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BackgroundThe epidemic Clostridioides difficile ribotype 027 strain resulted from the dissemination of 2 separate fluoroquinolone-resistant lineages: FQR1 and FQR2. Both lineages were reported to originate in North America; however, confirmatory large-scale investigations of C difficile ribotype 027 epidemiology using whole genome sequencing has not been undertaken in the United States.MethodsWhole genome sequencing and single-nucleotide polymorphism (SNP) analysis was performed on 76 clinical ribotype 027 isolates obtained from hospitalized patients in Texas with C difficile infection and compared with 32 previously sequenced worldwide strains. Maximum-likelihood phylogeny based on a set of core genome SNPs was used to construct phylogenetic trees investigating strain macro- and microevolution. Bayesian phylogenetic and phylogeographic analyses were used to incorporate temporal and geographic variables with the SNP strain analysis.ResultsWhole genome sequence analysis identified 2841 SNPs including 900 nonsynonymous mutations, 1404 synonymous substitutions, and 537 intergenic changes. Phylogenetic analysis separated the strains into 2 prominent groups, which grossly differed by 28 SNPs: the FQR1 and FQR2 lineages. Five isolates were identified as pre-epidemic strains. Phylogeny demonstrated unique clustering and resistance genes in Texas strains indicating that spatiotemporal bias has defined the microevolution of ribotype 027 genetics.Conclusions Clostridioides difficile ribotype 027 lineages emerged earlier than previously reported, coinciding with increased use of fluoroquinolones. Both FQR1 and FQR2 ribotype 027 epidemic lineages are present in Texas, but they have evolved geographically to represent region-specific public health threats.

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Objective: This article describes a CDI outbreak in a long-term care (LTC) facility that used molecular typing techniques and whole-genome sequencing to identify widespread dissemination of the clonal strain in the environment which was successfully removed after terminal cleaning. Setting: This study was conducted in a long-term care facility in Texas. Methods: A recently hospitalized LTC patient was diagnosed with CDI followed shortly thereafter by 7 subsequent CDI cases. A stool specimen was obtained from each patient for culturing and typing. An environmental point-prevalence study of the facility was conducted before and after terminal cleaning of the facility to assess environmental contamination. Cultured isolates were typed using ribotyping, multilocus variant analysis, and whole-genome sequencing. Results: Stool samples were available for 5 of 8 patients; of these specimens, 4 grew toxigenic C. difficile ribotype 027. Of 50 environmental swab samples collected throughout the facility prior to the facility-wide terminal cleaning, 19 (38%) grew toxigenic C. difficile (most commonly ribotype 027, 79%). The terminal cleaning was effective at reducing C. difficile spores in the environment and at eradicating the ribotype 027 strain (P < .001). Using multilocus variance analysis and whole-genome sequencing, clinical and environmental strains were highly related and, in some cases, were identical. Conclusion: Using molecular typing techniques, we demonstrated reduced environmental contamination with toxigenic C. difficile and the eradication of a ribotype 027 clone. These techniques may help direct infection control efforts and decrease the burden of CDI in the healthcare system.

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Clostridioides difficile infection (C. diff) is the most common cause of death due to secondary infection in hospital patients in the United States. Detection of C. diff cells in scanning electron microscopy (SEM) images is an important task to quantify the efficacy of the under-development treatments. However, detecting C. diff cells in SEM images is a challenging problem due to the presence of inhomogeneous illumination and occlusion. An Illumination normalization pre-processing step destroys the texture and adds noise to the image. Furthermore, cells are often clustered together resulting in touching cells and occlusion. In this paper, DETCID, a deep cell detection method using adversarial training, specifically robust to inhomogeneous illumination and occlusion, is proposed. An adversarial network is developed to provide region proposals and pass the proposals to a feature extraction network. Furthermore, a modified IoU metric is developed to allow the detection of touching cells in various orientations. The results indicate that DETCID outperforms the state-of-the-art in detection of touching cells in SEM images by at least 20 percent improvement of mean average precision.

Uniting data-centric perspectives and concepts to trace the foundations of DCAI.