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18 results for “Malaria Dataset”
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>
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>
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>
Somatic hypermutation-mediated paratope flexibility improves the cross-reactivity of human malaria antibodies -- Molecular Dynamics dataset
<p>4493 Manuscript Data<br>====================</p> <p>author: Anton Hanke<br>size of uncompressed folder: ~19Gb.<br>DOI: 10.5281/zenodo.11470585</p> <p># Standard MD simulation data</p> <p>Standard Simulations were generated with gromacs 2021.5 using the charmm36m forcefield Juli 2021 release tarball (https://mackerell.umaryland.edu/download.php?filename=CHARMM_ff_params_files/charmm36-jul2021.ff.tgz).<br>Post processed (PBC) simulations are structured as follows:<br>Mature generally refers to the wildtype 4493 antibody.</p> <p>- simulations/standardMD<br> |<br> |- prod.mdp example production mdp file used to run all production simulations.<br> |<br> |- mature Mature simulation set. (folder and file naming the same in all simulation directories)<br> | |- {peptide}_{replicate}_prod.gro {peptide} = peptide; {replicate} = standard MD replicate<br> | |- {peptide}_{replicate}_prod.tpr<br> | `- {peptide}_{replicate}_prod_align_noPBC.xtc (10Frames/ns)<br> |<br> |- mature_rerun Additional set of replicates with the wildtype 4493.<br> |- matureCapped Set of simulations with termini capped peptides<br> |- mature_nanpv2 Set of simulations with NPDP similar positioning of NANP<br> |- wo_pep Set of simulations without peptides for germline and mature<br> `- germline Set of germline simulations.</p> <p><br># RAMD simulation data</p> <p>RAMD simulations were generated with gromacs_2020.5 patched with RAMDv2 modified to account for the connected multiple ligand groups.<br>(Source code provided as tar file ./sw/gromacs_ramd_patchv2.tar.gz)</p> <p>Not all trajectories contain an unbinding event (gromacs CUDA bug.). <br>These trajectories were not considered in the analysis of simulations. </p> <p>- simulations/ramd<br> |<br> |- prod.mdp Exemplary production mdp file with RAMD settings, these were used in all trajectories w/<br> | differing RAMD random seed.<br> |<br> |- mature_2.625kcalmolA_4.0_3.0<br> | |- {peptide}_{replicate}_prod_{startFrame}.gro {peptide} = peptide; {replicate} = standard MD replicate; {startFrame} = Frame in standard MD used to start simulation.<br> | |- {peptide}_{replicate}_prod_{startFrame}.tpr<br> | |- {peptide}_{replicate}_prod_{startFrame}.ndx<br> | |- {peptide}_{replicate}_prod_{startFrame}_align_noPBC.xtc (100Frames/ns) Files omited due to size -- available on request.<br> | `- {peptide}_{replicate}_prod_{startFrame}_lastframe.pdb Last frame of the processed RAMD trajectory.<br> `- gl_2.625kcalmolA_4.0_3.0</p> <p><br># Analysis</p> <p>- analysis<br> |<br> |- entropie Quasi harmonic entropy estimation.<br> | |- inp Concatenated & Bootstrapped, coarse-grained and aligned trajectories of all systems<br> | |- out CPPTRAJ runs to calculate QHE on the bootstrapped trajectories<br> | |- run_complex.sh Script running analysis.<br> | |- ana.py Script to calculte average and std of QHE for each system. (generates *.out *.tsv *.png)<br> | |- cg.py Script used to bootstrap, coarse-grain align and build average structure with.<br> | `- delta_entropies.ods Excel file used to calculate Tab 1. in Main text of paper from entropies.out.<br> |<br> |- mmpbsa MMPBSA calculations (MM + SolvEnergy) with gmx_MMPBSA<br> | |- inp Input trajectories and topologies processed for MMPBSA<br> | |- out/gmx_mmpbsa Output directories in which gmx_MMPBSA was run.<br> | | ` *.dat Output files containing calculated energy terms from gmx_MMPBSA.<br> | |- mmpbsa.in MMPBSA input file used to run analysis.<br> | |- plot_results.py Python script to plot correlation of MMPBSA output with experimental data<br> | |- pca_eig_extr.py Script to reduce simulations to regions of high probability density within trajectory (not used in the present analysis)<br> | |- slurm-91315023.out Log file of the analysis run<br> | `- run_mmpbsa.sh Shell script to run the MMPBSA analysis (generates input and output file trees).<br> |<br> `- ramd RAMD analysation.<br> |- run.sh Shell script to run the analysis<br> |- run_ramd_ana.py Python script called by `run.sh` to run the analysis using `ramdAnalysis.py`<br> |- contact_clusters.py Python script to generate plots based on output of the analysis.<br> |- ramdAnalysis.py Python module containing analysis classes called/used within `run_ramd_ana.py`<br> | Based on tauRAMD & Fingerprint analysis by Dr. Daria Khokh (https://doi.org/10.1021%2Facs.jctc.8b00230; https://doi.org/10.1063%2F5.0019088)<br> |- abrun.* Log files from the present run<br> |- *.svg; *.png Analysis output files.<br> |- tramd_patchv2/ Output PDB structures from the analysis (excluded due to size, available on request)<br> `- representatives.pse Pymol session of cluster representatives along unbinding for germline and wildtype with contact probabilities within the<br> cluster mapped as b-factor.</p> <p># Figures</p> <p>- figure_pdbs PDB files (and pymol sessions) used to generate figures in the papers main text.</p>
Malaria disease and grading system dataset from public hospitals reflecting complicated and uncomplicated conditions
