Characterization of the Toxicological Effects of Aminocarb on Rats: Hematological, Biochemical, and Histological Analyses

Aminocarb is a widely applied carbamate insecticide with action of controlling pests such as Lepidoptera and Coleoptera. In this study, subchronic effects on Wistar rats were investigated using hematological, biochemical, and histological techniques. Rats were exposed orally at sublethal levels of 10, 20, or 40 mg/kg body weight (groups A, B, and C, respectively) for 14 d. Hematological results revealed no statistical differences after 1 d of exposure but significant reduction in white blood cells detected after 7 d of exposure in group C, as well as, in all treated groups after 14 d of exposure. Biochemical data showed a decrease of acetylcholinesterase activity in all groups after 1 d of exposure with a return to normal after 7 and 14 d. Significant increase in alkaline phosphatase activity of rats exposed to aminocarb was noted after 7 d of treatment. The levels of triglycerides were also significantly decreased. The present investigation also showed a significant increase in content of serum urea and creatinine in animals from group A (14 d), and from groups B and C (7 and 14 d). Histological results demonstrated hemorrhagic focus on hepatic and renal parenchyma in all exposed groups. Taken together, the attained results were dose dependent and indicated adverse effects of aminocarb on hepatic and renal functions, as well as on immune responsiveness at sublethal tested doses.

Modeling Vegetable Fractals by means of Fractional-order Equations

This paper characterizes four ‘fractal vegetables’: (i) cauliflower (brassica oleracea var. Botrytis); (ii) broccoli (brassica oleracea var. italica); (iii) round cabbage (brassica oleracea var. capitata) and (iv) Brussels sprout (brassica oleracea var. gemmifera), by means of electrical impedance spectroscopy and fractional calculus tools. Experimental data is approximated using fractional-order models and the corresponding parameters are determined with a genetic algorithm. The Havriliak-Negami five-parameter model fits well into the data, demonstrating that classical formulae can constitute simple and reliable models to characterize biological structures.