Schultz Bak (clothgym12)

cloud, released for research use in 2020). The platform combines Ares Genetics' proprietary database ARESdb, with state-of-the-art bioinformatics tools and curated public data. For identification, WGS showed 99 and 93% concordance with MALDI-TOF at the genus and species levels, respectively. WGS-predicted susceptibility showed 89% categorical agreement with phenotypic susceptibility across a total of 129 species-compound pairs analyzed, with categorical agreement exceeding 90% in 78 and reaching 100% in 32 species-compound pairs. Results of this study add to the growing body of literature showing that, with improvement of analytics, WGS data could be used to predict antimicrobial susceptibility. Copyright © 2020 Ferreira et al.Artificial intelligence (AI) is increasingly becoming an important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these solutions have the potential to improve a test's turnaround time, quality, and cost. Mathison's study used computer vision AI (https//doi.org/10.1128/JCM.02053-19), but additional opportunities for AI applications exist within the clinical microbiology laboratory. Large data sets within clinical microbiology that are amenable to the development of AI diagnostics include genomic information from isolated bacteria, metagenomic microbial findings from primary specimens, mass spectra captured from cultured bacterial isolates, and large digital images, which is the medium that Mathison chose to use. AI in general and computer vision in specific are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology. Copyright © 2020 American Society for Microbiology.Intestinal protozoa are responsible for relatively few infections in the developed world but the testing volume is disproportionately high. Manual light microscopy of stool remains the gold standard but can be insensitive, time consuming, and difficult to maintain competency. Artificial intelligence and digital slide scanning show promise for revolutionizing the clinical parasitology laboratory by augmenting detection of parasites and slide interpretation using a convolutional neural network (CNN) model. The goal of this study was to develop a sensitive model that could screen out negative trichrome slides, while flagging potential parasites for manual confirmation. Conventional protozoa were trained as "classes" in a deep CNN. Between 1394 and 23566 exemplars per class were used for training, based on specimen availability, from a minimum of 10 unique slides per class.. Scanning was performed using a 40X dry objective automated slide scanner. Data labeling was performed using a proprietary web interface. Clinical validation of the model was performed using 10 unique positive slides per class and 125 negative slides. Accuracy was calculated as slide-level agreement (e.g. parasite present or absent) with microscopy. Positive agreement was 98.88% [95% CI 93.76% to 99.98%] and negative agreement was 98.11% [95% CI 93.35% to 99.77%]. The model showed excellent reproducibility using slides containing multiple classes, a single class, or no parasites. The limit of detection of the model and scanner using serially diluted stool was 5-fold more sensitive than manual examinations by multiple parasitologists using 4 unique slide sets. Digital slide scanning and a CNN model are robust tools for augmenting the conventional detection of intestinal protozoa. Copyright © 2020 American Society for Microbiology.Applying dPCR technology to challenging clinical and industrial detection tasks has become more prevalent because of its capability for absolute quantification and rare target detection. However, practices learned from qPCR that promote assay robustness and wide-ranging utility are not readily applied in dPCR. These include internal amplification controls to account for false