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In this cluster-randomized trial conducted in Indonesia, deployment of mosquitoes infected with the w Mel strain of Wolbachia pipientis resulted in fewer symptomatic, virologically confirmed dengue infections and hospitalizations among residents.

Infection with any of the 4 dengue virus serotypes results in a diverse range of symptoms, from mild undifferentiated fever to life-threatening hemorrhagic fever and shock. Given that dengue virus infection elicits such a broad range of clinical symptoms, early and accurate laboratory diagnosis is essential for appropriate patient management. Virus detection and serological conversion have been the main targets of diagnostic assessment for many years, however cross-reactivity of antibody responses among the flaviviruses has been a confounding issue in providing a differential diagnosis. Furthermore, there is no single, definitive diagnostic biomarker that is present across the entire period of patient presentation, particularly in those experiencing a secondary dengue infection. Nevertheless, the development and commercialization of point-of-care combination tests capable of detecting markers of infection present during different stages of infection (viral nonstructural protein 1 and immunoglobulin M) has greatly simplified laboratory-based dengue diagnosis. Despite these advances, significant challenges remain in the clinical management of dengue-infected patients, especially in the absence of reliable biomarkers that provide an effective prognostic indicator of severe disease progression. This review briefly summarizes some of the complexities and issues surrounding clinical dengue diagnosis and the laboratory diagnostic options currently available.

The tetravalent dengue vaccine (CYD-TDV) has been shown to provide protection against dengue disease over 5-year follow-up in participants with previous dengue infection, but increased the risk of dengue hospitalisation and severe dengue during long-term follow-up in those without previous dengue infection. WHO recommended pre-vaccination screening to identify those with previous dengue infection (ie, dengue seropositive) who would benefit from vaccination. We re-evaluated CYD-TDV efficacy in those identified as dengue seropositive using five commercially available immunoassays, and assessed immunoassay performance. We included participants in the immunogenicity subsets of the phase 3 CYD14 (NCT01373281) and CYD15 (NCT01374516) CYD-TDV efficacy trials, which enrolled children aged 2–16 years in 2011–12 in five countries in the Asia-Pacific region (CYD14) and five Latin American countries (CYD15). Participants assessed had received at least one injection of study drug (CYD-TDV or placebo) and had baseline samples available. We tested baseline samples by IgG-based immunoassays to classify baseline dengue serostatus, using two ELISAs (EUROIMMUN and Panbio) and three rapid diagnostic tests (RDTs; TELL ME FAST, SD BIOLINE, and OnSite). Vaccine efficacy in preventing symptomatic, hospitalised, and severe virologically confirmed dengue was determined for participants who tested positive by each immunoassay. The specificity and sensitivity of each immunoassay was determined as percentage negative and positive agreement compared with the reference algorithm, which used dengue plaque reduction neutralisation test with 50% and 90% cutoffs and non-structural protein 1 IgG ELISA results to assign baseline serostatus. Samples were available for 3967 participants, 2735 (69·0%) of whom were classified as seropositive by the reference algorithm. Vaccine efficacy against symptomatic virologically confirmed dengue in immunoassay-positive participants was high across all five immunoassays (EUROIMMUN ELISA 88·2% [95% CI 77·3 to 93·9], Panbio ELISA 87·6% [76·7 to 93·4], TELL ME FAST RDT 88·8% [67·0 to 96·2], SD BIOLINE RDT 82·8% [66·9 to 91·1], and OnSite RDT 89·7% [64·6 to 97·0]), as was vaccine efficacy against hospitalised virologically confirmed dengue (EUROIMMUN-ELISA 72·8% [38·9 to 87·9], Panbio ELISA 77·5% [52·8 to 89·3], TELL ME FAST RDT 92·4% [37·8 to 99·1], SD BIOLINE RDT 87·2% [54·5 to 96·4], and OnSite RDT 73·7% [–5·1 to 93·4]) and severe virologically confirmed dengue (EUROIMMUN ELISA 86·9% [–16·8 to 98·5], Panbio ELISA 91·3% [27·6 to 99·0], TELL ME FAST RDT 100·0% [not estimable to 100·0%], SD BIOLINE RDT 89·4% [9·6 to 98·8], and OnSite RDT 73·4% [–193·7 to 97·6]). The immunoassays exhibited high specificity (≥98·8% for all immunoassays apart from SD BIOLINE RDT) but variable sensitivities, with higher sensitivities observed for the ELISAs (EUROIMMUN 89·2% [87·9 to 90·3] and Panbio 92·5 [91·4 to 93·5]) than the RDTs (TELL ME FAST 52·5% [50·6 to 54·4], SD BIOLINE 71·1% [69·3 to 72·8], and OnSite 47·6% [45·7 to 49·5]). Our findings suggest that these immunoassays could be used for pre-vaccination screening for CYD-TDV as tools to assist risk stratification until more sensitive and convenient tests become available. Sanofi Pasteur.

