The dual-mode platform based on cysteamine-stabilized gold nanoparticles for the high throughput and on-site detection of bongkrekic acid

Zhang, Yuqi; Hou, Siyu; Song, Huilin; Luo, Xi; Wu, Dan; Zheng, Feng; Liu, Wenyuan; Ji, Shunli

doi:10.1016/j.foodcont.2022.108887

Cited by 8 publications

(1 citation statement)

References 33 publications

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“…It has been confirmed that the fatal toxicity associated with B. gladioli pv. cocovenenans comes from the two secondary metabolites toxoflavin [8] and bongkrekic acid [9] produced by the strain. Toxoflavin inhibits the respiratory chain and produces peroxide, which causes damage to vascular smooth muscle and organs [10,11].…”

Section: Introductionmentioning

confidence: 99%

Identification of Burkholderia gladioli pv. cocovenenans in Black Fungus and Efficient Recognition of Bongkrekic Acid and Toxoflavin Producing Phenotype by Back Propagation Neural Network

Niu,

Song,

Hao

et al. 2024

Foods

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Burkholderia gladioli pv. cocovenenans is a serious safety issue in black fungus due to the deadly toxin, bongkrekic acid. This has triggered the demand for an efficient toxigenic phenotype recognition method. The objective of this study is to develop an efficient method for the recognition of toxin-producing B. gladioli strains. The potential of multilocus sequence typing and a back propagation neural network for the recognition of toxigenic B. cocovenenans was explored for the first time. The virulent strains were isolated from a black fungus cultivation environment in Qinba Mountain area, Shaanxi, China. A comprehensive evaluation of toxigenic capability of 26 isolates were conducted using Ultra Performance Liquid Chromatography for determination of bongkrekic acid and toxoflavin production in different culturing conditions and foods. The isolates produced bongkrekic acid in the range of 0.05–6.24 mg/L in black fungus and a highly toxin-producing strain generated 201.86 mg/L bongkrekic acid and 45.26 mg/L toxoflavin in co-cultivation with Rhizopus oryzae on PDA medium. Multilocus sequence typing phylogeny (MLST) analysis showed that housekeeping gene sequences have a certain relationship with a strain toxigenic phenotype. We developed a well-trained, back-propagation neutral network for prediction of toxigenic phenotype in B. gladioli based on MLST sequences with an accuracy of 100% in the training set and an accuracy of 86.7% in external test set strains. The BP neutral network offers a highly efficient approach to predict toxigenic phenotype of strains and contributes to hazard detection and safety surveillance.

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