A novel indirect ELISA for diagnosis of dengue fever

Identification of visible and near-infrared signature peaks for arboviruses and Plasmodium falciparum

Arbovirus and malaria infections affect more than half of the world's population causing major financial and physical burden. Current diagnostic tools such as microscopy, molecular and serological techniques are technically demanding, costly, or time consuming. Near-infrared spectroscopy has recently been demonstrated as a potential diagnostic tool for malaria and Dengue virus and as a screening tool for disease vectors. However, pathogen specific absorption peaks that allow detection of these infections are yet to be described. In this study, we identified unique visible and near-infrared peaks from existing laboratory strains of four major arboviruses including Barmah Forest virus, Dengue virus, Ross River virus, Sindbis virus and Plasmodium falciparum. Secondly, to determine the diagnostic ability of these peaks, we developed machine learning algorithms using artificial neural networks to differentiate arboviruses from media in which they were grown. Signature peaks for BFV were identified within the visible region at 410, 430, 562 and 588 nm and the near-infrared region at, 946, 958, 1130, 1154 and 1780 nm. DENV related peaks were seen at 410nm within the visible region and 1130 nm within the near-infrared region. Signature peaks for Ross River virus were observed within the visible region at 410 and 430 nm and within the near-infrared region at 1130 and 1780 nm, while Sindbis virus had a prominent peak at 410 nm within the visible region. Peaks at 514, 528, 547, 561, 582, and 595 nm and peaks at 1388, 1432, 1681, 1700, 1721, 1882, 1905, 2245, 2278, 2300 nm were unique for P. falciparum. Near-infrared spectroscopy predictive sensitivity defined as the ability to predict an arbovirus as an infection was 90% (n=20) for Barmah Forest virus, 100% (n=10) for Ross River virus and 97.5% (n=40) for Dengue virus, while infection specificity defined as the ability to predict media as not-infected was 100% (n=10). Our findings indicate that spectral signatures obtained by near-infrared spectroscopy are potential biomarkers for diagnosis of arboviruses and malaria.

Analyzing the Molecular Signature Genes and Pathways of Dengue Fever, Dengue Hemorrhagic Fever and Dengue Shock Syndrome Caused by Dengue Virus in India

Dengue fever, dengue hemorrhagic dengue fever and dengue shock syndrome were caused by Dengue mosquito bites. Common signs such as fever and headache, are connected to distinctive medical disorders. From the previous and ongoing studies, it is far unknown what genes or protein signaling pathway mechanism underlies the association between DF, DHF and DSS in Indian context. In our study, the gene expression dataset was retrieved from the GEO database with accession number GSE94892. Here, mRNA sequence analysis done of each DF, DHF and DSS patients from peripheral blood mononuclear cells sample. GEO2R became used to carry out differential gene expression analysis using a dengue data set. Protein-protein interaction networks have been built, gene set GO enrichment and KEGG Pathway enrichment done in SR plot, and cluster analyses have been performed in STRING and MCODE. During this study, we diagnosed 10 hub genes in all 3 condition. The gene set of showed that the ten hub genes diagnosed in each condition constituted the best range of common hub genes discovered beneath all 3 conditions in India. The conclusion of this study can be beneficial for treating DF, DHF and DSS conditions within the context of handling DEV in India.

Assessing the Diagnostic Value of Mean Monocyte Volume and Hematological Parameters in Predicting Dengue Fever: A Cross-Sectional Analysis

BACKGROUND AND AIM

Distinguishing dengue fever (DF) from other viral infections solely based on common presentations poses a challenge. Therefore, there is a pressing need for additional diagnostic parameters that are reliable, swift, and cost-effective. This study aims to provide novel insights into the diagnostic value of hematological parameters, particularly mean monocyte volume (MMV), in predicting DF in Kelantan, Malaysia.

METHODOLOGY

This cross-sectional study enrolled 162 patients with suspected DF symptoms. The diagnosis was confirmed through dengue immunoglobulin M (IgM) capture enzyme-linked immunosorbent assay (ELISA) or Dengue Early ELISA for nonstructural protein 1 (NS1) antigen detection. Hematological parameters were measured using the Coulter DxH 800 hematology analyzer (Beckman Coulter, Brea, CA), and the statistical analysis was performed using SPSS version 22 (IBM Corp., Armonk, NY).

