Identification of resistance in Escherichia coli and Klebsiella pneumoniae using excitation-emission matrix fluorescence spectroscopy and multivariate analysis.

Klebsiella pneumoniae and Escherichia coli are part of the Enterobacteriaceae family, being common sources of community and hospital infections and having high antimicrobial resistance. This resistance profile has become the main problem of public health infections. Determining whether a bacterium has resistance is critical to the correct treatment of the patient. Currently the method for determination of bacterial resistance used in laboratory routine is the antibiogram, whose time to obtain the results can vary from 1 to 3 days. An alternative method to perform this determination faster is excitation-emission matrix (EEM) fluorescence spectroscopy combined with multivariate classification methods. In this paper, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM), coupled with dimensionality reduction and variable selection algorithms: Principal Component Analysis (PCA), Genetic Algorithm (GA), and the Successive Projections Algorithm (SPA) were used. The most satisfactory models achieved sensitivity and specificity rates of 100% for all classes, both for E. coli and for K. pneumoniae. This finding demonstrates that the proposed methodology has promising potential in routine analyzes, streamlining the results and increasing the chances of treatment efficiency.

On the synergy between silver nanoparticles and doxycycline towards the inhibition of Staphylococcus aureus growth.

In a previous paper (RSC Adv., 2015, 5, 66886-66893), we showed that the combination of silver nanoparticles (NanoAg) with doxycycline (DO) culminated in an increased bactericidal activity towards E. coli. Herein we further investigated the metabolic changes that occurred on Staphylococcus aureus upon exposure to NanoAg with the help of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) coupled with multivariate data analysis. It has been discovered that the combination of DO with NanoAg produced metabolic changes in S. aureus that were not simply the overlap of the treatments with DO and NanoAg separately. Our results suggest that DO and NanoAg act synergistically to impede protein synthesis by the bacteria.