Department of Aquaculture, Faculty of Agriculture, Universiti Putra Malaysia, 43400 Selangor, Malaysia.
Ecotoxicol Environ Saf. 2012 Mar;77:28-34. doi: 10.1016/j.ecoenv.2011.10.026. Epub 2011 Nov 17.
This study examined the potential of artificial neural network (ANN) modeling to infer timing, route and dose of contaminant exposure from biomarkers in a freshwater fish. Hepatic glutathione S-transferase (GST) activity and biliary concentrations of BaP, 1-OH BaP, 3-OH BaP and 7,8D BaP were quantified in juvenile Clarias gariepinus injected intramuscularly or intraperitoneally with 10-50 mg/kg benzo[a]pyrene (BaP) 1-3 d earlier. A feedforward multilayer perceptron (MLP) ANN resulted in more accurate prediction of timing, route and exposure dose than a linear neural network or a radial basis function (RBF) ANN. MLP sensitivity analyses revealed contribution of all five biomarkers to predicting route of exposure but no contribution of hepatic GST activity or one of the two hydroxylated BaP metabolites to predicting time of exposure and dose of exposure. We conclude that information content of biomarkers collected from fish can be extended by judicious use of ANNs.
本研究旨在探讨人工神经网络（ANN）建模在推断淡水鱼中污染物暴露的时间、途径和剂量方面的潜力。研究人员通过肌肉或腹腔注射 10-50mg/kg 苯并[a]芘（BaP），对幼年非洲鲶鱼的肝谷胱甘肽 S-转移酶（GST）活性和胆汁中 BaP、1-OH BaP、3-OH BaP 和 7,8D BaP 的浓度进行了定量分析。结果表明，与线性神经网络或径向基函数（RBF）神经网络相比，前馈多层感知器（MLP）神经网络在预测时间、途径和暴露剂量方面的结果更为准确。MLP 敏感性分析表明，所有五种生物标志物均有助于预测暴露途径，但肝 GST 活性或两种羟化 BaP 代谢物均无助于预测暴露时间和暴露剂量。研究人员得出结论，通过明智地使用 ANN，可以扩展从鱼类中收集的生物标志物的信息含量。
