Histone deacetylase inhibitors: potential targets responsible for their anti-cancer effect

The histone deacetylase inhibitors (HDACi) have demonstrated anticancer efficacy across a range of malignancies, most impressively in the hematological cancers. It is uncertain whether this clinical efficacy is attributable predominantly to their ability to induce apoptosis and differentiation in the cancer cell, or to their ability to prime the cell to other pro-death stimuli such as those from the immune system. HDACi-induced apoptosis occurs through altered expression of genes encoding proteins in both intrinsic and extrinsic apoptotic pathways; through effects on the proteasome/aggresome systems; through the production of reactive oxygen species, possibly by directly inducing DNA damage; and through alterations in the tumor microenvironment. In addition HDACi increase the immunogenicity of tumor cells and modulate cytokine signaling and potentially T-cell polarization in ways that may contribute the anti-cancer effect in vivo. Here, we provide an overview of current thinking on the mechanisms of HDACi activity, with attention given to the hematological malignancies as well as scientific observations arising from the clinical trials. We also focus on the immune effects of these agents

ICAR: endoscopic skull‐base surgery

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An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level

This paper presents a development of general regression neural network (a form of artificial neural network) models for the prediction of annual quantities of hazardous chemical and healthcare waste at the national level. Hazardous waste is being generated from many different sources and therefore it is not possible to conduct accurate predictions of the total amount of hazardous waste using traditional methodologies. Since they represent about 40% of the total hazardous waste in the European Union, chemical and healthcare waste were specifically selected for this research. Broadly available social, economic, industrial and sustainability indicators were used as input variables and the optimal sets were selected using correlation analysis and sensitivity analysis. The obtained values of coefficients of determination for the final models were 0.999 for the prediction of chemical hazardous waste and 0.975 for the prediction of healthcare and biological hazardous waste. The predicting capabilities of the models for both types of waste are high, since there were no predictions with errors greater than 25%. Also, results of this research demonstrate that the human development index can replace gross domestic product and in this context even represent a better indicator of socio-economic conditions at the national level