Performance of the Hologic Panther Fusion® MRSA Assay for the nasal screening of methicillin-sensitive and methicillin-resistant Staphylococcus aureus carriage.

Staphylococcus aureus (SA) nasal carriage screening is usually based on either culture or molecular biology. The aim of the study was to evaluate the performance of the Panther Fusion® MRSA Assay (PF) that proposes a complete automation of the molecular screening for MSSA and MRSA carriage. Four hundred thirty-four nasal samples collected on ESwab™ were screened using PF. Results were compared with standard culture on BBL™ CHROMagar™ Staph aureus and chromID® MRSA agar. Discordant results were analyzed with additional techniques: Xpert SA Nasal Complete on GeneXpert (GX), culture on selective agar after 24 h in broth enrichment, and, if necessary, characterization of mec gene and SCCmec cassette using DNA microarray. The PF presented an overall agreement of 97.5% for SA detection and 97.9% for MRSA detection. Furthermore, 7.1% (31/434) of the samples were SA-negative in primary culture but SA-positive using PF and GX, confirming the greater sensitivity of molecular tests compared with culture. Of note, 4 out of 30 MRSA-positive samples were not detected due to an atypical SCCmec cassette, while 2 samples were falsely detected as MRSA due to co-colonization with a MSSA drop-out strain and a methicillin-resistant coagulase-negative staphylococcal strain. Considering all results, the PF instrument appears as a reliable and rapid (< 3 h) package for MSSA/MRSA nasal screening. This technology using random access capability and direct sampling of the primary container is innovative and corresponds therefore to a new step in complete molecular biology automation in bacteriology.

Spatial organization of chromosome territories in the interphase nucleus of trisomy 21 cells.

In the interphase cell nucleus, chromosomes adopt a conserved and non-random arrangement in subnuclear domains called chromosome territories (CTs). Whereas chromosome translocation can affect CT organization in tumor cell nuclei, little is known about how aneuploidies can impact CT organization. Here, we performed 3D-FISH on control and trisomic 21 nuclei to track the patterning of chromosome territories, focusing on the radial distribution of trisomic HSA21 as well as 11 disomic chromosomes. We have established an experimental design based on cultured chorionic villus cells which keep their original mesenchymal features including a characteristic ellipsoid nuclear morphology and a radial CT distribution that correlates with chromosome size. Our study suggests that in trisomy 21 nuclei, the extra HSA21 induces a shift of HSA1 and HSA3 CTs out toward a more peripheral position in nuclear space and a higher compaction of HSA1 and HSA17 CTs. We posit that the presence of a supernumerary chromosome 21 alters chromosome compaction and results in displacement of other chromosome territories from their usual nuclear position.

Deep Multi-Instance Learning Using Multi-Modal Data for Diagnosis of Lymphocytosis.

We investigate the use of recent advances in deep learning and propose an end-to-end trainable multi-instance convolutional neural network within a mixture-of-experts formulation that combines information from two types of data-images and clinical attributes-for the diagnosis of lymphocytosis. The convolutional network learns to extract meaningful features from images of blood cells using an embedding level approach and aggregates them. Moreover, the mixture-of-experts model combines information from these images as well as clinical attributes to form an end-to-end trainable pipeline for diagnosis of lymphocytosis. Our results demonstrate that even the convolutional network by itself is able to discover meaningful associations between the images and the diagnosis, indicating the presence of important unexploited information in the images. The mixture-of-experts formulation is shown to be more robust while maintaining performance via. a repeatability study to assess the effect of variability in data acquisition on the predictions. The proposed methods are compared with different methods from literature based both on conventional handcrafted features and machine learning, and on recent deep learning models based on attention mechanisms. Our method reports a balanced accuracy of [Formula: see text] and outperfroms the handcrafted feature-based and attention-based approaches as well that of biologists which scored [Formula: see text], [Formula: see text] and [Formula: see text] respectively. These results give insights on the potentials of the applicability of the proposed method in clinical practice. Our code and datasets can be found at https://github.com/msahasrabudhe/lymphoMIL.