Dataset bordetella 2018

Dataset bordetella 201

Correlations of serum biomarker concentrations and IgM and IgG levels against <i>B</i>. <i>bacilliformis</i>.

<p>Only biomarkers with statistical significant associations with antibody levels are shown. P-values were computed through unadjusted linear regressions. The grey area shows the 95% confidence interval for predictions from the linear model.</p

Epidemiologic and demographic characteristics of study participants stratified by RT-PCR, IgM and IgG results.

<p>Epidemiologic and demographic characteristics of study participants stratified by RT-PCR, IgM and IgG results.</p

Correlations of eotaxin and EGF concentrations with <i>B</i>. <i>bacilliformis</i> bacteremia by RT-PCR.

<p>Scatter plots of subjects with detectable bacteremia by RT-PCR. Only biomarkers with statistical significant correlations with antibody levels are shown. <i>r</i>_s and P-values were computed through Spearman rank correlations. The grey area shows the 95% confidence interval for predictions from the linear model.</p

Serum biomarker concentrations according to <i>B</i>. <i>bacilliformis</i> IgM and IgG serostatus.

<p>Boxplots illustrate the 25^th and 75^th quartiles, and whiskers the percentile 10–90 of biomarkers significantly associated with IgM or IgG seropositivity. P-values were computed through unadjusted linear regressions.</p

Spearman correlations between biomarker concentrations and IgM and IgG levels for biomarkers found to be associated with antibody responses.

<p>Spearman correlations between biomarker concentrations and IgM and IgG levels for biomarkers found to be associated with antibody responses.</p

Combinations of markers associated with detectable <i>B</i>. <i>bacilliformis</i> bacteremia obtained through partial least squares discriminant analysis (PLS-DA).

<p>(A) Graphs of marker loadings to the 3 components of the PLS-DA. Bars quantify the importance (loadings) of each marker for the specific PLS-DA components that were significantly associated with RT-PCR results. Biomarkers that substantially contributed to the components (loadings > |0.3|) are highlighted in black. (B) PLS-DA plots representing each sample (dots) with respect the 3 first PLS-DA components.</p

Serum biomarker concentrations in subjects with and without detectable <i>B</i>. <i>bacilliformis</i> bacteremia.

<p>Boxplots illustrate the 25^th and 75^th quartiles, and whiskers the percentile 10–90 of biomarkers significantly associated with RT-PCR positivity. P-values were computed through unadjusted linear regressions.</p

Dataset OROV

<p><b>First outbreak of Oropouche Fever reported in a non-endemic central-western region of the Peruvian Amazon. </b><b>Molecular diagnosis and clinical characteristics. </b></p><p><b>Introduction: </b>Oropouche virus (OROV) is an underreported and emerging infectious disease. Its incidence is underestimated mainly due to clinical similarities with other diseases that are also caused by arboviruses present in endemic areas. We report the first outbreak of OROV in the western region of the Peruvian Amazon in the department of Huanuco, Peru. </p><p><b>Methods</b>: This outbreak occurred in the region of Huanuco, Peru during July of 2016. Blood samples were taken from 268 patients who presented acute febrile syndrome to be later analyzed for Oropouche Virus via Polymerase Chain Reaction.</p><p><b>Results</b>: Of all 268 patients, 46 (17%) cases tested were positive for OROV. the most common symptom reported was headaches with a frequency of 87% (n=40) followed by myalgias with 76% (n=35), arthralgias with 65.2% (n=30), retro-ocular pain 60.8% (n=28) and hyporexia with 50% (n=23). </p><p> Concerning signs and symptoms that may suggest severe OROV infection, 4.3% (n=2) had low platelet count, 8.6% (n=4) had intense abdominal pain, and only 2.1% (n=1) had a presentation with thoracic pain<br></p

Clinical symptoms in patients with arbovirus infection positive by PCR.

<p>Clinical symptoms in patients with arbovirus infection positive by PCR.</p