<p>Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy.</p>
How long is the last mile? Evaluating successful malaria elimination trajectories [Dataset]
<p>Annual malaria case series were sought for 56 successful elimination programmes through an extensive non-systematic review of published and unpublished documents, including searches of PubMed (https://pubmed.ncbi.nlm.nih.gov/), Google Scholar (scholar.google.com), Google Books (books.google.com), WHO’s Institutional Repository for Information Sharing (https://apps.who.int/iris), online surveillance databases, and the authors’ existing collection of malaria-related books and reports. Several of these elimination programmes were implemented in regions that today have disputed jurisdictional claims or countries that no longer exist; their inclusion is to understand the impact of the historical programmes implemented there and no statement on their current geopolitical context is intended.</p> <p>When annual case totals were classified by origin, the number of locally acquired (i.e., autochthonous) cases was recorded separately from the number of imported cases. If not classified (as is typical until case incidence falls to very low levels), all reported cases were assumed to be locally acquired. Where locally acquired cases were further subclassified, indigenous and cryptic cases were tallied together, while introduced or induced cases were not included in case totals since such cases may arise sporadically in response to importation and are not considered to jeopardize elimination. </p> <p>The first year in which zero indigenous or locally acquired cases were recorded was taken as the termination point of the case series, regardless of whether sporadic or resurgent local transmission occurred subsequently. While this definition of elimination is looser than that required for certification by WHO (i.e., three years with no indigenous transmission), it permits us to examine a wider range of elimination experiences, including some where success was not maintained. Secondary re-elimination efforts (where a year with zero local cases was achieved, malaria subsequently resurged, and then elimination was again achieved), as in the case of Mauritius or the former republics of the USSR, were not included. </p> <p>Sources for the malaria case data are enumerated in the accompanying Word document. For questions or clarifications, please contact Justin Cohen at jcohen@clintonhealthaccess.org.</p>
Dataset of the study on field evidence for manipulation of mosquito host selection by the human malaria parasite
<p>This repository contains the dataset from host preferences assays to determine odour-mediated mosquito host preference as well as mosquito host selection determination through identification of the blood meal origin from indoor-resting blood-fed mosquito females in Burkina Faso.</p>
Data from: Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors
The impact of insecticide resistance in malaria vectors is poorly understood and quantified. Here a series of geospatial datasets for insecticide resistance in malaria vectors are provided so that trends in resistance in time and space can be quantified and the impact of resistance found in wild populations on malaria transmission in Africa can be assessed. Data are also provided for common genetic markers of resistance to support analyses of whether these genetic data can improve the ability to monitor resistance in low resource settings. Specifically, data have been collated and geopositioned for the prevalence of insecticide resistance, as measured by standard bioassays, in representative samples of individual species or species complexes. Data are provided for the Anopheles gambiae species complex, the Anopheles funestus subgroup, and for nine individual vector species. In addition, allele frequencies for known resistance associated markers in the Voltage-gated sodium channel (Vgsc) are provided. In total, eight analysis-ready, standardised, geopositioned datasets encompassing over 20,000 African mosquito collections between 1957 and 2017 are provided.
Underlying dataset for the manuscript titled "Identification of RTS,S/AS01 vaccine-induced humoral biomarkers predictive of protection against controlled human malaria infection"
<p><span>The antibody binding data included in this dataset is associated with the manuscript “Spreng, R.L., Seaton, K.E. et al., <em><span>Identification of RTS,S/AS01 vaccine-induced humoral biomarkers predictive of protection against controlled human malaria infection; </span></em><span>Accepted for Publication in JCI Insight (2024)</span>. These data come from the characterization of serum samples from four controlled human malaria infection (CHMI) clinical trials of the RTS,S vaccine, including NCT01366534 (referred to as MAL068), NCT01857869 (referred to as MAL071), NCT03162614 (referred to as MAL092), and NCT03824236 (referred to as MAL102).</span></p>
Malaria disease and grading system dataset from public hospitals reflecting complicated and uncomplicated conditions
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Data from: Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors
Open the record for dataset details and reuse information.
