Background The burden of dengue virus (DENV) infection across geographical regions of India is poorly quantified. We estimated the age-specific seroprevalence, force of infection, and number of infections in India. MethodsWe did a community-based survey in 240 clusters (118 rural, 122 urban), selected from 60 districts of 15 Indian states from five geographical regions. We enumerated each cluster, randomly selected (with an Andriod application developed specifically for the survey) 25 individuals from age groups of 5-8 years, 9-17 years, and 18-45 years, and sampled a minimum of 11 individuals from each age group (all the 25 randomly selected individuals in each age group were visited in their houses and individuals who consented for the survey were included in the study). Age was the only inclusion criterion; for the purpose of enumeration, individuals residing in the household for more than 6 months were included. Sera were tested centrally by a laboratory team of scientific and technical staff for IgG antibodies against the DENV with the use of indirect ELISA. We calculated age group specific seroprevalence and constructed catalytic models to estimate force of infection. FindingsFrom June 19, 2017, to April 12, 2018, we randomly selected 17 930 individuals from three age groups. Of these, blood samples were collected and tested for 12 300 individuals (5-8 years, n=4059; 9-17 years, n=4265; 18-45 years, n=3976). The overall seroprevalence of DENV infection in India was 48•7% (95% CI 43•5-54•0), increasing from 28•3% (21•5-36•2) among children aged 5-8 years to 41•0% (32•4-50•1) among children aged 9-17 years and 56•2% (49•0-63•1) among individuals aged between 18-45 years. The seroprevalence was high in the southern (76•9% [69•1-83•2]), western (62•3% [55•3-68•8]), and northern (60•3% [49•3-70•5]) regions. The estimated number of primary DENV infections with the constant force of infection model was 12 991 357 (12 825 128-13 130 258) and for the age-dependent force of infection model was 8 655 425 (7 243 630-9 545 052) among individuals aged 5-45 years from 30 Indian states in 2017.Interpretation The burden of dengue infection in India was heterogeneous, with evidence of high transmission in northern, western, and southern regions. The survey findings will be useful in making informed decisions about introduction of upcoming dengue vaccines in India.
Dengue is a serious global health concern especially in tropical and subtropical countries. About 2.5 billion of the world's population is at risk for dengue infection. Early diagnosis is the key to prevent the deterioration of health of the patient to severe illness. Laboratory diagnosis of dengue is essential for providing appropriate supportive treatment to dengue patients with febrile illness, which is difficult to diagnose clinically. Here, we demonstrate surface enhanced Raman scattering (SERS) based diagnosis of dengue virus in clinical blood samples collected from total of 102 subjects. All of the samples were well characterized by conventional NS1 antigen and IgM antibody ELISA kits. The silver nanorods array fabricated by glancing angle deposition technique were employed as SERS substrates. A small amount of patient blood serum (5 μL) was taken for analysis and the report was prepared within a minute. SERS spectra of pure NS1 protein as well as spiked in serum was also recorded separately. Principal component analysis (PCA) was employed as the statistical tool to differentiate dengue positive, dengue negative, and healthy subjects on the basis of their respective SERS spectra. This method provides a sensitive, rapid, and field deployable diagnosis of dengue at the early stage (within 5 days of the onset of symptoms).
Blood-feeding enriched gut-microbiota boosts mosquitoes' anti-Plasmodium immunity.Here, we ask how Plasmodium vivax alters gut-microbiota, anti-Plasmodial immunity, and impacts tripartite Plasmodium-mosquito-microbiota interactions in the gut lumen. We used a metagenomics and RNAseq strategy to address these questions. In naïve mosquitoes, Elizabethkingia meningitis and Pseudomonas spp. are the dominant bacteria and blood-feeding leads to a heightened detection of Elizabethkingia, Pseudomonas and Serratia 16S rRNA. A parallel RNAseq analysis of blood-fed midguts also shows the presence of Elizabethkingia-related transcripts. After, P. vivax infected blood-meal, however, we do not detect bacterial 16S rRNA until circa 36 h. Intriguingly, the transcriptional expression of a selected array of antimicrobial arsenal cecropins 1-2, defensin-1, and gambicin remained low during the first 36 h-a time frame when ookinetes/early oocysts invaded the gut. We conclude during the preinvasive phase, P. vivax outcompetes midgut-microbiota. This microbial suppression likely negates the impact of mosquito immunity which in turn may enhance the survival of P. vivax. Detection of sequences matching to mosquito-associated Wolbachia opens a new inquiry for its exploration as an agent for "paratransgenesis-based" mosquito control.
Background Since its re-emergence in 2005, chikungunya virus (CHIKV) transmission has been documented in most Indian states. Information is scarce regarding the seroprevalence of CHIKV in India. We aimed to estimate the agespecific seroprevalence, force of infection (FOI), and proportion of the population susceptible to CHIKV infection. MethodsWe did a nationally representative, cross-sectional serosurvey, in which we randomly selected individuals in three age groups (5-8, 9-17, and 18-45 years), covering 240 clusters from 60 selected districts of 15 Indian states spread across all five geographical regions of India (north, northeast, east, south, and west). Age was the only inclusion criterion. We tested serum samples for IgG antibodies against CHIKV. We estimated the weighted age-group-specific seroprevalence of CHIKV infection for each region using the design weight (ie, the inverse of the overall probability of selection of state, district, village or ward, census enumeration block, and individual), adjusting for non-response. We constructed catalytic models to estimate the FOI and the proportion of the population susceptible to CHIKV in each region.
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