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613 results for “Dengue”
Study of Sanofi Pasteur's CYD Dengue Vaccine in Healthy Subjects in Singapore
ClinicalTrials.gov study NCT00880893. IPD Sharing: YES. Countries: 1. Publications: 2.
Study of a Tetravalent Dengue Vaccine Administered Concomitantly or Sequentially With Adacel® in Healthy Subjects
ClinicalTrials.gov study NCT02992418. IPD Sharing: YES. Countries: 1. Publications: 1.
Immunogenicity and Safety of a Tetravalent Dengue Vaccine Booster Injection in Subjects Who Previously Completed a 3-dose Schedule
ClinicalTrials.gov study NCT02824198. IPD Sharing: YES. Countries: 1. Publications: 2.
Lot-to-lot Consistency of 3 Lots of Tetravalent Dengue Vaccine (TDV) in Non-endemic Country(Ies) for Dengue
ClinicalTrials.gov study NCT03423173. IPD Sharing: YES. Countries: 1. Publications: 2.
Dengue incidence and climatic variables in Cali from 2015 to 2021
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Data from: Blockade of dengue virus transmission from viremic blood to Aedes aegypti mosquitoes using human monoclonal antibodies
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Data from: Long-term persistence of monotypic dengue transmission in small size isolated populations, French Polynesia, 1978-2014
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Dengue Cases Réunion -- DiSera et al paper
<p>Number of dengue cases in Réunion, provided by Dr. Bertran Sudre (ECDC). For additional information, contact Laurel DiSera (ldisera@iri.columbia.edu) or Ángel G. Muñoz (agmunoz@iri.columbia.edu).</p>
RNA sequences for Aedes species, Dengue, and Chikungunya viruses
<p>There are arthropod-borne disease outbreaks as a result of pathogen influx including arboviruses which are transmitted by strains of <em>Aedes</em> species that occur periodically in varying spots on the globe. The aim of this study was to determine phylogenetic relationship of <em>Aedes</em> mosquitoes, Dengue, and Chikungunya viruses along the Coastline of Kenya based on sequences of:</p> <ol> <li>mitochondria nicotinamide adenine dehydrogenase sub unit 4 gene for Aedes species.</li> <li>non-structural protein 5 gene for Dengue virus</li> <li>non-structural protein 1 gene for Chikungunya virus</li> </ol>
Terrestrial mammals from America with dengue incidence
<p>This dataset contains dengue virus incidence in mammals from published papers.</p> <p>It has 17 fields:</p> <ol> <li>Reference</li> <li>Sampling year</li> <li>Country</li> <li>Diagnostic test</li> <li>Diagnostic test type</li> <li>Subclade</li> <li>Virus</li> <li>Order</li> <li>Family</li> <li>Mammal species</li> <li>Individual tested</li> <li>Incidence</li> <li>Prevalence</li> <li>Susceptibility</li> <li>Location</li> <li>latitude</li> <li>longitude</li> </ol>
Predictive scoring for risk of complications in pediatric dengue infection
<p class="MsoNormal"><strong><span>Background: </span></strong><span>Dengue infection has been a worrisome cause of mortality and morbidity in children. Though numerous scoring systems have been developed, they are in the adult population or are too complicated for use in children. Pediatric dengue infection has a wide spectrum from a mild illness to severe complications and an unpredictable course. Hence the need for a predictive scoring system where the possibility of complications can be identified which can contribute to reduction in mortality and morbidity of dengue by prompt referrals and anticipatory management. </span></p> <p class="MsoNormal"><span><strong>Methodology:</strong> Prospective case cohort study of children with confirmed dengue. </span></p> <p class="MsoNormal"><span><strong>Results</strong>: 303 children were included and divided into two groups – the dengue fever group and the complicated dengue group based on the WHO clinical classification. The clinical and laboratory parameters were analysed individually, cut offs identified by ROC curves and compared for significance between the two groups. The parameters that emerged were hypotension, PCV ≥ 42%, platelet count ≤ 75000 cells/cumm, WBC ≥ 7000 cells/cumm, and ALT ≥ 70U/L.</span> <span>Using the adjusted odd's Ratio, and coefficient, individual predictive scores were tabulated ranging from 0 to 3, with a total score of 0 to 7. A cut-off score of 2 was then identified based upon the sensitivity(84.13%) and specificity(72.50%) as the ideal score to predict complicated dengue. Internal validation of the score was done where the area under the curve for predicting complicated dengue was 0.86(95% CI 0.8-0.92) with a P value of <0.001.</span><strong><span> </span></strong></p> <p class="MsoNormal"><strong><span>Conclusion</span></strong><span>: </span><span>Our dengue predictive scoring system has been developed using five indicators, with a score of 2 and above out of 7, suggesting increased risk of developing complications. This has been validated internally and can be used to predict complicated dengue among children.</span></p>
