mol, 0.15 kcal/mol with HLA- B*3503, HLA- DRB1 * 0703, allele - Class I and Class II using AutoDock. These two peptides predicted by reverse vaccinology approach are likely to induce immune response mediated by T - cells. Simulation using GROMACS has revealed LVNDGLNII epitope forms more stable complex with HLA molecule and will be useful in developing epitope-based Nipah virus vaccine. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.For the past few decades, the mechanisms of immune responses to cancer have been exploited extensively and significant attention has been given into exploiting the therapeutic potential of the immune system. Cancer immunotherapy has been established as a promising innovative treatment for many forms of cancer. Immunotherapy has gained its prominence through various strategies including; cancer vaccines, monoclonal antibodies (mAbs), adoptive T cell cancer therapy and immune checkpoint therapy. However, the full potential of cancer immunotherapy is yet to be attained. Recent studies have identified the use of bioinformatics tools as a viable option to help transform the treatment paradigm of several tumors by providing a therapeutically efficient method of cataloging, predicting and selecting immunotherapeutic targets which are known bottlenecks in the application of immunotherapy. Herein, we gave an insightful overview of the types of immunotherapy techniques used currently, their mechanisms of action and discussed some bioinformatics tools and databases applied in the immunotherapy of cancer. This review also provides some future perspectives in the use of bioinformatics tools for immunotherapy. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND It is known from the most recent literature that far-infrared (FIR) radiations promote a broad spectrum of therapeutic benefits for cells and tissues. OBJECTIVE To identify molecular mechanisms by which FIT patches, as a far infrared technology, protects against damage caused by inflammatory process and oxidative stress. METHODS Endothelial cells (HUVEC, Human Umbilical Vein Endothelial Cells) were used as in vitro experimental model. HUVEC were stimulated with a pro-inflammatory cytokine, TNF-α, or hydrogen peroxide (H2O2) to induce oxidative stress. As markers of inflammation were evaluated VCAM1 (Vascular Cell Adhesion Molecule 1), ICAM1 (Intercellular Adhesion Molecule 1) and E-Selectin by Western Blot analysis. Oxidative stress was assessed by cytofluorimetric analysis. The experiments were performed on control cells (no patch) or in cells treated with the FIT infrared technology applied on the basis of the culture plate. RESULTS HUVEC stimulated with TNF-α and treated with FIT patches had significant reduction of the expression of VCAM1, ICAM1 and E-selectin (p less then 0.05). HUVEC stimulated with H2O2 and treated with FIT patches were significantly protected from oxidative stress (p less then 0.01) when compared to control cells. We measured cell viability and proliferation in HUVEC and HEK-293 (Human embryonic kidney cells) cells by MTT assay. HEK-293 and HUVEC treated with FIT patches showed a significantly higher percentage of basal vitality compared to control cells (p less then 0.0001 for HEK-293, p less then 0.05 for HUVEC). CONCLUSION FIT therapy patches - infrared technology, through these protective mechanisms, could be used in all pathologies where an increase in inflammation, oxidative stress and degenerative state are present. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Enhancing compound biological activity is the central task for lead optimization in small molecule drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity test. To address the issue, it is high-demanding to develop high quality in silico bioactivity prediction approaches, to prioritize those more active compound derivatives and reduce the trial-and-error process. METHODS Two kinds of bioactivity prediction models based on a large-scale structure activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. GKT137831 research buy RESULTS Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most inferior prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). CONCLUSION An accurate prediction model for bioactivity was built with consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.