RESULTS

A total of 108 patients tested positive for DF, while 54 tested negative. We observed significant differences in WBC count, platelet count, and monocyte percentage between patients with DF and non-DF, while no significant correlation was noted for MMV. Subsequent statistical analysis, including receiver operating characteristic (ROC) curve analysis, revealed that monocyte percentage exhibited the largest area under the curve (0.715), indicating its potential as moderate discriminative power in diagnosing DF.

CONCLUSIONS

Our study findings indicate that monocyte percentage and MMV outcomes are insufficient for predicting DF, suggesting potential areas for further research.

Diagnosis of Imported Dengue and Zika Virus Infections in Italy from November 2015 to November 2022: Laboratory Surveillance Data from a National Reference Laboratory

Dengue (DENV) and Zika (ZIKV) viruses are mosquito-borne human pathogens. In Italy, the presence of the competent vector Aedes albopictus increases the risk of autochthonous transmission, and a national plan for arboviruses prevention, surveillance, and response (PNA 2020-2025) is in place. The results of laboratory diagnosis of both viruses by the National Reference Laboratory for arboviruses (NRLA) from November 2015 to November 2022 are presented. Samples from 655 suspected cases were tested by both molecular and serological assays. Virus and antibody kinetics, cross-reactivity, and diagnostic performance of IgM ELISA systems were analysed. Of 524 cases tested for DENV, 146 were classified as confirmed, 7 as probable, while 371 were excluded. Of 619 cases tested for ZIKV, 44 were classified as confirmed, while 492 were excluded. All cases were imported. Overall, 75.3% (110/146) of DENV and 50% (22/44) of ZIKV cases were confirmed through direct virus detection methods. High percentages of cross reactivity were observed between the two viruses. The median lag time from symptoms onset to sample collection was 7 days for both DENV molecular (range 0-20) and NS1 ELISA (range 0-48) tests, with high percentages of positivity also after 7 days (39% and 67%, respectively). For ZIKV, the median lag time was 5 days (range 0-22), with 16% positivity after 7 days. Diagnostic performance was assessed with negative predictive values ranging from 92% to 95% for the anti-DENV systems, and of 97% for the ZIKV one. Lower positive predictive values were seen in the tested population (DENV: 55% to 91%, ZIKV: 50%). DENV and ZIKV diagnosis by molecular test is the gold standard, but sample collection time is a limitation. Serological tests, including Plaque Reduction Neutralization Test, are thus necessary. Co-circulation and cross-reactivity between the two viruses increase diagnostic difficulty. Continuous evaluation of diagnostic strategies is essential to improve laboratory testing.

A Critical Analysis of Intracranial Hemorrhage as a Fatal Complication of Dengue Fever

Dengue fever is the most rapidly spreading mosquito-borne virus in the world, infecting about 100 million individuals. A rare but possibly dangerous consequence of dengue illness is intracranial hemorrhage (ICH). Currently, the pathogenesis of ICH is unknown. A number of studies have found a variety of risk factors for ICH in dengue. In addition, studies have reported the use of emergency surgery while monitoring thrombocytopenia in the therapy of dengue ICH. This review enumerates the potential predictors of ICH in dengue, discusses the use of brain imaging, and mentions the possibility of emergency surgery.

Use of Graphene and Its Derivatives for the Detection of Dengue Virus

Every year, the dengue virus and its principal mosquito vector, Aedes sp., have caused massive outbreaks, primarily in equatorial countries. The pre-existing techniques available for dengue detection are expensive and require trained personnel. Graphene and its derivatives have remarkable properties of electrical and thermal conductivity, and are flexible, light, and biocompatible, making them ideal platforms for biosensor development. The incorporation of these materials, along with appropriate nanomaterials, improves the quality of detection methods. Graphene can help overcome the difficulties associated with conventional techniques. In this review, we have given comprehensive details on current graphene-based diagnostics for dengue virus detection. We have also discussed state-of-the-art biosensing technologies and evaluated the advantages and disadvantages of the same.

