Relevance of the deletion polymorphisms of the glutathione S-transferases GSTT1 and GSTM1 in pharmacology and toxicology

Although cytosolic glutathione S-transferase (GST) enzymes occupy a key position in biological detoxification processes, two of the most relevant human isoenzymes, GSTT1-1 and GSTM1-1, are genetically deleted (non-functional alleles GSTT1*0 and GSTM1*0) in a high percentage of the human population, with major ethnic differences. The structures of the GSTT and GSTM gene areas explain the underlying genetic processes. GSTT1-1 is highly conserved during evolution and plays a major role in phase-II biotransformation of a number of drugs and industrial chemicals, e.g. cytostatic drugs, hydrocarbons and halogenated hydrocarbons. GSTM1-1 is particularly relevant in the deactivation of carcinogenic intermediates of polycyclic aromatic hydrocarbons. Several lines of evidence suggest that hGSTT1-1 and/or hGSTM1-1 play a role in the deactivation of reactive oxygen species that are likely to be involved in cellular processes of inflammation, ageing and degenerative diseases. There is cumulating evidence that combinations of the GSTM1*0 state with other genetic traits affecting the metabolism of carcinogens (CYP1A1, GSTP1) may predispose the aero-digestive tract and lung, especially in smokers, to a higher risk of cancer. The GSTM1*0 status appears also associated with a modest increase in the risk of bladder cancer, consistent with a GSTM1 interaction with carcinogenic tobacco smoke constituents. Both human GST deletions, although largely counterbalanced by overlapping substrate affinities within the GST superfamily, have consequences when the organism comes into contact with distinct man-made chemicals. This appears relevant in industrial toxicology and in drug metabolism.

Lyprinol : is it a useful anti-inflammatory agent?

The New Zealand green lipped mussel preparation Lyprinol is available without a prescription from a supermarket, pharmacy or Web. The Food and Drug Administration have recently warned Lyprinol USA about their extravagant anti-inflammatory claims for Lyprinol appearing on the web. These claims are put to thorough review. Lyprinol does have anti-inflammatory mechanisms, and has anti-inflammatory effects in some animal models of inflammation. Lyprinol may have benefits in dogs with arthritis. There are design problems with the clinical trials of Lyprinol in humans as an anti-inflammatory agent in osteoarthritis and rheumatoid arthritis, making it difficult to give a definite answer to how effective Lyprinol is in these conditions, but any benefit is small. Lyprinol also has a small benefit in atopic allergy. As anti-inflammatory agents, there is little to choose between Lyprinol and fish oil. No adverse effects have been reported with Lyprinol. Thus, although it is difficult to conclude whether Lyprinol does much good, it can be concluded that Lyprinol probably does no major harm.

Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

Reading the play : adaptation by prediction of agent motion

An adaptive agent improves its performance by learning from experience. This paper describes an approach to adaptation based on modelling dynamic elements of the environment in order to make predictions of likely future state. This approach is akin to an elite sports player being able to “read the play”, allowing for decisions to be made based on predictions of likely future outcomes. Modelling of the agent‟s likely future state is performed using Markov Chains and a technique called “Motion and Occupancy Grids”. The experiments in this paper compare the performance of the planning system with and without the use of this predictive model. The results of the study demonstrate a surprising decrease in performance when using the predictions of agent occupancy. The results are derived from statistical analysis of the agent‟s performance in a high fidelity simulation of a world leading real robot soccer team.