RESULTS: Unique and specific primer pairs were designed to amplify the 8 genes. The specificity of the primers was confirmed by DNA sequencing of the nanoplex PCR products and BLAST analysis. The sensitivity and specificity of V-BiA-RE nanoplex PCR assay was evaluated against the conventional culture method. The analytical sensitivity of the assay was found to be 1 ng at the DNA level while the analytical specificity was evaluated with 43 reference enterococci and non-enterococcal strains and was found to be 100%. The diagnostic accuracy was determined using 159 clinical specimens, which showed that 97% of the clinical isolates belonged to E. faecalis, of which 26% showed the HLGR genotype, but none were vancomycin resistant. The presence of an internal control in the V-BiA-RE nanoplex PCR assay helped us to rule out false negative cases.
CONCLUSION: The nanoplex PCR assay is robust and can give results within 4 hours about the 8 genes that are essential for the identification of the most common Enterococcus spp. and their antibiotic sensitivity pattern. The PCR assay developed in this study can be used as an effective surveillance tool to study the prevalence of enterococci and their antibiotic resistance pattern in hospitals and farm animals.
OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.
METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.
RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.
CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.
METHODS: Vancomycin at various concentrations was added to JectOS and polymethyl methacrylate (PMMA). Then, the cement was molded into standardized dimensions for in vitro testing. Cylindrical vancomycin-JectOS samples were subjected to compressive strength. The results obtained were compared to PMMA-vancomycin compressive strength data attained from historical controls. The zone of inhibition was carried out using vancomycin-JectOS and vancomycin-PMMA disk on methicillin-resistant strain culture agar.
RESULTS: With the addition of 2.5%, 5%, and 10% vancomycin, the average compressive strengths reduced to 8.01 ± 0.95 MPa (24.6%), 7.52 ± 0.71 MPa (29.2%), and 7.23 ± 1.34 MPa (31.9%). Addition of vancomycin significantly weakened biomechanical properties of JectOS, but there was no significant difference in the compressive strength at increasing concentrations. The average diameters of zone of inhibition for JectOS-vancomycin were 24.7 ± 1.44 (2.5%) mm, 25.9 ± 0.85 mm (5%), and 26.8 ± 1.81 mm (10%), which outperformed PMMA.
CONCLUSION: JectOS has poor mechanical performance but superior elution property. JectOS-vancomycin cement is suitable as a void filler delivering high local concentration of vancomycin. We recommended using it for contained bone defects that do not require mechanical strength.