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34,778 results for “diseases”
Figure 2 Bar chart represents the percentage of the correct diagnosis using fuzzy diagnosis, K- nearest neighbor and Naïve Bayes classifiers.-Comparison of Fuzzy Diagnosis with K-Nearest Neighbor and Naïve Bayes Classifiers in Disease Diagnosis
<p>Figure 2 Bar chart represents the percentage of the correct diagnosis using fuzzy<br> diagnosis, K- nearest neighbor and Naïve Bayes classifiers.</p>
Figure 1. The comparison of the Area Under the ROC Curve (AUC) for fuzzy Diagnosis, KNN and NB-Comparison of Fuzzy Diagnosis with K-Nearest Neighbor and Naïve Bayes Classifiers in Disease Diagnosis
<p>The area under the receiver operating characteristic (ROC) curve (AUC) is used to measure<br> the performance of fuzzy diagnosis, KNN and NB. In order to show the difference between AUC<br> for the three methods, a single figure which combines the three AUC for the three methods was<br> used for comparison as shown in figure 1 below.</p>
Data from: Multisectoral approach in zoonotic disease surveillance
<p>Zoonoses are naturally transmissible between humans and animals. Globally, they account for more than 60% of human infections, 75% of emerging infections, 2.7 million human deaths, and 10% of the total DALYs lost yearly in Africa. In the last three decades, Kenya has had sporadic outbreaks of zoonoses. To increase the speed of reporting and efficiencies in detection and control, a multi-sectoral collaboration in zoonotic disease surveillance (MZDS) between human and animal health workers is essential. In an effort, Zoonotic disease unit (ZDU) in Kenya has been established at national and county levels. A cross sectional study was carried out to determine the level of utilization of multisectoral collaboration and its associated determinants in zoonotic disease surveillance among animal and human healthcare workers in Nakuru County. Quantitative data was gathered from 102 participants and quantitative data from 5 key informants. To test for significant differences, Chi-square and independent t-test were used. MZDS utilization level was 16% and the factors associated with higher utilization include; knowing what MZDS entails, education level, sector affiliation, trainings, supportive infrastructure and data storage. Lack of financing and poor coordination are hindrances to MZDS. In conclusion, there is need to finance MZDS activities, strengthen coordination mechanisms, carry out more sensitization and trainings among animal and human healthcare.</p>
A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on Parkinson's Disease. We provide data extracted via web scraping, along with metadata resulting from the extraction process using the NCBI API. The data pertains to the article titled 'A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation'.</p> <h2>Metadata Description</h2> <ul> <li> <h3>Analisys_MJFF_05_04_2024.xlsx</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>AU</td> <td>List of authors in abbreviated format.</td> <td>Text</td> </tr> <tr> <td>AF</td> <td>List of authors with full names.</td> <td>Text</td> </tr> <tr> <td>TI</td> <td>Full title of the article.</td> <td>Text</td> </tr> <tr> <td>SO</td> <td>Name of the journal or publication.</td> <td>Text</td> </tr> <tr> <td>SO_CO</td> <td>Country of origin of the publication.</td> <td>Text</td> </tr> <tr> <td>LA</td> <td>Language of the article.</td> <td>Text</td> </tr> <tr> <td>DT</td> <td>Type of document, such as "Journal Article".</td> <td>Text</td> </tr> <tr> <td>DE</td> <td>Keywords or descriptors associated with the article.</td> <td>Text</td> </tr> <tr> <td>MESH</td> <td>MeSH terms that describe the content of the article.</td> <td>Text</td> </tr> <tr> <td>DI</td> <td>Digital Object Identifier (DOI).</td> <td>Text</td> </tr> <tr> <td>PG</td> <td>Number of pages or page range.</td> <td>Numeric</td> </tr> <tr> <td>GRANT_ID</td> <td>Identification of funding, when available.</td> <td>Text</td> </tr> <tr> <td>GRANT_ORG</td> <td>Organization that provided the funding.</td> <td>Text</td> </tr> <tr> <td>UT, PMID</td> <td>Unique identifiers of the article.</td> <td>Numeric</td> </tr> <tr> <td>DB</td> <td>Name of the database where the article is indexed.</td> <td>Text</td> </tr> <tr> <td>AU_UN</td> <td>Information about the academic unit or institution of the authors.</td> <td>Text</td> </tr> </tbody> </table> <ul> <li> <h3>References_MJFF_v2_Final_Corrected.csv</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>Title</td> <td>Name of the article or publication.</td> <td>Text</td> </tr> <tr> <td>Authors</td> <td>List of authors who contributed to the article.</td> <td>Text</td> </tr> <tr> <td>Journal Name</td> <td>Name of the journal or periodical where the article was published.</td> <td>Text</td> </tr> <tr> <td>Publisher</td> <td>Name of the publisher who published the article.</td> <td>Text</td> </tr> <tr> <td>Volume</td> <td>Volume number of the journal in which the article appears.