Fish, toxicity assessment

Reinhartz, A. Lampert, I. Herzberg, M. Fish F. A new, short-term sensitive, bacterial assay kit for the detection of toxicants. Toxic. Assess. 1987, 2, 193-206. 

Tissne Residne Approach for Toxicity Assessment Invertebrates and Fish. Leavenworth, Washington, 7 to 10 Jnn 2007. To be pnblished by SETAC and CRC Press, 2010. 

Toxicity assessment. Ethanol extract of the leaf, administered intraperitoneally to mice, was active, LDjf, 0.75 g/kg"" " . Ethanol extract of the fresh leaf and stem, administered intraperitoneally to mice at the minimum toxic dose of 1 mL/animal, was active. Water extract of the fresh leaf and stem, administered intraperitoneally to mice at the minimum toxic dose of 1 mL/ animal, was active " . Aqueous extract of the husk fiber, administered orally to mice, was active, LDjf, 2.30 g/kgf" " . Tricarboxylate carrier influence. Oil, administered to rats at a dose of 15% of the diet for 3 weeks, produced a differential mitochondrial fatty acid composition and no appreciable change in phospholipids composition and cholesterol level. Compared with coconut oil-fed rats, the mitochondrial tricarboxylate carrier activity was markedly decreased in liver mitochondria from fish oil-fed rats. No difference in the Arrhenius plot between the two groups was observed "". 

Couture, P., Blaise, C., Cluis, D. and Bastien, C. (1989) Zirconium toxicity assessment using bacteria, algae and fish assay, Water, Air, and Soil Pollution 47 (1-2), 87-100. 

What equilibria need to be considered in assessing the fish toxicity of a galvanic waste containing Zn(II) and cyanide ... 

In the following process (step 2), further information on the likely biological activity of the compound may be obtained through classihcation schemes (where available) for the endpoint of interest. For example, classification schemes by Verhaar [42] and Russom [45] can be used when assessing the mode of action for acute fish toxicity. The classification scheme developed by Cramer et al. [44] is useful for evaluating the likely systemic toxicity of a compound. 

Polak, M.E., Rawson, D.M. and Haggett, B.G.D. (1996) Redox mediated biosensors incorporating cultured fish cells for toxicity assessment. Biosens. Bioelectron. 11(12), 1253-1257. 

Walker, J. (1988) Relative sensitivity of algae, bacteria, invertebrates and fish to phenol analysis of 234 tests conducted for more than 149 species. Toxicity Assessment, 3, 415 147. 

Lipnick, R. L., Bickings, C. K., Johnson, D. E. and Eastmond, D. A. (1986) Comparison of QSAR predictions with fish toxicity screening data for 110 phenols, in Aquatic Toxicology and Hazard Assessments (eds R. C. Bahner and D. J. Hansen), STP 891, ASTM, Philadelphia, PA. 

EinaHy, the ecotoxicological studies, designed to assess the impact of the substance on the environment, embrace acute toxicity tests to fish and Daphnia, and a battery of tests for the biodegradabiUty of the substance and its biological oxygen demand characteristics. 

The final article, by S. G. Bell and G. A. Codd of the University of Dundee Department of Biological Services, is concerned with detection, analysis, and risk assessment of cyanobacterial toxins. These can be responsible for animal, fish, and bird deaths and for ill-health in humans. The occurrence of toxic cyanobacterial blooms and scums on nutrient-rich waters is a world-wide phenomenon and cases are cited from Australia, the USA, and China, as well as throughout Europe. The causes, indentification and assessment of risk, and establishment of criteria for controlling risk are discussed. 

The sheer complexity of environmental mixtnres of EDCs, possible interactive effects, and capacity of some EDCs to bioaccumulate (e.g., in fish, steroidal estrogens and alkylphenolic chemicals have been shown to be concentrated up to 40,000-fold in the bile [Larsson et al. 1999 Gibson et al. 2005]) raises questions about the adequacy of the risk assessment process and safety margins established for EDCs. There is little question that considerable further work is needed to generate a realistic pictnre of the mixture effects and exposure threats of EDCs to wildlife populations than has been derived from studies on individual EDCs. Further discussion of the toxicity of mixtures will be found in Chapter 2, Section 2.6. 

This gives an example of fate modeling in which the risks of an insect growth inhibitor, CGA-72662, in aquatic environments were assessed using a combination of the SWRRB and EXAMS mathematical models.. Runoff of CGA-72662 from agricultural watersheds was estimated using the SWRRB model. The runoff data were then used to estimate the loading of CGA-72662 into the EXAMS model for aquatic environments. EXAMS was used to estimate the maximum concentrations of CGA-72662 that would occur in various compartments of the defined ponds and lakes. The maximum expected environmental concentrations of CGA-72662 in water were then compared with acute and chronic toxicity data for CGA-72662 in fish and aquatic invertebrates in order to establish a safety factor for CGA-72662 in aquatic environments. 

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