ABSTRACT
Environmental water is considered one of the main vehicles for the transmission of antimicrobial resistance (AMR), posing an increasing threat to humans and animals health. Continuous efforts are being made to eliminate AMR; however, the detection of AMR pathogens from water samples often requires at least one culture step, which is time-consuming and can limit sensitivity. In this study, we employed comparative genomics to identify the prevalence of AMR genes within among: Escherichia coli, Klebsiella, Salmonella enterica and Acinetobacter, using publicly available genomes. The mcr-1, blaKPC (KPC-1 to KPC-4 alleles), blaOXA-48, blaOXA-23 and blaVIM (VIM-1 and VIM-2 alleles) genes are of great medical and veterinary significance, thus were selected as targets for the development of isothermal loop-mediated amplification (LAMP) detection assays. We also developed a rapid and sensitive sample preparation method for an integrated culture-independent LAMP-based detection from water samples. The developed assays successfully detected the five AMR gene markers from pond water within 1 h and were 100% sensitive and specific with a detection limit of 0.0625 µg/mL and 10 cfu/mL for genomic DNA and spiked bacterial cells, respectively. The integrated detection can be easily implemented in resource-limited areas to enhance One Health AMR surveillances and improve diagnostics.
Subject(s)
Anti-Bacterial Agents , Escherichia coli Proteins , Animals , Humans , Anti-Bacterial Agents/pharmacology , Drug Resistance, Bacterial , Nucleic Acid Amplification Techniques/methods , Escherichia coli , Water , Sensitivity and SpecificityABSTRACT
An artificial intelligence-assisted low-cost portable device for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is presented here. This standalone temperature-controlled device houses tubes designed for conducting reverse transcription loop-mediated isothermal amplification (RT-LAMP) assays. Moreover, the device utilises tubes illuminated by LEDs, an in-built camera, and a small onboard computer with automated image acquisition and processing algorithms. This intelligent device significantly reduces the normal assay run time and removes the subjectivity associated with operator interpretation of colourimetric RT-LAMP results. To further improve this device’s usability, a mobile app has been integrated into the system to control the LAMP assay environment and to visually display the assay results by connecting the device to a smartphone via Bluetooth. This study was undertaken using ~5000 images produced from the ~200 LAMP amplification assays using the prototype device. Synthetic RNA and a small panel of positive and negative SARS-CoV-2 patient samples were assayed for this study. State-of-the-art image processing and artificial intelligence algorithms were applied to these images to analyse them and to select the most efficient algorithm. The template matching algorithm for image extraction and MobileNet CNN architecture for classification results provided 98.0% accuracy with an average run time of 20 min to confirm the endpoint result. Two working points were chosen based on the best compromise between sensitivity and specificity. The high sensitivity point has a sensitivity value of 99.12% and specificity value of 70.8%, while at the high specificity point, the sensitivity is 96.05% and specificity 93.59%. Furthermore, this device provides an efficient and cost-effective platform for non-health professionals to detect not only SARS-CoV-2 but also other pathogens in resource-limited laboratories, factories, airports, schools, universities, and homes.