Acetaminophen (APAP) is the most commonly reported toxic ingestion in the world. Severe liver injury resulting from overdose or chronic use of APAP remains a significant clinical problem. In recent years, the mechanisms underlying liver injury caused by APAP have become much better understood. We have studied the protective effect of chitosan supplementation against APAP-induced hepatotoxicity with respect to changes in the levels of total and lipid-bound sialic acid in the serum and in the liver tissue and changes in the activity of diagnostic marker enzymes, lipid peroxidation, and ceruloplasmin oxidase enzyme in normal and experimental groups of rats. During the experimental period, chitosan (200 mg/kg body weight per day) was administered to APAP + chitosan-treated rats by oral gavage. Results showed that treatment with APAP induced a significant increase in the serum alanine aminotransferase and alkaline phosphatase activities, in total and lipid-bound sialic acids levels, and in the liver lipid peroxide content. The administration of chitosan significantly prevented APAP-induced alterations in the levels of diagnostic marker enzymes, total sialic acid, lipid-bound sialic acid, and malondialdehyde in the experimental groups of rats. Furthermore, chitosan administration increased the activity of ceruloplasmin oxidase. In conclusion, our results suggest that chitosan has a protective effect on APAP-induced hepatic injury in rats. The study sheds light on the therapeutic potential of chitosan in an APAP-induced hepatotoxicity model.
We model the MIBP problem (MP) in GAMS 23.7 and solve the problem using BONMIN and COUENNE solver packages. We model the corresponding MIP reformulation, model (LMP), in AMPL 20141128 and solve it using GUROBI 6.5.0. The computations are done in a Dell personal computer with Intel(R) Core(TM) i5 − 4300U CPU 2.50 GHz processor, and with 8.00 GB of RAM.The performance measures used for models (MP) and (LMP) are presented in Table 1. This table summarizes the size of the problems solved, the running time in CPU seconds, and the optimality gap reported by each solver. Each problem corresponds to one of the states in the southeastern U.S. The last problem set (SE), corresponds to the whole Southeast. Two stopping
Co-firing biomass is a strategy that leads to reduced greenhouse gas emissions in coal-fired power plants.Incentives such as production tax credit (PTC) are designed to help power plants overcome the financial challenges faced during the implementation phase. Decision makers at power plants face two big challenges.The first challenge is identifying whether the benefits from incentives such as PTC can overcome the costs associated with co-firing. The second challenge is identifying the extent to which a plant should co-fire in order to maximize profits. We present a novel mathematical model that integrates production and transportation decisions at power plants. Such a model enables decision makers evaluate the impacts of co-firing on the system performance and the cost of generating renewable electricity. The model presented is a nonlinear mixed integer program which captures the loss in process efficiencies due to using biomass, a product which has lower heating value as compared to coal; the additional investment costs necessary to support biomass co-firing; as well as savings due to PTC. In order to solve efficiently real-life instances of this problem we present a Lagrangean relaxation model which provide upper bounds and two linear approximations which provide lower bounds for the problem in hand. We use numerical analysis to evaluate the quality of these bounds. We develop a case study using data from nine states located in the southeast region of USA. Via numerical experiments we observe that: (a) Incentives such as PTC do facilitate renewable energy production.(b) The PTC should not be "one size fits all". Instead, tax credits could be a function of plant capacity, or the amount of renewable electricity produced. (c) There is a need for comprehensive tax credit schemes to encourage renewable electricity production and reduce GHG emissions.
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