Molecular characterization of recombinant premembrane protein of dengue virus serotype‐2 for development of diagnostic assay

Dengue is an acute arboviral infection common in tropical and subtropical countries. Dengue has been highlighted as a public health concern in the last five decades, affecting almost 50% of the population in developing nations. Dengue infection results in a complex symptomatic disease that ranges from headache, fever, and skin rash to extreme hemorrhage fever and liver dysfunction. The diagnosis of the disease is essential for effective treatment. The early onset of the infection can be detected through viral structural peptides that act as markers for detection, including Pre-Membrane (Pre-M) protein. In the currently proposed research, the structural gene obtained from local isolates was targeted for studies. For this purpose, recombinant structural protein Pre-M was amplified, cloned, and expressed in the bacterial expression system. The expression of the structural protein (Pre-M) was scrutinized by Sodium Dodecyl Sulphate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) and validated by western blot and dot blot, and afterwards, the antigen was purified. The purified Pre-M protein carries the potential for the development of in-house diagnostic assay as well as for vaccine production. This study aimed to develop a highly specific, sensitive, and cost-effective in-house enzyme-linked immunoassay (ELISA) for the detection of antibodies of Pakistani most prevalent dengue virus serotype 2 (DENV-2). The success of this research would also pave the way toward developing novel vaccines for the future prevention of dengue infection.

Diagnosis of dengue virus infection using spectroscopic images and deep learning

Dengue virus (DENV) infection is one of the major health issues and a substantial epidemic infectious human disease. More than two billion humans are living in dengue susceptible regions with annual infection mortality rate is about 5%-20%. At initial stages, it is difficult to differentiate dengue virus symptoms with other similar diseases. The main objective of this research is to diagnose dengue virus infection in human blood sera for better treatment and rehabilitation process. A novel and robust approach is proposed based on Raman spectroscopy and deep learning. In this regard, the ResNet101 deep learning model is modified by exploiting transfer learning (TL) concept on Raman spectroscopic data of human blood sera. Sample size was selected using standard statistical tests. The proposed model is evaluated on 2,000 Raman spectra images in which 1,200 are DENV-infected of human blood sera samples, and 800 are healthy ones. It offers 96.0% accuracy on testing data for DENV infection diagnosis. Moreover, the developed approach demonstrated minimum improvement of 6.0% and 7.0% in terms of AUC and Kappa index respectively over the other state-of-the-art techniques. The developed model offers superior performance to capture minute Raman spectral variations due to the better residual learning capability and generalization ability compared to others deep learning models. The developed model revealed that it might be applied for diagnosis of DENV infection to save precious human lives.

Linear and Continuous Flavivirus Epitopes From Naturally Infected Humans

This manuscript is an up-to-date review of experimentally validated linear and continuous epitopes identified from arbovirus members of the Flavivirus genus. We summarized 153 immunoreactive peptides from the Dengue virus, Zika virus, Japanese encephalitis virus, West Nile virus, and tick-borne encephalitis virus described in studies published from 1989 to 2020. We included peptides from structural (envelope, capsid, and pre-membrane) and nonstructural (Ns1–5) viral proteins that demonstrated relevant immunoreactivity with antibodies from naturally infected or vaccinated humans. We included peptides that demonstrated relevant reactivity features, such as indicators of disease severity related to immunological or immunopathological outcomes, differential or group diagnostic markers, immunotherapy candidates, and potential for vaccine formulation. The majority of immunoreactive peptides were described for DENV probably due to its long-lasting impact on human health and the lack of efficient vaccines and therapeutic methods. Immune landscape data regarding linear immunoreactive and continuous flavivirus peptides are still scarce, and a complete and more detailed map remains to be elucidated. Therefore, this review provides valuable data for those investigating the antibody response against flavivirus infection.

