Etiological diagnosis of diarrheal diseases may be complicated by their multi-factorial nature. In addition, Escherichia coli strains present in the gut can occasionally harbor virulence genes (VGs) without causing disease, which complicates the assessment of their clinical significance in particular. The aim of this study was to detect and quantify nine VGs (stx1, stx2, eae, aggR, ehxA, invA, est, elt and bfpA) typically present in five E. coli enteric pathotypes [enterohaemorrhagic E. coli (EHEC), enterotoxigenic E. coli (ETEC), enteropathogenic E. coli (EPEC), enteroaggregative E. coli (EAEC), and enteroinvasive E. coli (EIEC)] in fecal samples collected from 49 patients with acute diarrhea and 32 healthy controls from Madrid, Spain. In addition, the presence of four serotype-related genes (wzxO104 and fliCH4, rfbO157, and fliCH7) was also determined. Presence of target genes was assessed using a quantitative real-time PCR assay previously developed, and the association of presence and burden of VGs with clinical disease and/or other risk factors was explored. Prevalence of ehxA [typically associated with Shigatoxin producing E. coli (STEC) and (EPEC), invA (EIEC), and the rfbO157+fliCH7 (STEC)] combination were significantly (p < 0.02) higher in the diarrheic group, while the wzxO104+fliCH4 combination was significantly (p = 0.014) more prevalent in the control group. On the other hand, eae was detected in more than 90% of the individuals in both patient and control populations, and it was not associated with bfpA, suggesting the absence of typical EPEC. No significant differences in the quantitative values were detected for any VG among study groups, but the difference in the load of aggR (EAEC) and invA in the patients with respect to the controls was close to the significance, suggesting a potential role of these VGs in the clinical signs observed when they are present at high levels.
We have developed a simple and sensitive method for the detection of influenza A virus based on giant magnetoresistance (GMR) biosensor. This assay employs monoclonal antibodies to viral nucleoprotein (NP) in combination with magnetic nanoparticles (MNPs). Presence of influenza virus allows the binding of MNPs to the GMR sensor and the binding is proportional to the concentration of virus. Binding of MNPs onto the GMR sensor causes change in the resistance of sensor, which is measured in a real time electrical readout. GMR biosensor detected as low as 1.5 × 102 TCID50/mL virus and the signal intensity increased with increasing concentration of virus up to 1.0 × 105 TCID50/mL. This study showed that the GMR biosensor assay is relevant for diagnostic application since the virus concentration in nasal samples of influenza virus infected swine was reported to be in the range of 103 to 105 TCID50/mL.
Classical phylogenetic methods such as neighbor-joining or maximum likelihood trees, provide limited inferences about the evolution of important pathogens and ignore important evolutionary parameters and uncertainties, which in turn limits decision making related to surveillance, control, and prevention resources. Bayesian phylodynamic models have recently been used to test research hypotheses related to evolution of infectious agents. However, few studies have attempted to model the evolutionary dynamics of porcine reproductive and respiratory syndrome virus (PRRSV) and, to the authors' knowledge, no attempt has been made to use large volumes of routinely collected data, sometimes referred to as big data, in the context of animal disease surveillance. The objective of this study was to explore and discuss the applications of Bayesian phylodynamic methods for modeling the evolution and spread of a notable 1-7-4 RFLP-type PRRSV between 2014 and 2015. A convenience sample of 288 ORF5 sequences was collected from 5 swine production systems in the United States between September 2003 and March 2015. Using coalescence and discrete trait phylodynamic models, we were able to infer population growth and demographic history of the virus, identified the most likely ancestral system (root state posterior probability = 0.95) and revealed significant dispersal routes (Bayes factor > 6) of viral exchange among systems. Results indicate that currently circulating viruses are evolving rapidly, and show a higher level of relative genetic diversity over time, when compared to earlier relatives. Biological soundness of model results is supported by the finding that sow farms were responsible for PRRSV spread within the systems. Such results cannot be obtained by traditional phylogenetic methods, and therefore, our results provide a methodological framework for molecular epidemiological modeling of new PRRSV outbreaks and demonstrate the prospects of phylodynamic models to inform decision-making processes for routine surveillance and, ultimately, to support prevention and control of food animal disease at local and regional scales.