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iprodione
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY · 2024 · IF 6.2

Iprodione induces hepatotoxicity in zebrafish by mediating ROS generation and upregulating p53 signalling pathway

Weitao Hu,Guilan Chen,Wenbin Yuan,Chen Guo,Fasheng Liu,Shouhua Zhang,Zigang Cao

Iprodione is an effective and broad-spectrum fungicide commonly used for early disease control in fruit trees and vegetables. Due to rainfall, iprodione often finds its way into water bodies, posing toxicity risks to non-target organisms and potentially entering the human food chain. However, there is limited information available regarding the developmental toxicity of iprodione specifically on the liver in existing literature. In this study, we employed larval and adult zebrafish as models to investigate the toxicity of iprodione. Our findings revealed that iprodione exposure led to yolk sac edema and increased mortality in zebrafish. Notably, iprodione exhibited specific effects on zebrafish liver development. Additionally, zebrafish exposed to iprodione experienced an overload of reactive oxygen species, resulting in the upregulation of p53 gene expression. This, in turn, triggered hepatocyte apoptosis and disrupted carbohydrate/lipid metabolism as well as energy demand systems. These results demonstrated the substantial impact of iprodione on zebrafish liver development and function. Furthermore, the application of astaxanthin (an antioxidant) and p53 morpholino partially mitigated the liver toxicity caused by iprodione. To summarize, iprodione induces apoptosis through the upregulation of p53 mediated by oxidative stress signals, leading to liver toxicity in zebrafish. Our study highlights that exposure to iprodione can result in hepatotoxicity in zebrafish, and it may potentially pose toxicity risks to other aquatic organisms and even humans.

CAS / MDL: 141517-21-7
研究领域: 毒理学 · 环境科学 · 分子生物学
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FOOD AND AGRICULTURAL IMMUNOLOGY · 2019 · IF 2.398

Rapid detection of iprodione in cucumber and apple using an immunochromatographic strip test

Pengjie Luo,Xia Chen,Jing Xiao,Yunfeng Zhao,Zifei Wang

In recent years, fungicides have been frequently detected in soil, water, and air because of their extensive use, with detrimental effects on the environment. Therefore, the screening of foods for iprodione (IPRO) has become mandatory. A rapid, specific and sensitive method based on an immunochromatographic strip test has been developed for the determination of IPRO residues in cucumber and apple. The antigen IPRO-ovalbumin and a goat anti-mouse IgG antibody were drawn onto a nitrocellulose membrane as the test and control lines, respectively. Under optimal conditions, the cut-off limits of the semi-quantitative strip test for IPRO was as low as 10 ng/mL in 0.01 M PBS pH 7.4, 20 ng/mL in cucumber juice, and 25 ng/mL in apple juice and the results were obtained within 5 min. Our data suggests that this immunochromatographic assay can be used for the sensitive, rapid, and specific on-site screening of IPRO in cucumber and apple.

CAS / MDL: 9003-99-0 54827-17-7
研究领域: 农业化学 · 食品安全 · 分析化学
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MICROCHEMICAL JOURNAL · 2025 · IF 5.1

Detection and classification of lambda-cyhalothrin and iprodione residues in tobacco leaves by SERS combined with supervised machine learning

Weifan Pan,Peng Zhang,Qi Wang,Liangguang Du,Xue Yang,Xiaowei Guo,Jiamin Yu

Surface-enhanced Raman spectroscopy (SERS) is a promising technique for the rapid detection of trace pesticide residues in agricultural produce. Currently, research is predominantly centered on utilizing SERS to discern residues characterized by non-overlapping spectral peaks. Yet, the technology encounters challenges in accurately identifying substances when their Raman spectral peaks overlap. In this paper, a silver nanoparticles (Ag Nps) modified SiO 2 nanoparticles (SiO 2 Nps) arrays composite SERS substrate (SiO 2 @Ag Nps-SERS substrate) was prepared for detecting lambda-cyhalothrin (LCT) and iprodione (IPR) residues in tobacco leaves, with detection limits of 0.1 ppm and 1 ppm respectively. And then, supervised machine learning models were used to identify and classify LCT and IPR residues with overlapped Raman characteristic peaks at 1000 cm −1 . Support Vector Machine (SVM), Partial Least Squares Discriminant Analysis (PLS-DA) and Decision Tree (DT) models could all accurately classify the spectral data of LCT and IPR residues, and the classification accuracy of SVM model was the highest, reaching 99.74 %. This capability is a testament to the innovative strength of our approach, highlighting its potential for universal applicability in spectral data analysis.

研究领域: 分析化学 · 农业科学 · 人工智能
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