Underlying data for 'Rapid molecular assays for the detection of the four dengue viruses in infected mosquitoes'
<p>The pantropic emergence of severe dengue disease can partly be attributed to the co-circulation of different dengue viruses (DENVs) in the same geographical location. Effective monitoring for circulation of each of the four DENVs is critical to inform disease mitigation strategies. In low resource settings, this can be effectively achieved by utilizing inexpensive, rapid, sensitive and specific assays to detect viruses in mosquito populations. In this study, we developed four rapid DENV tests with direct applicability for low-resource virus surveillance in mosquitoes. The test protocols utilize a novel sample preparation step, a single-temperature isothermal amplification, and a simple lateral flow detection. Analytical sensitivity testing demonstrated tests could detect down to 1,000 copies/µL of virus-specific DENV RNA, and analytical specificity testing indicated tests were highly specific for their respective virus, and did not detect closely related flaviviruses. All four DENV tests showed excellent diagnostic specificity and sensitivity when used for detection of both individually infected mosquitoes and infected mosquitoes in pools of uninfected mosquitoes. With individually infected mosquitoes, the rapid DENV-1, -2 and -3 tests showed 100% diagnostic sensitivity (95% CI = 69% to 100%, n=8 for DENV-1; n=10 for DENV 2,3) and the DENV-4 test showed 92% diagnostic sensitivity (CI: <span>62% to 100%, n=12</span>) along with 100% diagnostic specificity (CI: 48–100%) for all four tests. Testing infected mosquito pools, the rapid DENV-2, -3 and -4 tests showed 100% diagnostic sensitivity (95% CI = 69% to 100%, n=10) and the DENV-1 test showed 90% diagnostic sensitivity (<span>55.50% to 99.75%, n=10</span>) together with 100% diagnostic specificity (CI: 48–100%). Our tests reduce the operational time required to perform mosquito infection status surveillance testing from > two hours to only 35 minutes, and have potential to improve accessibility of mosquito screening, improving monitoring and control strategies in low-income countries most affected by dengue outbreaks.</p>
Selection pressure analysis of dengue virus complete genome and E gene nucleotide sequences from Pakistan
<p>This dataset comprises 43 E gene and 44 complete genome nucleotide sequences of the dengue virus from serotypes DENV-1 to DENV-4, representing all documented sequences in Pakistan to date, sourced from the Virus Pathogen Resource (ViPR) database and NCBI. The E gene is critical as it is involved in serotype changes of the dengue virus, making it a pivotal target for understanding shifts in viral pathogenicity and immune escape mechanisms. The aim of compiling this dataset is to facilitate comprehensive genetic analysis and enhance understanding of the evolutionary dynamics of the dengue virus within the region. To assess the evolutionary pressures acting on these sequences, we conducted a selection pressure analysis utilizing computational methods. These methods include the Single Likelihood Ancestor Counting (SLAC), Fixed Effects Likelihood (FEL), adaptive Branch Site Random Effects Likelihood (aBSREL), Mixed Effects Model of Evolution (MEME), and the Genetic Algorithm for Recombination Detection (GARD), all implemented in the HyPhy software package. Our analysis focused on identifying genomic sites under both positive and negative selection pressures, providing insights into the adaptive evolutionary processes affecting the E gene of the dengue virus in Pakistan. Understanding the molecular evolution of this gene is crucial for predicting serotype evolution, potentially aiding in the development of effective vaccines and therapeutic strategies.</p>
Fig. 1 in Circulating dengue virus serotypes and vertical transmission in AEdES larvae during outbreak and inter-outbreak seasons in a high dengue risk area of Sri Lanka
Fig. 1 Map of Sri Lanka showing the location of Mawanella, the study area
Control y seguimiento de la transmisión del dengue a través del mosquito tigre
<p>Analisis sobre la expansion y el desarrollo del Dengue y el mosquito tigre en América, centrado en los casos de Brasil y EEUU, mostrando los casos de dengue grave y muerte.</p>
Dengue and microcephaly cases in Brazil used in Carvalho et al.
<p>Datasets used in the analysis of the manuscript entitled "Association of past dengue fever epidemics with the risk of Zika microcephaly at population level in Brazil" by Carvalho et al. (submitted).</p> <p>dengueBR2001-2014.csv contains the number of reported dengue cases by month in a selection of Brazilian microregions.</p> <p>microBR2014-2016.csv contains the cumulated number of reported microcephaly cases between 2014 and 2016 in a selection of Brazilian microregions.</p>
Table 2 in Circulating dengue virus serotypes and vertical transmission in AEdES larvae during outbreak and inter-outbreak seasons in a high dengue risk area of Sri Lanka
<p><b>Table 2</b> Distribution of DENV serotypes in patients with suspected dengue and in <i>Aedes</i> mosquito larvae</p><table><tbody><tr><th>Patient no.</th><th><i>Ae. aegypti</i></th><th><i>Ae. albopictus</i></th><th>DENV serotype identified in mosquito pools</th><th>DENV serotype identified in patients</th></tr></tbody><tbody><tr><th>1</th><td>Detected</td><td>ND</td><td>DENV-3</td><td>DENV-3</td></tr><tr><th>2</th><td>ND</td><td>Detected</td><td>DENV-1</td><td>DENV-1</td></tr><tr><th>3</th><td>ND</td><td>Detected</td><td>DENV-3</td><td>DENV-3</td></tr><tr><th>4</th><td>ND</td><td>Detected</td><td>DENV-4</td><td>ND</td></tr><tr><th>5</th><td>ND</td><td>Detected</td><td>DENV-3</td><td>ND</td></tr><tr><th>6</th><td>ND</td><td>Detected</td><td>DENV-1</td><td>ND</td></tr><tr><th>7</th><td>Detected</td><td>ND</td><td>DENV-2</td><td>ND</td></tr><tr><th>8</th><td>Detected</td><td>ND</td><td>DENV-1</td><td>ND</td></tr><tr><th>9</th><td>Detected</td><td>ND</td><td>DENV-1</td><td>ND</td></tr><tr><th>10</th><td>ND</td><td>Detected</td><td>DENV-3</td><td>ND</td></tr><tr><th>11</th><td>ND</td><td>Detected</td><td>DENV-2</td><td>ND</td></tr><tr><th>12</th><td>ND</td><td>Detected</td><td>DENV-2</td><td>DENV-1</td></tr><tr><th>13</th><td>ND</td><td>Detected</td><td>DENV-2</td><td>ND</td></tr><tr><th>14</th><td>ND</td><td>Detected</td><td>DENV-4</td><td>ND</td></tr><tr><th>15</th><td>ND</td><td>Detected</td><td>DENV-1</td><td>ND</td></tr><tr><th>16</th><td>ND</td><td>Detected</td><td>DENV-2</td><td>DENV-2</td></tr></tbody></table><p><i>ND</i> Not detected</p>
Table 1 in Circulating dengue virus serotypes and vertical transmission in AEdES larvae during outbreak and inter-outbreak seasons in a high dengue risk area of Sri Lanka
<p><b>Table 1</b> Distribution of <i>Aedes</i> mosquito larvae in and around residences of patients with suspected dengue in Mawanella from December 2015 to March 2017</p><table><tbody><tr><th>Period</th><th>Month and year of sample collection</th><th>Total no. of vector pools collected in entomological survey</th><th>No. of <i>Aedes</i> mosquito pools identified</th></tr><tr><th><i>Ae. aegypti</i></th><th><i>Ae. albopictus</i></th></tr></tbody><tbody><tr><th>Epidemic</th><td>12/2015</td><td>18</td><td>3</td><td>15</td></tr><tr><th></th><td>1/2016</td><td>22</td><td>8</td><td>14</td></tr><tr><th>Inter-epidemic</th><td>2/2016</td><td>5</td><td>0</td><td>5</td></tr><tr><th></th><td>3/2016</td><td>3</td><td>1</td><td>2</td></tr><tr><th></th><td>4/2016</td><td>4</td><td>1</td><td>3</td></tr><tr><th></th><td>5/2016</td><td>12</td><td>1</td><td>11</td></tr><tr><th></th><td>6/2016</td><td>15</td><td>9</td><td>6</td></tr><tr><th>Epidemic</th><td>7/2016</td><td>7</td><td>1</td><td>6</td></tr><tr><th></th><td>8/2016</td><td>5</td><td>2</td><td>3</td></tr><tr><th></th><td>9/2016</td><td>5</td><td>2</td><td>3</td></tr><tr><th>Inter-epidemic</th><td>10/2016</td><td>6</td><td>1</td><td>5</td></tr><tr><th></th><td>11/2016</td><td>6</td><td>1</td><td>5</td></tr><tr><th>Epidemic</th><td>12/2016</td><td>14</td><td>3</td><td>11</td></tr><tr><th></th><td>1/2017</td><td>23</td><td>8</td><td>15</td></tr><tr><th>Inter-epidemic</th><td>2/2017</td><td>1</td><td>0</td><td>1</td></tr><tr><th></th><td>3/2017</td><td>25</td><td>8</td><td>17</td></tr><tr><th>Total</th><td></td><td>171</td><td>49</td><td>122</td></tr></tbody></table>
Climate, inter-serotype competition and arboviral interactions shape Dengue dynamics in Thailand
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Machine learning deconvolution of the immune response to dengue
<p>Machine learning deconvolution of the immune response to dengue - dataset of antibody repertoire sequencing</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.