The effects of caffeinated and non-caffeinated paracetamol administration, with or without vitamins A and E supplementation on the protein and enzyme levels in Wistar albino rats were investigated using caffeinated paracetamol and paracetamol as caffeinated and non-caffeinated paracetamol respectively, and water soluble acetic acid derivatives of vitamins A and E. Serum AST, ALT and ALP levels (u/l) significantly increased [P < 0.05] following paracetamol administration. Caffeination as well as administration of vitamins A and E caused significant decreases[P < 0.05] in AST and ALP levels in all test groups when co-administered with paracetamol and in ALT level except in the caffeinated paracetamol + Vitamin E group in which ALT and ALP level except in the caffeinated paracetamol + vitamin E group in which ALT and ALP levels significantly increased [P < 0.05]. Total serum protein level (g/100ml) significantly increased following caffeination as well as during co-administration of caffeinated paracetamol and Vitamin E; and significantly decreased during co-administration of paracetamol and vitamin A. Paracetamol administration without caffeination or supplementation with vitamin A and E can therefore cause increases in serum liver enzymes that is suggestive of liver necrosis which can be ameliorated to varying degrees by caffeine, vitamin A and E.
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http://dx.doi.org/10.4314/njps.v22i1-2.54894 | DOI Listing |
J Clin Pharmacol
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Laboratory of Carbon and Ceramic Materials (LMC), Department of Physics, Center of Exact Sciences, Federal University of Espírito Santo, Vitória 29075-910, Espírito Santo, Brazil.
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Trace organic contaminants (TrOCs), including pharmaceutically active compounds (PhACs), present significant challenges for conventional water treatment processes and pose potential risks to environmental and human health. To address these issues, nanofiltration (NF) and reverse osmosis (RO) membrane technologies have gained attention. This study aims to evaluate the performance of NF and RO membranes in removing TrOCs from wastewater and develop a predictive model using the Solution Diffusion Model.
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