Datasets on Malaria Disease
<p>A survey was conducted in Kwara State using questionnaires for data collection and these are the datasets gathered from the survey</p>
Malaria Blood Smear Image Dataset Creation
<p><strong>Dataset Creation</strong></p> <p>The dataset was collected from Tanzania. We sought out ethical clearance that gave permission to collect samples of patients that tested positive for malaria and as well as negative. The blood samples were stained and images were taken using the iPhone 6s mounted on top of an Olympus microscope. Afterward, the images were labeled by the three Lab technologists by drawing bounding boxes around the malaria parasites and white blood cells. </p> <p> </p> <p>Ethical Statement</p> <p>The nationally recognized ethics committee at The University of Dodoma and Benjamin Mkapa Hospital Research Center approved this research. It granted permission to isolate the samples of the positive cases so as to capture images for the purpose of this research. The data was collected from patients with suspected cases of malaria who willingly went to the hospital for diagnosis and treatment. We took images of what the lab technician was examining under the microscope. To avoid privacy violations of patients, no details about the patient identity were taken for this research rather than the images of their stained blood samples, age, gender, and location.</p> <p> </p> <p>Sample Collected</p> <p>The samples were collected from patients that had been requested, by a doctor, to receive a malaria test. A total of 40 cases were included in the present study,<em> </em>20 patients had positive confirmation of having malaria.<em> </em>Their mean age was 23.7 years (SD: 17.9 years) and 44.3% of cases were males and 53.7% were females. The mean ages of positive confirmed cases and negative confirmed cases were 23.30±17.7 and 25.89±18.7 years, respectively. All cases were residents of the Morogoro Region in Tanzania which have a higher rate of malaria patients.<em> </em></p> <p> </p> <p>Reagent Preparation</p> <p>Before subjecting a blood sample to a microscope for observation and image capturing, it had to be stained using a reagent. For that case, a buffer solution using 1 liter of distilled water and 1 buffer tablet were prepared with the aim of making a 7.2 PH solution. Thereafter, a Giemsa working solution was prepared by taking 2.5 ml of Giemsa stain stock into 25 ml of water making a 10% concentration. The working solution was then filtered using a circle filter paper. After filtration, the samples were placed horizontally and stained for 10 minutes. The stained samples were washed by using tap-water and placed vertically using a staining rack for the water to run off. At this stage, the dried stained blood samples were ready for observation under a 100 magnification of Olympus microscope. </p> <p> </p> <p>Image Collection</p> <p>This phase involved using a smartphone (iPhone 6s+) to capture images of stained blood smear that were observed under a microscope. A small portion of immersion oil was applied to the stained thick blood smear to enhance visibility. The slide was then placed under an Olympus CX 21 microscope for observation. The lens used had 100x magnification as recommended by the WHO (D Payne 1988). The microscope was continuously adjusted by a lab technician to ensure proper focus. At the same time, the iPhone 6s+ mobile phone was mounted to the microscope using the Labcam Microscope Adapter as shown in figure 1, and pictures were taken.</p> <p>The standard malaria diagnosis involves a lab technologist examining not less than 100 fields for a single slide under observation (D Payne, 1988). Therefore, approximately 100 images were captured for every blood smear slide placed under observation. For the 100 patients, we had a total of 100,000 images captured with 5000 images from positive patients. All 5000 images from 50 positive (infected) patients required annotation (labeling of the parasites and white blood cells). On the other hand, the 5000 images from uninfected patients did not require any annotation. The images captured were in JPG format, with a resolution of 4302 X 3204 pixels and a size of approximately 1 MB. The images were stored in a folder labeled with a date the slide was taken followed by a sample number for identification of the image.</p> <p> </p> <p>Image Annotation</p> <p>A team of three experts from the College of Health Science of the University of Dodoma and Benjamin Mkapa Hospital performed the annotation of the 2000 images altogether. The images were annotated using the LabelImg annotation tool. The annotation involved creating bounding boxes for the plasmodium and white blood cell classes. Annotators were instructed to label a target class by drawing the smallest possible box that contains all the visible parts of the plasmodium and the white blood cells. The output of the annotation was a Pascal VOC XML file with specific details on where the image is stored, the size of the image, filename, and coordinates of bounding boxes of all objects present in the image (Plasmodium and white blood cells). The time taken to annotate a single image with a fewer number of parasites, this means less than 20 parasites, took approximately 2 minutes while for a case with a higher number of parasites approximately more than 100 parasites, took around 15 to 20 minutes for a single image. In general for one patient, it took about 8 hours to annotate an image of the stained blood sample. The table below shows a summary of the dataset that was created in this stage.</p> <p> </p>
Survey dataset malaria short course
<p>The de-identified dataset on malaria short course</p>
Dataset for Pre-referral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda
<p>Dataset for the publication <strong>"Pre-referral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda".</strong></p> <p>Data from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021</p> <p>Analysis of the effect of the roll-out and administration of pre-referral rectal artesunate (RAS) on referral completion to designated referral health facilities in the Democratic Republic of the Congo, Nigeria and Uganda.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.