Dengue is a major public health issue worldwide and severe dengue (SD) is life threatening. It is critical to triage patients with dengue infection in the early stage. However, there is limited knowledge on early indicators of SD. The objective of this study is to identify risk factors for the prognosis of SD and try to find out some potential predictive factors for SD from dengue fever (DF) in the early of infection. The PubMed, Cochrane Library and Web of Science databases were searched for relevant studies from June 1999 to December 2020. The pooled odds ratio (OR) or standardized mean difference (SMD) with 95% confidence intervals (CI) of identified factors was calculated using a fixed or random effect model in the meta-analysis. Tests for heterogeneity, publication bias, subgroup analyses, meta-regression, and a sensitivity analysis were further performed. A total of 6,848 candidate articles were retrieved, 87 studies with 35,184 DF and 8,173 SD cases met the eligibility criteria. A total of 64 factors were identified, including population and virus characteristics, clinical symptoms and signs, laboratory biomarkers, cytokines, and chemokines; of these factors, 34 were found to be significantly different between DF and SD, while the other 30 factors were not significantly different between the two groups after pooling the data from the relevant studies. Additionally, 9 factors were positive associated with SD within 7 days after illness when the timing subgroup analysis were performed. Practical factors and biomarkers for the identification of SD were established, which will be helpful for a prompt diagnosis and early effective treatment for those at greatest risk. These outcomes also enhance our knowledge of the clinical manifestations and pathogenesis of SD.

Dengue laboratory diagnosis is essentially based on detection of the virus, its components or antibodies directed against the virus in blood samples. Blood, however, may be difficult to draw in some patients, especially in children, and sampling during outbreak investigations or epidemiological studies may face logistical challenges or limited compliance to invasive procedures from subjects. The aim of this study was to assess the possibility of using saliva and urine samples instead of blood for dengue diagnosis. Serial plasma, urine and saliva samples were collected at several time-points between the day of admission to hospital until three months after the onset of fever in children with confirmed dengue disease. Quantitative RT-PCR, NS1 antigen capture and ELISA serology for anti-DENV antibody (IgG, IgM and IgA) detection were performed in parallel on the three body fluids. RT-PCR and NS1 tests demonstrated an overall sensitivity of 85.4%/63.4%, 41.6%/14.5% and 39%/28.3%, in plasma, urine and saliva specimens, respectively. When urine and saliva samples were collected at the same time-points and tested concurrently, the diagnostic sensitivity of RNA and NS1 detection assays was 69.1% and 34.4%, respectively. IgG/IgA detection assays had an overall sensitivity of 54.4%/37.4%, 38.5%/26.8% and 52.9%/28.6% in plasma, urine and saliva specimens, respectively. IgM were detected in 38.1% and 36% of the plasma and saliva samples but never in urine. Although the performances of the different diagnostic methods were not as good in saliva and urine as in plasma specimens, the results obtained by qRT-PCR and by anti-DENV antibody ELISA could well justify the use of these two body fluids to detect dengue infection in situations when the collection of blood specimens is not possible.

Approximately, half of the population in the world including tropical and sub-tropical climates region is at risk of dengue. Being an endemic country, Bangladesh has experienced the largest dengue epidemic in 2019. The present study aimed at evaluating the clinical and laboratory profile of dengue patients in northern Bangladesh during the epidemic. This cross-sectional study included 319 serologically confirmed dengue patients admitted in Shaheed Ziaur Rahman Medical College Hospital in Bogra district. It is one of the main tertiary care hospitals in northern Bangladesh. Data were collected from July to September 2019. Patients' clinical and laboratory data were extracted from clinical records. Patients were classified into two classes according to the WHO 2009 dengue classification such as (i) non-severe dengue and (ii) severe dengue. Chi-square test and independent t-test were used in this study. Of the 319 patients, 94.1% had non-severe dengue and the remaining 5.9% had severe dengue (severe plasma leakage 68.4%, severe organ involvement 68.4%, and severe clinical bleeding 10.5%). Most of the patients were suffering from primary dengue infection. The most common clinical presentation was fever followed by headache and myalgia. Vomiting and abdominal pain were the most prevalent warning signs. The common hematological findings on admission were leukopenia (63.3%), thrombocytopenia (30.4%) and increased hematocrit (26.6%). Raised serum ALT or AST was observed in 14.1% cases whereas raised serum creatinine was observed in 6.6% cases. Signs of plasma leakage (pleural effusion, respiratory distress, and ascites, rise of hematocrit >20% during hospital stay) and hepatic or renal involvement (serum ALT >42UI/L or serum creatinine >1.2 mg/dL) on admission were mostly associated with severe dengue. The study provides clinical evidence on presentation as well as hematological and biochemical profile of dengue patients in northern Bangladesh that should be implicated in effective patient management.

