Efficacy of 16S rRNA variable regions high-resolution melt analysis for bacterial pathogens identification in periprosthetic joint infections.

BACKGROUND: Accurate and rapid identification of microorganisms causing periprosthetic joint infections (PJIs) are necessary for choosing an appropriate antibiotic therapy. Therefore, molecular techniques are suggested for diagnosis in suspected PJIs. The Broad-range PCR and High-Resolution Melt Analysis (HRMA) were evaluated for the identification of causative organisms of PJIs in this study. RESULTS: For 47 of 63 specimens, both the culture and broad-range PCR were positive. The culture was found to be able of organism's detection in 74.6% (47/63) of patients. Of 47 positive cultures, 11 (23.4%) were polymicrobial and 36 (76.59%) were monomicrobial cultures, in which 34 (91.89%) cases were detected by HRM assay. The sensitivity, specificity of HRMA vs monomicrobial culture were 91.89, 93.75%, respectively. The sensitivity, specificity of total HRMA (mono + poly) vs culture were 82.92, 93.75%. CONCLUSIONS: HRM assay coupled with broad-range PCR are effective screening, rapid, and relatively cost-effective methods for discrimination of PJIs especially in aiding culture method. Using computer programs such as the Matlab-2018b program for HRM data analysis is also valuable and helpful in diagnosis.

Link prediction in real-world multiplex networks via layer reconstruction method.

Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed layer reconstruction method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.