ABSTRACT
Bacterial meningitis (BM) is a public health burden in developing countries, including Central Asia. This disease is characterized by a high mortality rate and serious neurological complications. Delay with the start of adequate therapy is associated with an increase in mortality for patients with acute bacterial meningitis. Cerebrospinal fluid culture, as a gold standard in bacterial meningitis diagnosis, is time-consuming with modest sensitivity, and this is unsuitable for timely decision-making. It has been shown that bacterial meningitis differentiation from viral meningitis could be done through different parameters such as clinical signs and symptoms, laboratory values, such as PCR, including blood and cerebrospinal fluid (CSF) analysis. In this study, we proposed the method for distinguishing the bacterial form of meningitis from enteroviral one. The method is based on the machine learning process deriving making decision rules. The proposed fast-and-frugal trees (FFTree) decision tree approach showed an ability to determine procalcitonin and C-reactive protein (CRP) with cut-off values for distinguishing between bacterial and enteroviral meningitis (EVM) in children. Such a method demonstrated 100% sensitivity, 96% specificity, and 98% accuracy in the differentiation of all cases of bacterial meningitis in this study. These findings and proposed method may be useful for clinicians to facilitate the decision-making process and optimize the diagnostics of meningitis.
Subject(s)
C-Reactive Protein/metabolism , Enterovirus Infections/diagnosis , Meningitis, Bacterial/diagnosis , Meningitis, Viral/diagnosis , Procalcitonin/blood , Biomarkers/blood , C-Reactive Protein/cerebrospinal fluid , Child , Child, Preschool , Clinical Decision-Making/methods , Decision Trees , Diagnosis, Differential , Enterovirus Infections/blood , Female , Humans , Infant , Machine Learning , Male , Meningitis, Bacterial/blood , Meningitis, Viral/blood , Procalcitonin/cerebrospinal fluid , Sensitivity and SpecificityABSTRACT
INTRODUCTION: Giardia intestinalis is the most important and common diarrhea-causing parasitic protozoa worldwide with growing clinical relevance in public health. There are many documented cases of G. intestinalis resistance to metronidazole (MZ). Pyruvate: ferredoxin oxidoreductase (PFOR), the membrane-localized enzyme, plays a key role in the development of resistance to drugs. The aim of the present study was to evaluate the difference in the levels of PFOR gene expression between MZ-resistant and MZ-susceptible strains of G. intestinatlis. METHODOLOGY: From 159 samples with G. intestinalis cysts, 48 strains were successfully cultivated. Using specific pair primers, PFOR gene expressions were estimated in different groups of Giardia. The polymerase chain reaction (PCR) data were analyzed with Bayesian analysis of qRT-PCR data using MCMC.qpcr package, with relative expression software tool (REST) and quantitative PCR CopyCount web source. RESULTS: In the group of Giardia with minimum inhibitory concentration (MIC) of 6.3 µM, the level of PFOR gene expression was downregulated and compared with controls, differed by 1.5 to 2.8 times. At the same time, there was no significant difference in PFOR gene expression between the control (susceptible) group and the group with MIC of 3.2 µM. CONCLUSIONS: Though there is association between PFOR gene expression and metronidazole resistance of Giardia intestinalis, the level of PFOR gene expression cannot be a strong genetic marker to predict level of resistance to metronidazole based on MICs.
