Toxicity of an industrial effluent was characterized using a toxicity identification method established at our research institute. Toxicity was evaluated using the Ceriodaphnia dubia survival test. The industrial effluent chosen in the present study had toxic effects on C. dubia before treatment. The effluent was first filtered to remove suspended solids (F-treatment). Activated charcoal was then added to the effluent to adsorb organic substances (AC-treatment), followed by treatment of the supernatant with Chelex-100 resin to remove di- and trivalent cations, including transition metals (C100-treatment). The effluent exhibited a toxic effect on C. dubia even after the AC-treatment, but it was no longer toxic after the C100-treatment, indicating that the effluent contained cations that affect C. dubia survival. To further identify the metals responsible for the toxicity, benzoin-alpha-oxime or dimethylglyoxime was added to the effluent to chelate specific cations. The toxicity of the effluent was eliminated by addition of dimethylglyoxime but not significantly so by addition of benzoin-alpha-oxime, suggesting that the toxicants in the effluent strongly formed complexes with dimethylglyoxime. These results combined with those of metal analysis strongly suggest that nickel might be the noxious agent. The toxicity identification method described in this paper is effective for the identification of metal toxicants in industrial effluents. The method using insoluble chelating resins and chelators for specific metals would serve as a useful addition to the standard toxicity identification evaluation procedure.
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http://dx.doi.org/10.1897/07-327R.1 | DOI Listing |
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Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India.
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Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides.
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