Fabrication of SiO2/PEGDA Inverse Opal Photonic Crystal with Fluorescence Enhancement Effects

The present paper reports the fabrication of inverse opal photonic crystals (IOPCs) by using SiO2 spherical particles with a diameter of 300 nm as an opal photonic crystal template and poly(ethylene glycol) diacrylate (PEGDA) as an inverse opal material. Characteristics and fluorescence properties of the fabricated IOPCs were investigated by using the Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), reflection spectroscopy, and fluorescence microscopy. The results clearly showed that the IOPCs were formed comprising of air spheres with a diameter of ∼270 nm. The decrease in size led to a decrease in the average refractive indexes from 1.40 to 1.12, and a remarkable stopband blue shift for the IOPCs was thus achieved. In addition, the obtained results also showed a fluorescence enhancement over 7.7-fold for the Fluor® 488 dye infiltrated onto the IOPCs sample in comparison with onto the control sample

Sensitivity and specificity of a novel classifier for the early diagnosis of dengue.

BACKGROUND:Dengue is the commonest arboviral disease of humans. An early and accurate diagnosis of dengue can support clinical management, surveillance and disease control and is central to achieving the World Health Organisation target of a 50% reduction in dengue case mortality by 2020. METHODS:5729 children with fever of <72 hrs duration were enrolled into this multicenter prospective study in southern Vietnam between 2010-2012. A composite of gold standard diagnostic tests identified 1692 dengue cases. Using statistical methods, a novel Early Dengue Classifier (EDC) was developed that used patient age, white blood cell count and platelet count to discriminate dengue cases from non-dengue cases. RESULTS:The EDC had a sensitivity of 74.8% (95%CI: 73.0-76.8%) and specificity of 76.3% (95%CI: 75.2-77.6%) for the diagnosis of dengue. As an adjunctive test alongside NS1 rapid testing, sensitivity of the composite test was 91.6% (95%CI: 90.4-92.9%). CONCLUSIONS:We demonstrate that the early diagnosis of dengue can be enhanced beyond the current standard of care using a simple evidence-based algorithm. The results should support patient management and clinical trials of specific therapies

Diagnostic performance of NS1 rapid test in enrolment plasma samples and odds of NS1 detection in relation to plasma viremia.

<p>^a Viremia measurement in the enrolment plasma sample (the same sample was also used for NS1 testing).</p><p>PPV: positive predictive value; NPV: negative predictive value; DENV: dengue virus; OR: odds ratios for detecting NS1 for each 10-fold higher DENV RNA concentration. There were 77 dengue cases where the infecting serotype was unknown.</p><p>Diagnostic performance of NS1 rapid test in enrolment plasma samples and odds of NS1 detection in relation to plasma viremia.</p

Univariate and multivariate analysis of candidate predictors of laboratory-confirmed dengue.

<p>BMI: body mass index; WBC: white blood cell count; PLT: platelet count; HCT: hematocrit; ALB: albumin; AST: aspartate aminotransferase; CK: creatine kinase</p><p>Univariate and multivariate analysis of candidate predictors of laboratory-confirmed dengue.</p

Performance of the Early Dengue Classifier (EDC) in all subjects.

<p>Figure A displays possible sensitivity/specificity trade-offs for different cut-off values and the distance from the corresponding points on the ROC curve to the upper left corner (perfect model). Figure B displays the receiver operating characteristic (ROC) curve. Figure C is a calibration plot. It displays a scatterplot-smoother of predicted versus observed risks (dotted line), predicted versus observed risks for ten patient strata of equal size grouped according to predicted risks (triangles) and the ideal identity line (dashed line). The rugs at the bottom of the graphs characterize the distribution of predicted risks in true dengue and non-dengue cases, respectively.</p

Nomogram of the Early Dengue Classifier (EDC) to predict the risk of dengue.

<p>A horizontal line from a predictor value to the “Points” axis assigns points to the 3 required variables age, platelet count (PLT), and white blood cell count (WBC). The sum of these points (total points) can then be translated to the corresponding predicted risk of dengue. As an example, a 9-year-old patient with a PLT of 100x10³/mm³, and a WBC of 5x10³/mm³ has a score of 15+32+84 = 131, and the corresponding risk of dengue is about 70%. Note: As <1% of patients had platelet (PLT) count >500x10³/mm³ or white blood cell (WBC) count >30x10³/mm³, for better visualization, PLT and WBC counts were truncated at 500x10³/mm³ and 30x10³/mm³ respectively.</p