Molecular and in silico analyses for detection of Shiga toxin-producing Escherichia coli (STEC) and highly pathogenic enterohemorrhagic Escherichia coli (EHEC) using genetic markers located on plasmid, O Island 57 and O Island 71.

BACKGROUND: Due to the diversity of Shiga toxin-producing Escherichia coli (STEC) isolates, detecting highly pathogenic strains in foodstuffs is challenging. Currently, reference protocols for STEC rely on the molecular detection of eae and the stx1 and/or stx2 genes, followed by the detection of serogroup-specific wzx or wzy genes related to the top 7 serogroups. However, these screening methods do not distinguish between samples in which a STEC possessing both determinants are present and those containing two or more organisms, each containing one of these genes. This study aimed to evaluate ecf1, Z2098, Z2099, and nleA genes as single markers and their combinations (ecf1/Z2098, ecf1/Z2099, ecf1/nleA, Z2098/Z2099, Z2098/nleA, and Z2099/nleA) as genetic markers to detect potentially pathogenic STEC by the polymerase chain reaction (PCR) in 96 animal samples, as well as in 52 whole genome sequences of human samples via in silico PCR analyses. RESULTS: In animal isolates, Z2098 and Z2098/Z2099 showed a strong association with the detected top 7 isolates, with 100% and 69.2% of them testing positive, respectively. In human isolates, Z2099 was detected in 95% of the top 7 HUS isolates, while Z2098/Z2099 and ecf1/Z2099 were detected in 87.5% of the top 7 HUS isolates. CONCLUSIONS: Overall, using a single gene marker, Z2098, Z2099, and ecf1 are sensitive targets for screening the top 7 STEC isolates, and the combination of Z2098/Z2099 offers a more targeted initial screening method to detect the top 7 STEC isolates. Detecting non-top 7 STEC in both animal and human samples proved challenging due to inconsistent characteristics associated with the genetic markers studied.

Experimental study on classical and metaheuristics algorithms for optimal nano-chitosan concentration selection in surface coating and food packaging.

In this study the Lagrange interpolation optimization algorithm based on two variables with respect to all experimental replicates (POA), was compared with two other heuristics methods (WOA and GOA). Modification of the apple surface by an edible nano coating solution in food packaging was used as case study. The experiment was performed as a factorial test based on completely randomized design by 100 permutations data sets. Results showed a significant difference between the three optimization methods (POA, WOA and GOA) which indicates the necessity of optimization and also efficiency of the present POA. The optimum result by POA, similar to a rose petal property, could rise 72% in surface contact angle (CA). The scanning electron microscopy (SEM) images of the derived surfaces showed almost a uniform spherical nanoparticles morphology. Remarkable advantages of this new approach are no additional material requirement, healthful, easy, inexpensive, fast and affordable technique for surface improvement.