Dengue Detection: Advances in Diagnostic Tools from Conventional Technology to Point of Care

The dengue virus (DENV) is a vector-borne flavivirus that infects around 390 million individuals each year with 2.5 billion being in danger. Having access to testing is paramount in preventing future infections and receiving adequate treatment. Currently, there are numerous conventional methods for DENV testing, such as NS1 based antigen testing, IgM/IgG antibody testing, and Polymerase Chain Reaction (PCR). In addition, novel methods are emerging that can cut both cost and time. Such methods can be effective in rural and low-income areas throughout the world. In this paper, we discuss the structural evolution of the virus followed by a comprehensive review of current dengue detection strategies and methods that are being developed or commercialized. We also discuss the state of art biosensing technologies, evaluated their performance and outline strategies to address challenges posed by the disease. Further, we outline future guidelines for the improved usage of diagnostic tools during recurrence or future outbreaks of DENV.

A novel anti-NS2BNS3pro antibody-based indirect ELISA test for the diagnosis of dengue virus infections

Dengue virus reportedly circulates as four genetically distinct serotypes for which there is no widely accepted vaccine or drug at present. Morbidity and mortality caused by this virus are alarming for the possible increased threat to human health. A suitable diagnostic test is the prerequisite for designing and developing control measures. But, the tests being employed at present possess one or the other drawback for this disease diagnosis. During the dengue virus infections, NS2B is essential for the stability and catalytic activity of the NS3 protease. N-terminal 185 amino acids of NS3 protease domain along with hydrophilic portion of NS2B (NS2BNS3pro) is being used to screen dengue inhibitors but not for diagnosis until now. In the present study, we have used purified NS2BNS3pro as an antigen to trap anti-NS2BNS3pro antibodies of the clinical samples. Antibodies were detected successfully in both Western blot analysis and enzyme-linked immunosorbent assay (ELISA) tests. In ELISA, antibodies were detected in both primary and secondary infections of all serotypes. Interestingly, 17 samples declared as other febrile infections by NS1 and IgM/IgG tests were found to be positive in present test, which were further confirmed by reverse-transcription polymerase chain reaction. In silico studies suggested the absence of conserved epitopes between NS2BNS3pro and the counterpart in JEV, Zika, and CHIKV, indicating less possibility of crossreaction, which was in turn confirmed by using synthetic peptides representing the above epitopes. Statistical analysis with 76% specificity, 87% sensitivity, and 95% concordance also supported the present test as a suitable test for large scale diagnosis of dengue virus infections.

Specific diagnostic method for St. Louis encephalitis virus using a non-structural protein as the antigen

St. Louis encephalitis virus (SLEV) is a mosquito-borne re-emerging flavivirus in Argentina. It is currently necessary to develop specific serological tests that can efficiently discriminate the flaviviruses that circulate in our country. The immunoassays to diagnose SLEV lack specificity because they are based on the detection of structural viral proteins and the human immunoglobulins produced during infection against these proteins cross-react with other flaviviruses. Here, we describe an enzyme-immunoassay designed to detect human IgG antibodies specific to the viral non-structural protein NS5. The results indicate that NS5 is a promising antigen useful to discriminate SLEV from other circulating flaviviruses.

Identification of B-Cell Epitopes with Potential to Serologicaly Discrimnate Dengue from Zika Infections

Dengue is currently one of the most important arbovirus infections worldwide. Early diagnosis is important for disease outcome, particularly for those afflicted with the severe forms of infection. The goal of this work was to identify conserved and polymorphic linear B-cell Dengue virus (DENV) epitopes that could be used for diagnostic purposes. To this end, we aligned the predicted viral proteome of the four DENV serotype and performed in silico B-cell epitope mapping. We developed a script in Perl integrating alignment and prediction information to identify potential serotype-specific epitopes. We excluded epitopes that were similarly present in the yellow fever and zika viruses' proteomes. A total of 15 polymorphic and nine conserved peptides among DENV serotypes were selected. Peptides were spotted on cellulose membranes and tested against sera from rabbits that were monoinfected with each DENV serotype. Although serotype-specific peptides failed to recognize any sera, three conserved peptides were recognized by all anti-dengue sera and were included on an ELISA test employing a well-characterized human sera bank. Of the three peptides, one was able to efficiently identify sera from all four DENV serotypes and to discriminate them from Zika virus positive sera.