Abstract
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.
Highlights
Klebsiella pneumoniae and Escherichia coli are part of the Enterobacteriaceae family, being common sources of community and hospital infections and having high antimicrobial resistance
Klebsiella pneumoniae samples belonged to three groups, which were named as: Control (ATCC 1706—sensitive samples), resistant 1 (CCBH 6633—samples that show resistance to carbapenems) and resistant 2 (CCBH 4955 KPC—samples resistant to carbapenems, cephalosporins, penicillin)
The excitation-emission matrix (EEM) data obtained for Escherichia coli: sensitive (Fig. 2a), NDM (Fig. 2b) and CCBH 7018 (Fig. 2c) are presents in Fig. 2, after spectral pre-processing
Summary
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. 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. The antibiotic sensitivity behavior of the isolated strains can be determined by disc diffusion method[15], such as Minimal Inhibitory Concentrations (MIC)[16] or Minimal Bactericidal Concentrations (MBC)[17]
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