Establishing reliable early warning models for severe dengue cases is a high priority to facilitate triage in dengue-endemic areas and optimal use of limited resources. However, few studies have identified the complex interactive relationship between potential risk factors and severe dengue. This research aimed to assess the potential risk factors and detect their high-order combinative effects on severe dengue. A structured questionnaire was used to collect detailed dengue outbreak data from eight representative hospitals in Dhaka, Bangladesh, in 2019. Logistic regression and machine learning models were used to examine the complex effects of demographic characteristics, clinical symptoms, and biochemical markers on severe dengue. A total of 1,090 dengue cases (158 severe and 932 non-severe) were included in this study. Dyspnoea (Odds Ratio [OR] = 2.87, 95% Confidence Interval [CI]: 1.72 to 4.77), plasma leakage (OR = 3.61, 95% CI: 2.12 to 6.15), and hemorrhage (OR = 2.33, 95% CI: 1.46 to 3.73) were positively and significantly associated with the occurrence of severe dengue. Classification and regression tree models showed that the probability of occurrence of severe dengue cases ranged from 7% (age >12.5 years without plasma leakage) to 92.9% (age ≤12.5 years with dyspnoea and plasma leakage). The random forest model indicated that age was the most important factor in predicting severe dengue, followed by education, plasma leakage, platelet, and dyspnoea. The research provides new evidence to identify key risk factors contributing to severe dengue cases, which could be beneficial to clinical doctors to identify and predict the severity of dengue early.

Background The incidence of dengue is rising steadily in Malaysia since the first major outbreak in 1973. Despite aggressive measures taken by the relevant authorities, Malaysia is still facing worsening dengue crisis over the past few years. There is an urgent need to evaluate dengue cases for better understanding of clinic-laboratory spectrum in order to combat this disease. Methods A retrospective analysis of dengue patients admitted to a tertiary care teaching hospital during the period of six years (2008 – 2013) was performed. Patient’s demographics, clinical and laboratory findings were recorded via structured data collection form. Patients were categorized into dengue fever (DF) and dengue hemorrhagic fever (DHF). Appropriate statistical methods were used to compare these two groups in order to determine difference in clinico-laboratory characteristics and to identify independent risk factors of DHF. Results A total 667 dengue patients (30.69 ± 16.13 years; Male: 56.7 %) were reviewed. Typical manifestations of dengue like fever, myalgia, arthralgia, headache, vomiting, abdominal pain and skin rash were observed in more than 40 % patients. DHF was observed in 79 (11.8 %) cases. Skin rash, dehydration, shortness of breath, pleural effusion and thick gall bladder were more significantly ( P  < 0.05) associated with DHF than DF. Multivariate regression analysis demonstrated presence of age > 40 years (OR: 4.1, P  < 0.001), secondary infection (OR: 2.7, P  = 0.042), diabetes mellitus (OR: 2.8, P  = 0.041), lethargy (OR: 3.1, P  = 0.005), thick gallbladder (OR: 1.7, P  = 0.029) and delayed hospitalization (OR: 2.3, P  = 0.037) as independent predictors of DHF. Overall mortality was 1.2 % in our study. Conclusions Current study demonstrated that DF and DHF present significantly different clinico-laboratory profile. Older age, secondary infection, diabetes mellitus, lethargy, thick gallbladder and delayed hospitalization significantly predict DHF. Prior knowledge of expected clinical profile and predictors of DHF/DSS development would provide information to identify individuals at higher risk and on the other hand, give sufficient time to clinicians for reducing dengue related morbidity and mortality.

Dengue virus (DENV) infection is a major global health problem. While DENV infection rarely results in serious complications, the more severe illness dengue hemorrhagic fever (DHF) has a significant mortality rate due to the associated plasma leakage that may lead to hypovolemic shock. Proper care thus requires identifying patients with DHF among those with suspected dengue so that they can be provided with adequate and prompt fluid replacement. In this study we used seventeen years of pediatric patient data from a prospective cohort study in two hospitals in Thailand to develop models to predict DHF among patients with suspected dengue infection. We produced models for a general hospital setting and for a primary care unit setting lacking lab facilities. The best model using combined data from both hospitals achieved an AUC of 0.90 for the general hospital setting and 0.79 for the primary care unit setting. We then investigated the generalizability of the models by training models with data from one hospital and testing them with data from the other. For some models, we found a significant reduction in performance. Possible sources of this are differences in how attributes are defined or measured and differences in the hematological parameters of the two patient populations. We conclude that while high accuracy prediction of DHF is possible, care must be taken when applying DHF predictive models from one clinical setting to another.

Annually, around half a million people with severe dengue require hospitalization, all over the globe, with around 12,500 (2.5%) succumbing to the illness. In this prospective observational study, we recruited 74 patients with dengue infection, 28 had severe dengue and 46 had dengue with warning signs. sIL-2R levels were significantly raised in the severe dengue group, compared to the warning signs group. Using an ROC curve (receive operator characteristic), at a cutoff of 5.379 ng/ml, it predicted the severe dengue classification with a p -value of < 0.001. As a marker for predicting hemophagocytic lymphohistiocytosis (HLH), the ROC curve revealed a cutoff of ≥ 5.379 ng/ml for sIL-2R levels with the AUROC being 0.83, suggesting a strong diagnostic performance( p -value < 0.001). sIL-2R levels can be used for predicting severe dengue classification with moderate sensitivity and specificity. Secondary HLH is an under-reported entity in dengue infection and early surveillance with the help of sIL-2R may be helpful in-patient care management.