Evaluation of Oxytetracycline Metabolites Cross-Reactivity with Oxytetracycline Enzyme-Linked Immunosorbent Assay (ELISA)

Hijaz, Faraj; Killiny, Nabil

doi:10.3390/antibiotics9040183

Cited by 7 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The europium assay showed high cross-reactivity with the main metabolite of OTC, 4- epi -OTC (50.0 ± 3.6%), whereas α-a po -OTC and β- apo -OTC showed very low cross-reactivity (<7.5%). In our previous study, 4- epi -OTC also showed high cross-reactivity with the Acell ELISA kit, whereas α- apo -OTC and β- apo -OTC metabolite did not show any cross-reactivity below 50 ng mL −1 [ 11 ]. Our current results indicated that 4- epi -OTC, which is an epimer of OTC, forms a stable complex with europium.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Development of Europium-Sensitized Fluorescence-Based Method for Sensitive Detection of Oxytetracycline in Citrus Tissues

et al. 2021

Self Cite

View full text Add to dashboard Cite

Antimicrobial compounds have been successfully used to control many plant and animal diseases. Recently, oxytetracycline (OTC) and streptomycin have been approved for the treatment of Huanglongbing in citrus. Since the application of OTC is under strict regulations, several methods have been developed to determine and monitor its levels in the environment including high-performance liquid chromatography, ELISA, colorimetric, and fluorometric assays. In this study, we developed a fluorometric method for the determination of OTC in plant tissues based on its complexation with europium. Our preliminary trials showed that phenols and flavonoids interfere with the europium assay by reacting with the sensitizing reagent, cetyltrimethylammonium chloride. Consequently, we used the 60 mg hydrophilic–lipophilic balanced (HLB) cartridges to purify the OTC from the plant matrix. The recovery of OTC from spiked leaf samples was 75 ± 7.6%. Using the 500 mg HLB, we were able to detect 0.3 ppm OTC in the final sample extract, which corresponds to 3 µg g−1 fresh weight (FWT). The developed method was successfully used to measure the level of OTC in leaves obtained from trunk-injected trees. The results obtained by the europium method were similar to those obtained using the ELISA assay. We also tested the cross-reactivity of OTC metabolites with the europium method. The 4-epi-OTC showed a high cross-reactivity (50.0 ± 3.6%) with europium assay, whereas α-apo-OTC and β-apo-OTC showed small cross-reactivity. We showed that the europium-sensitized fluorescence-based method can be successfully used to assess OTC in citrus plant tissues after a cleanup step. Our results showed that this method was sensitive, reproducible, and can be used to analyze many samples simultaneously.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The ACCEL ELISA assay is very sensitive but has a narrow linear range (1.56–50 ng mL −1 ). Although the OTC ELISA assay is fast and convenient, it is very expensive [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Development of Europium-Sensitized Fluorescence-Based Method for Sensitive Detection of Oxytetracycline in Citrus Tissues

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The inability to effectively prevent and control HLB remains a major hindrance to the global citrus industry's development. Currently, highaccuracy methods used for diagnosing HLB include the use of electron microscopy (Folimonova et al 2010), enzyme-linked immunosorbent assay (Hijaz et al 2020), conventional polymerase chain reaction (PCR) (Pandey et al 2021), and quantitative PCR (qPCR) (Kogenaru et al 2014). These methods are only suitable for laboratory testing, and they are time-consuming, costly, and require a high level of expertise, making them impractical for on-site detection.…”

Section: Introductionmentioning

confidence: 99%

A small neural network deployed on edge devices for detecting citrus Huanglongbing.

Tang,

Shi,

Chen

et al. 2024

Preprint

View full text Add to dashboard Cite

Citrus Huanglongbing (HLB) poses a significant threat to the profitability of the citrus industry worldwide. In traditional agricultural practices, manually identifying citrus trees infected with HLB based on certain leaf characteristics is time-consuming, subjective, and inefficient. The initial automatic identification of citrus Huanglongbing (HLB) relies on traditional image processing and machine learning algorithms, exhibiting low accuracy and slow processing speed. In order to enhance both the detection accuracy and speed, researchers have introduced deep learning methods based on neural networks for the identification of citrus HLB. However, the neural network models currently used for citrus leaf HLB identification have large parameter sizes, high deployment costs, and require high computational power, making them unsuitable for deployment on edge devices for field detection. Therefore, in order to promptly detect and address diseased plants, improve farmers' agricultural operational efficiency, ensure the accessibility of deep learning in small-scale agriculture, and address the need for cost-effective measures, there is an urgent need for a low-cost deep learning framework. Therefore, we compared the performance of several commonly used deep convolutional neural networks in industry for citrus Huanglongbing (HLB) detection. We constructed image classification networks based on AlexNet, ResNet, MobileNet-V1, and MobileNet-V3, and evaluated the network models based on model size, parameter count, and classification performance. As a result, we proposed a deep learning-based method for detecting citrus HLB. This method has a small model parameter count, low computational cost, fast detection speed, and high detection accuracy. It can be deployed on edge devices or other embedded devices. This method has a small model parameter count, fast detection speed, and high accuracy. The classification task is achieved by training the overall feature extraction network and the classification network at the network's tail on the constructed training set. The actual detection results show that the detection accuracy for healthy citrus leaves reaches 99.02%, and for HLB-infected leaves, the detection accuracy reaches 99.07%. The overall accuracy is 99.04%. Both recall and precision rates are excellent, meeting the precision requirements for on-site detection.

show abstract

Colorimetric aptasensor utilizing MOF-235 with exceptional peroxidase-like activity for the detection of oxytetracycline residues in raw milk

Wang,

Lu,

Zhang

et al. 2024

Food Chemistry

View full text Add to dashboard Cite

Evaluation of Oxytetracycline Metabolites Cross-Reactivity with Oxytetracycline Enzyme-Linked Immunosorbent Assay (ELISA)

Cited by 7 publications

References 11 publications

Development of Europium-Sensitized Fluorescence-Based Method for Sensitive Detection of Oxytetracycline in Citrus Tissues

Development of Europium-Sensitized Fluorescence-Based Method for Sensitive Detection of Oxytetracycline in Citrus Tissues

A small neural network deployed on edge devices for detecting citrus Huanglongbing.

Colorimetric aptasensor utilizing MOF-235 with exceptional peroxidase-like activity for the detection of oxytetracycline residues in raw milk

Contact Info

Product

Resources

About