Aquatic Toxicology Laboratory, Environmental Toxicology Group, Food, Drug & Chemical, Environment and Systems, Toxicology (FEST) Division, Council of Scientific and Industrial Research-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India.
Indian Mine Planners and Consultants, GE-61, Rajdanga, Kolkata, West Bengal, India.
J Hazard Mater. 2024 May 15;470:134076. doi: 10.1016/j.jhazmat.2024.134076. Epub 2024 Mar 21.
Recently, the rampant administration of antibiotics and their synthetic organic constitutes have exacerbated adverse effects on ecosystems, affecting the health of animals, plants, and humans by promoting the emergence of extreme multidrug-resistant bacteria (XDR), antibiotic resistance bacterial variants (ARB), and genes (ARGs). The constraints, such as high costs, by-product formation, etc., associated with the physico-chemical treatment process limit their efficacy in achieving efficient wastewater remediation. Biodegradation is a cost-effective, energy-saving, sustainable alternative for removing emerging organic pollutants from environmental matrices. In view of the same, the current study aims to explore the biodegradation of ciprofloxacin using microbial consortia via metabolic pathways. The optimal parameters for biodegradation were assessed by employing machine learning tools, viz. Artificial Neural Network (ANN) and statistical optimization tool (Response Surface Methodology, RSM) using the Box-Behnken design (BBD). Under optimal culture conditions, the designed bacterial consortia degraded ciprofloxacin with 95.5% efficiency, aligning with model prediction results, i.e., 95.20% (RSM) and 94.53% (ANN), respectively. Thus, befitting amendments to the biodegradation process can augment efficiency and lead to a greener solution for antibiotic degradation from aqueous media.
最近，抗生素及其合成有机成分的大量使用加剧了其对生态系统的不良影响，通过促进极端多药耐药菌（XDR）、抗生素耐药菌变种（ARB）和基因（ARGs）的出现，影响了动物、植物和人类的健康。物理化学处理过程中存在成本高、副产物形成等限制因素，限制了其在有效废水修复方面的效果。生物降解是一种经济高效、节能、可持续的替代方法，可用于从环境基质中去除新兴有机污染物。有鉴于此，本研究旨在通过代谢途径探索微生物群落对环丙沙星的生物降解作用。通过使用人工神经网络（ANN）和统计优化工具（响应面法，RSM）的 Box-Behnken 设计（BBD），利用机器学习工具评估生物降解的最佳参数。在最佳培养条件下，设计的细菌群落以 95.5%的效率降解环丙沙星，与模型预测结果一致，即 95.20%（RSM）和 94.53%（ANN）。因此，对生物降解过程进行适当的改进可以提高效率，并为从水介质中降解抗生素提供更环保的解决方案。
