A Label and Probe-Free Zika Virus Immunosensor Prussian Blue@carbon Nanotube-Based for Amperometric Detection of the NS2B Protein.

Zika virus (ZIKV) is a mosquito-borne infection, predominant in tropical and subtropical regions causing international concern due to the ZIKV disease having been associated with congenital disabilities, especially microcephaly and other congenital abnormalities in the fetus and newborns. Development of strategies that minimize the devastating impact by monitoring and preventing ZIKV transmission through sexual intercourse, especially in pregnant women, since no vaccine is yet available for the prevention or treatment, is critically important. ZIKV infection is generally asymptomatic and cross-reactivity with dengue virus (DENV) is a global concern. An innovative screen-printed electrode (SPE) was developed for amperometric detection of the non-structural protein (NS2B) of ZIKV by exploring the intrinsic redox catalytic activity of Prussian blue (PB), incorporated into a carbon nanotube-polypyrrole composite. Thus, this immunosensor has the advantage of electrochemical detection without adding any redox-probe solution (probe-less detection), allowing a point-of-care diagnosis. It was responsive to serum samples of only ZIKV positive patients and non-responsive to negative ZIKV patients, even if the sample was DENV positive, indicating a possible differential diagnosis between them by NS2B. All samples used here were confirmed by CDC protocols, and immunosensor responses were also checked in the supernatant of C6/36 and in Vero cell cultures infected with ZIKV.

NS1 glycoprotein detection in serum and urine as an electrochemical screening immunosensor for dengue and Zika virus.

The incidence of infection by the dengue virus (DENV) has grown dramatically, reaching 128 countries in tropical and subtropical regions worldwide, with a pattern of hyper-endemicity. DENV is a mosquito-borne disease having four serotypes, one or two circulating in epidemic outbreaks. The diagnosis of DENV is challenging mainly due to the circulation of new viruses with remarkable similarities, such as Zika (ZIKV) that may cause fetal microcephaly. DENV affects 390 million people per year, but these numbers may be higher due to the underreported and misclassified cases. Recently, the NS1 nonstructural protein has been described in serum and urine of DENV and ZIKV patients, suggesting its use as a biomarker for screening since a negative NS1 sample confirms the absence of these infections. Herein, a label-free immunosensor comprising an assembled nanostructured thin film of carbon nanotube-ethylenediamine is described. The advantage of in situ electrosynthesis of polymer film is to allow major control of thickness and conductivity, in addition to designing the reactive groups for functionalization. A quartz crystal microbalance system was used to estimate the thickness of the polymeric film obtained. The anti-NS1 monoclonal antibodies were immobilized to carbon nanotubes by covalent linkage, permitting a high stability during measurements. Analytical responses to NS1 were obtained by differential pulse voltammetry (DPV), showing a linear range from 20 to 800 ng mL-1 and reproducibility of 3.0%, with a limit of detection (LOD) of 6.8 ng mL- 1. This immunosensor was capable of detecting ZIKV and DENV NS1 in spiked urine and real serum in a clinical range.Graphical abstract.

A new algorithm for segmenting and counting Aedes aegypti eggs in ovitraps.

Dengue fever has become a major international public health concern in recent decades. As dengue fever not have available vaccine or specific treatment, the only known form to prevent the illness is by applying strategies to control its vector, the Aedes aegypti mosquito. Ovitraps, special traps to collect mosquito eggs, are used to detect Aedes aegypti presence and to approximate the gauge of the adult mosquitoes population in the environment by counting the number of eggs laid in an trap. This counting is usually performed in a manual, visual and non-automatic form. This work proposes a new automatic method to automatically count the number of eggs in digital images of ovitraps based on image processing techniques (color systems exploration) and k-Means clustering algorithm. The proposed method performs an improvement on the results when compared with previous studies.

Image segmentation of ovitraps for automatic counting of Aedes Aegypti eggs.

The Aedes Aegypti mosquito is the vector of the most difficult public health problems in tropical and semi-tropical world: the epidemic proliferation of dengue, a viral disease that can cause human beings death specially in its most dangerous form, dengue haemorrhagic fever. One of the most useful methods for mosquito detection and surveillance is the ovitraps: special traps to collect eggs of the mosquito. It is very important to count the number of Aedes Aegypti eggs present in ovitraps. This counting is usually performed in a manual, visual and non-automatic form. This work approaches the development of automatic methods to count the number of eggs in ovitraps images using image processing, particularly color segmentation and mathematical morphology-based non-linear filters.