Inventory of Extended-Spectrum-ß-Lactamase-Producing Enterobacteriaceae in France as Assessed by a Multicenter Study.

The objective of this study was to perform an inventory of the extended-spectrum-ß-lactamase (ESBL)-producing Enterobacteriaceae isolates responsible for infections in French hospitals and to assess the mechanisms associated with ESBL diffusion. A total of 200 nonredundant ESBL-producing Enterobacteriaceae strains isolated from clinical samples were collected during a multicenter study performed in 18 representative French hospitals. Antibiotic resistance genes were identified by PCR and sequencing experiments. The clonal relatedness between isolates was investigated by the use of the DiversiLab system. ESBL-encoding plasmids were compared by PCR-based replicon typing and plasmid multilocus sequence typing. CTX-M-15, CTX-M-1, CTX-M-14, and SHV-12 were the most prevalent ESBLs (8% to 46.5%). The three CTX-M-type EBSLs were significantly observed in Escherichia coli (37.1%, 24.2%, and 21.8%, respectively), and CTX-M-15 was the predominant ESBL in Klebsiella pneumoniae (81.1%). SHV-12 was associated with ESBL-encoding Enterobacter cloacae strains (37.9%). qnrB, aac(6')-Ib-cr, and aac(3)-II genes were the main plasmid-mediated resistance genes, with prevalences ranging between 19.5% and 45% according to the ESBL results. Molecular typing did not identify wide clonal diffusion. Plasmid analysis suggested the diffusion of low numbers of ESBL-encoding plasmids, especially in K. pneumoniae and E. cloacae However, the ESBL-encoding genes were observed in different plasmid replicons according to the bacterial species. The prevalences of ESBL subtypes differ according to the Enterobacteriaceae species. Plasmid spread is a key determinant of this epidemiology, and the link observed between the ESBL-encoding plasmids and the bacterial host explains the differences observed in the Enterobacteriaceae species.

Brucella suis biovar 2 infection in humans in France: emerging infection or better recognition?

Brucellosis is usually acquired by humans through contact with infected animals or the consumption of raw milk from infected ruminants. Brucella suis biovar 2 (BSB2) is mainly encountered in hares and wild boars (Sus scrofa), and is known to have very low pathogenicity to humans with only two case reports published in the literature. Human cases of brucellosis caused by BSB2 were identified through the national mandatory notification of brucellosis. The identification of the bacterium species and biovar were confirmed by the national reference laboratory. Epidemiological data were obtained during medical follow-up visits. Seven human cases were identified between 2004 and 2016, all confirmed by the isolation of BSB2 in clinical specimens. All patients had direct contact with wild boars while hunting or preparing wild boar meat for consumption. Five patients had chronic medical conditions possibly responsible for an increased risk of infection. Our findings suggest that BSB2 might be an emerging pathogen in hunters with massive exposure through the dressing of wild boar carcasses. Hunters, especially those with chronic medical conditions, should be informed about the risk of BSB2 infection and should receive information on protective measures.

iDermatoPath - a novel software tool for mitosis detection in H&E-stained tissue sections of malignant melanoma.

BACKGROUND: Malignant Melanoma (MM) is characterized by a growing incidence and a high malignant potential. Besides well-defined prognostic factors such as tumour thickness and ulceration, the Mitotic Rate (MR) was included in the AJCC recommendations for diagnosis and treatment of MM. In daily routine, the identification of a single mitosis can be difficult on haematoxylin and eosin slides alone. Several studies showed a big inter- and intra-individual variability in detecting the MR in MM even by very experienced investigators, thus raising the question for a computer-assisted method. OBJECTIVE: The objective was to develop a software system for mitosis detection in MM on H&E slides based on machine learning for diagnostic support. METHODS: We developed a computer-aided staging support system based on image analysis and machine learning on the basis of 59 MM specimens. Our approach automatically detects tumour regions, identifies mitotic nuclei and classifies them with respect to their diagnostic relevance. A convenient user interface enables the investigator to browse through the proposed mitoses for fast and efficient diagnosing. RESULTS: A quantitative evaluation on manually labelled ground truth data revealed that the tumour region detection yields a medium spatial overlap index (dice coefficient) of 0.72. For the mitosis detection, we obtained high accuracies of above 83%. CONCLUSION: On the technical side, the developed iDermatoPath software tool provides a novel approach for mitosis detection in MM, which can be further improved using more training data such as dermatopathologist annotations. On the practical side, a first evaluation of the clinical utility was positive, albeit this approach provides most benefit for difficult cases in a research setting. Assuming all slides to be digitally processed and reported in the near future, this method could become a helpful additional tool for the pathologist.

In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium.

Scattered inflammatory cells are commonly observed in mammary gland tissue, most likely in response to normal cell turnover by proliferation and apoptosis, or as part of immunosurveillance. In contrast, lymphocytic lobulitis (LLO) is a recurrent inflammation pattern, characterized by lymphoid cells infiltrating lobular structures, that has been associated with increased familial breast cancer risk and immune responses to clinically manifest cancer. The mechanisms and pathogenic implications related to the inflammatory microenvironment in breast tissue are still poorly understood. Currently, the definition of inflammation is mainly descriptive, not allowing a clear distinction of LLO from physiological immunological responses and its role in oncogenesis remains unclear. To gain insights into the prognostic potential of inflammation, we developed an agent-based model of immune and epithelial cell interactions in breast lobular epithelium. Physiological parameters were calibrated from breast tissue samples of women who underwent reduction mammoplasty due to orthopedic or cosmetic reasons. The model allowed to investigate the impact of menstrual cycle length and hormone status on inflammatory responses to cell turnover in the breast tissue. Our findings suggested that the immunological context, defined by the immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches.