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1,543 results for “malaria”
Inter-Chemical Correlation results for the study: HHEARx2016-1432 (Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children)
Title: Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children <br>Species: Homo sapiens <br>Number of samples: 1256 <br>Number of named analytes: 51 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=5 <br>
Post-trial access practice in Malaria, Tuberculosis, and NTDs Clinical Trial studies in Sub-Saharan African countries, quantitative study
<p>This is the data set used <span>to evaluate post trial access plan and implementation practice on TB, Malaria and NTD clinical trial studies conducted in the sub-Saharan African countries. </span></p>
A dataset of human and Plasmodium falciparum genotypes in severe malaria cases from The Gambia and Kenya
<p>This data release contains human and <em>Plasmodium falciparum</em> malaria genotypes from the article:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The release contains genotypes from human and <em>Plasmodium falciparum</em> genetic variants, genotyped using blood samples from 4,171 children ascertained with severe symptoms of malaria at the Royal Victoria Teaching Hospital (now the Edward Francis Small Teaching Hospital), The Gambia, and from the Kilifi District Hospital (now Kilifi County Hospital), Kenya in the period 1995-2009.</p> <p>An accompanying set of association test summary statistics has also been released on Zenodo (doi: <a href="https://doi.org/10.5281/zenodo.5722497">10.5281/zenodo.5722497</a>). Please see <a href="http://www.malariagen.net/resource/32">www.malariagen.net/resource/32</a> for full details of other resources associated with the above manuscript.</p> <p> </p>
Dataset and code to reproduce analysis on the impact of indoor residual spraying (IRS) on malaria at Illovo Nchalo, Malawi
<p><strong>V3 edit: </strong>The latest R file contains extra lines of code to produce prediction intervals. </p> <p> </p> <p><strong>The repository contains:</strong></p> <p>- Excel sheets for each round of indoor residual spraying from 2014 - 2018 for villages based on the Illovo Nchalo Estate (provided by public health officer)</p> <p>- Weather data for 1999 - 2019 downloaded from Sasri Weather web for Malawi - Illovo Nchalo (Open access after signing up)</p> <p>- Explanation of variables downloaded from Sasri Weather Web</p> <p>- Expected population: number of residents living in Illovo clinic's catchment areas based on 2016 and 2019 census. Linear interpolation for the other years</p> <p>- Malaria data per month per clinic from the public health officer's records at Illovo Nchalo for 7 clinics for 2014 - 2018</p> <p>- Malaria data downloaded and selected from DHIS2 (access upon request and approval)</p> <p>- R file to reproduce figures, tables, and results for the paper under submission for PLOS GPH</p> <p>- Geopackages of data that is not open-source already to reproduce the map in figure 1</p> <p> </p> <p><strong>Description of IRS data:</strong></p> <p>- Village: Name of the villages based at Illovo being targeted for IRS</p> <p>- Target_spray: Number of structures within the village targeted for spraying</p> <p>- Sprayed: Number of structures actually sprayed</p> <p>- Date_start: Start date of the IRS campaign in a village</p> <p>- Date_end: End date of the IRS campaign in that village</p> <p>- Coverage_p: Percentage of structures sprayed calculated from "target_spray" and "sprayed"</p> <p> </p> <p><strong>Notes on reconciling the different years of IRS:</strong></p> <p>1. Post office and D. compound have been added to Nkombedzi</p> <p>2. B compound has been added to Riverside/Mess</p> <p>3. The following villages attend the following clinics</p> <p> </p> <p><strong>The following villages attend the assigned clinics:</strong><br>- Mess and Bonksville -> Factory<br>- Mlambe and Paxman -> Mangulenje<br>- Sande Ranch -> Lengwe<br>- Mechanical Pool -> Mwanza</p> <p> </p> <p><strong>Description of the malaria data:</strong></p> <p>- Date, month, year</p> <p>- Time_dummy: 1 to 48, over the study period</p> <p>- Village: The name of the village the clinic is based in. In further analyses, this is referred to as "clinic" instead to avoid confusion.</p> <p>- Total_cases: total number of cases testing positive for malaria by RDT, or in a very small percentage of cases microscopy (only used when RDT gives inconclusive or conflicting results, or when symptoms persist with negative RDT). Cases_on + cases_off = total_cases</p> <p>- Cases_on: Number of malaria cases from residents of villages located within the boundaries of the Illovo estate</p> <p>- Cases_off: Number of malaria cases from residents of villages located (just) outside the boundaries of the Illovo estate</p> <p>- Total_patients: Total number of patients attending the clinic that month</p> <p> </p> <p>From the selected control clinics only "WHO NMCP P Confirmed malaria cases" was used to indicate the number of malaria cases and "CMED Total Population" to indicate the clinic catchment population. Further info on DHIS2 website. </p> <p> </p> <p>For further information don't hesitate to contact Remy Hoek Spaans. </p> <p> </p> <p> </p> <p> </p>
Sample accession list for "Malaria protection due to sickle haemoglobin depends on parasite genotype"
<p>This dataset contains a list of sample accessions and associated metadata for <em>P.falciparum</em><br> DNA samples sequenced for the analysis presented in the paper:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The data contains: i. a single tab-delimited text file containing accessions and sequence read quality control-related information related to the processing described in [1], and ii. a README file describing the contents of the data in markdown and HTML format. Please see the enclosed README file for full details.</p> <p>A full list of datasets which have been released with this manuscript can be found on the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a>.</p> <p> </p>
Estimating malaria disease burden in the Asia-Pacific
<p>This repository hosts all the Supplementary Material for the publication: Maude RJ, Mercado CEG, Rowley J, Ekapirat N, Dondorp AM. (2019) Estimating malaria disease burden in the Asia-Pacific (under review)</p>
Dataset: Health worker compliance with severe malaria treatment guidelines in the context of implementing pre-referral rectal artesunate in the Democratic Republic of the Congo, Nigeria and Uganda: an operational study
<p>Dataset underlying the publication "<strong>Health worker compliance with severe malaria treatment guidelines in the context of implementing pre-referral rectal artesunate in the Democratic Republic of the Congo, Nigeria and Uganda: an operational study</strong>" (Plos Medicine)</p> <p>Data originating from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021.</p> <p>Analysis of health workers' compliance with the treatment guidelines for severe malaria in the context of rolling out pre-referral rectal artesunate (RAS) in the Democratic Republic of the Congo, Nigeria and Uganda. Details provided in the publication.</p>
Plasmodium falciparum infection in febrile Congolese children: prevalence of clinical malaria ten years after introduction of Artemisinin-combination therapies
<p>dataset used in the paper.</p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Summary statistics for association tests between human and Plasmodium falciparum genetic variants in 3,346 severe malaria cases from The Gambia and Kenya
<p>This dataset contains summary statistics for association tests between human and<br> <em>Plasmodium falciparum</em> malaria parasite genetic variants, using data from 3,346 severe malaria cases from The Gambia and Kenya. These results underlie the analysis described in our paper:</p> <p><strong>"Malaria protection due to sickle haemoglobin depends on parasite genotype"</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy Nguyen, Sónia M. Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor, David J. Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>:: <a href="https://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a><br> <br> The genotype data underlying these summary statistics has also been deposited on Zenodo<br> (<a href="https://zenodo.org/record/4973477">doi:10.5281/zenodo.4973477</a>). The <a href="https://www.well.ox.ac.uk/~gav/hptest)">HPTEST software</a> used to generate these results has also been deposited (<a href="https://doi.org/10.5281/zenodo.5685580">doi:10.5281/zenodo.5685580</a>). Please see the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a> for a full list of datasets which have been released with this manuscript.</p> <p><strong>Data contents.</strong></p> <p>The dataset consists of a single <a href="http://sqlite.org">sqlite database file</a> containing the results, and an accompanying README file in markdown and html format. Please see the README file for full details of the data contents.</p> <p> </p>
Model-informed target product profiles of long-acting- injectables for use as seasonal malaria prevention: code and simulation data
<p>This simulation data set and code reproduces the Figures and analysis of PLOS Global Public Health peer-reviewed article </p> <p><strong>Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention</strong></p> <p>Authors:</p> <p>Lydia Burgert<sup>1, 2</sup>, Theresa Reiker<sup>1, 2</sup>, Monica Golumbeanu<sup>1,2</sup>, Jörg J. Möhrle<sup>1, 2, 3</sup>, Melissa A. Penny*<sup>1, 2</sup></p> <p> </p> <p><sup>1</sup> Swiss Tropical and Public Health Institute, Basel, Switzerland</p> <p><sup>2</sup> University of Basel, Basel, Switzerland</p> <p><sup>3 </sup>Medicines for Malaria Venture, Geneva, Switzerland</p> <p>*Corresponding author: <a href="mailto:melissa.penny@unibas.ch">melissa.penny@unibas.ch</a></p>
Sub-national tailoring of malaria interventions in Mainland Tanzania: simulation of the impact of strata-specific intervention combinations using modelling
<p>Simulation dataset. </p>
Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda
<p>Datasets underlying the publication "Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda":</p> <p><strong>Figure 6: </strong>Number of children enrolled in the Patient Surveillance System (grey bars), and percentage of these children being administered rectal artesunate (RAS), by country.</p> <p><strong>Figure 8:</strong> Overall case fatality ratio (CFR) in patients with danger signs and a positive malaria test at enrolment across the entire study period, by enrolment location and country. Data for Uganda excludes enrolments at PHCs (N=34).</p>
Microbiomes associated with avian malaria survival differ between susceptible Hawaiian honeycreepers and sympatric malaria-resistant introduced birds
<p>Of the estimated 55 Hawaiian honeycreepers (subfamily Carduelinae) only 17 species remain, 9 of which the International Union for Conservation of Nature considers endangered. Among the most pressing threats to honeycreeper survival is avian malaria, caused by the introduced blood parasite <em>Plasmodium relictum</em>, which is increasing in distribution in Hawai`i as a result of climate change. Preventing further honeycreeper decline will require innovative conservation strategies that confront malaria from multiple angles. Research on mammals revealed strong connections between gut microbiome composition and malaria susceptibility, illuminating a potential novel approach to malaria control through the manipulation of gut microbiota. </p> <p><span>One honeycreeper species, Hawai`i `amakihi (<em>Chlorodrepanis virens</em>), persists in some areas of high malaria prevalence, indicating they have acquired some level of immunity. To investigate if avian host-specific microbes may be associated with malaria survival, we characterized cloacal microbiomes and malaria infection for 174 `amakihi and 172 malaria-resistant warbling white-eyes (<em>Zosterops japonicus</em>) from Hawai`i Island using 16S rRNA gene metabarcoding and qPCR. Neither microbial alpha nor beta diversity covaried with infection, but 149 microbes showed positive associations with malaria survivors. Among these were <em>Escherichia</em> and <em>Lactobacillus</em> spp., which appear to mitigate malaria severity in mammalian hosts, revealing promising candidates for future probiotic research for augmenting malaria immunity in sensitive endangered species.</span></p>
Dataset for Starting at the community: Treatment seeking pathways of children with suspected severe malaria in Uganda
<p>Dataset for the publication <strong>"Starting at the community: Treatment seeking pathways of children with suspected severe malaria in Uganda".</strong></p> <p>Data from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021</p> <p>Descriptive analysis of treatment-seeking pathways of and antimalarial treatment provision for children under 5 years with suspected severe malaria in three districts of Northern Uganda. All children first sought treatment from a community health worker before being referred to a higher-level facility.</p>
Malaria Stage Classifier dataset
<p>This is the dataset for the Malaria Stage Classifier, which introduces a new method for the stage-specific classification of malaria-infected red blood cells (RBCs) and provides a fast, high-accuracy recognition even with limited training sets by a smart reduction of data dimension. RBCs are extracted from an image, reduced to characteristic one-dimensional cross-sections, and classified by a pretrained neural network. The method is applicable to images recorded by various microscopy techniques. The dataset can be used to retrain the neural network with new data.</p>
Dataset for: Effectiveness of rectal artesunate as pre-referral treatment for severe malaria in children under 5 years of age: a multi-country observational study
<p>Dataset underlying the publication "<strong>Effectiveness of rectal artesunate as pre-referral treatment for severe malaria in children under 5 years of age: a multi-country observational study</strong>" (BMC Medicine)</p> <p>Data originating from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021.</p> <p>Analysis of the health effect of the roll-out and administration of pre-referral rectal artesunate (RAS) in the Democratic Republic of the Congo, Nigeria and Uganda. Details provided in the publication.</p> <p> </p>
Database that contains all images (plus 180 more) employed in the article: "Image features for quality analysis of thick blood smears employed in malaria diagnosis"
<p>We share with you a bank of images obtained from microscopic fields of thick blood smears employed in the malaria diagnosis, and also the .csv file that contains the labels for each image.</p> <p>The images are saved with a unique name that is found in the first column of the .csv file. The second column contains the labels from each image, according to their unique names.</p> <p>The labeling process was done with the online toolbox Labelbox. Labelbox, "Labelbox," Online, 2020. [Online]. Available: https://labelbox.com </p> <p>If you are interested in using our database, cite our article as a way to recognize our work. We will be grateful for that. </p> <p>CITATION: Fong Amaris, W.M., Martinez, C., Cortés-Cortés, L.J. et al. Image features for quality analysis of thick blood smears employed in malaria diagnosis. Malar J 21, 74 (2022). https://doi.org/10.1186/s12936-022-04064-2</p> <p>URL of our paper: https://malariajournal.biomedcentral.com/articles/10.1186/s12936-022-04064-2</p> <p><strong>--- This is the link where you can find our images Bank: https://drive.google.com/drive/folders/1Qrv0e4bSEtkeqtPABz-klQp-6D6OjU-X?usp=sharing </strong></p> <p>It is important you to know that along with this .txt file, we are sharing the .csv file that contains 600 names of images (in the first column) with their respective labels (second column aside)</p> <p>This file corresponds to the instructions of an extended label file related to 600 images (and 600 new labels) in contrast to our previous label file with 420 labels from 420 images (https://www.researchgate.net/publication/359439520_Database420LabelsInstructionstxt ; https://www.researchgate.net/publication/359438904_Database420Labelscsv).</p> <p>Best Regards</p> <p> </p>
Fig. 3 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 3 Seasonal variation of sibling species (An. coluzzii and An. gambiae) in the study area. Abbreviations: DS, dry season; RS, rainy season
Fig. 2 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 2 Mortality rate of Anopheles gambiae Kisumu (laboratory susceptible strain) after 30 min exposure to cement and mud walls treated with pirimiphos-methyl in 2017 (a) and 2018 (b). The red line indicates the WHO efficacy threshold (mortality of 80%) of an insecticide
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OpenNeuro
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