</td> <td>Numeric or Text</td> </tr> <tr> <td>Edition Number</td> <td>Number of the edition of the journal in which the article is found.</td> <td>Numeric or Text</td> </tr> <tr> <td>Starting Page</td> <td>Number of the first page of the article in the publication.</td> <td>Numeric</td> </tr> <tr> <td>Ending Page</td> <td>Number of the last page of the article.</td> <td>Numeric</td> </tr> <tr> <td>Publication Date</td> <td>Date on which the article was published.</td> <td>Date</td> </tr> <tr> <td>Open Access Status</td> <td>Indicates whether the article is available in open access.</td> <td>Text</td> </tr> <tr> <td>License</td> <td>Type of license under which the article was published.</td> <td>Text</td> </tr> <tr> <td>DOI (Digital Object Identifier)</td> <td>Unique identifier for the article that provides a permanent link to the online access.</td> <td>Text</td> </tr> <tr> <td>OA Location URL</td> <td>Direct URL to the article, if available in open access.</td> <td>Text</td> </tr> <tr> <td>Citation Count</td> <td>Number of times the article has been cited by other publications.</td> <td>Numeric</td> </tr> </tbody> </table> <p> </p>
FIGURE 2 in A review of "pyrite disease" for paleontologists, with potential focused interventions
FIGURE 2. Hygroscopic halotrichite. Water is gathered at the tip of the growing crystal and diffuses to the pyrite surface. Field of view is 3 mm.
FIGURE 1 in A review of "pyrite disease" for paleontologists, with potential focused interventions
FIGURE 1. Unit cell of pyrite, FeS2, drawn with VESTA. Data from (Rieder et al., 2007). Bonds are drawn to emphasize the pairs of sulfur atoms. The unit cell is outlined. Iron atoms (Fe) form a network for movement of electrons through the structure.
Genetic architecture of disease resistance and tolerance in Douglas-fir trees
<p><span>Understanding the genetic architecture of tolerance and resistance to pathogens is important to monitor and maintain resilient tree populations. Here we investigate the genetic basis of tolerance and resistance to needle cast disease in Douglas-fir (<em>Pseudotsuga menziesii</em>) caused by two fungal pathogens: Swiss needle cast (SNC) caused by <em>Nothophaeocryptopus gaeumannii</em>, and Rhabdocline needle cast (RNC) caused by <em>Rhabdocline pseudotsugae</em>). We performed a case-control genome-wide association analysis (GWA) and found these traits to be polygenic and under selection.</span> <span>We showed that stomatal regulation as well as ethylene and jasmonic acid pathways are important for resisting SNC infection and secondary metabolite pathways play a role in tolerating SNC once the plant is infected. We identified a key upstream transcription factor of plant defence, ERF1, as the main candidate for RNC resistance. Our findings contribute to the understanding of the highly polygenic architectures underlying disease resistance and tolerance in Douglas-fir and have important implications for forestry and conservation as the climate changes.</span></p>
High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling - Datasets
<p>Datasets and notebooks used in the publication High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling</p>
Fig. 5 in Histopatological effects of bleaching and disease on the coral Siderastrea stellata from coastal reefs of Brazil
Fig. 5. Degraded reproductive structures of Siderastrea stellata Verrill, 1868: a-b, stage V oocytes from healthy sample; c, spermatids from white plague disease samples; d, mesenteric filaments from bleached sample. Note in (d) black arrow points tonecrosis of mesenteric filaments.
Fig. 4 in Histopatological effects of bleaching and disease on the coral Siderastrea stellata from coastal reefs of Brazil
Fig. 4. Comparison of mean densities of zooXanthellae per 100 µm2 of gastrodermis between healthy samples, bleached, white plague diseased and colonies of coral Siderastrea stellata Verrill, 1868 with pigmentation pattern altered (PPA).
Fig. 2. Siderastrea stellata Verrill, 1868 in Histopatological effects of bleaching and disease on the coral Siderastrea stellata from coastal reefs of Brazil
Fig. 2. Siderastrea stellata Verrill, 1868 colonies healthy conditions (a), bleached (b), white plague disease (c), and colonies with pigmentation pattern altered (PPA) (d). And their respective histological sections, stained in H & E: a.1, healthy body wall epithelial layers; b.1, body wall with gastrodermis and mucus laden calicodermis, black arrow pointing to necrosis of gastrodermis, and red arrow pointing to mucocytes promote hyperplasia of the mesoglea; c.1, epithelium disrupted and presence of many mucocytes (black arrow), and focal vacuolation of gastrodermis (red arrow); d.1, basal body wall with calicodermis, and necrosis of eternal epithelium (black arrow).
Fig. 3 in Histopatological effects of bleaching and disease on the coral Siderastrea stellata from coastal reefs of Brazil
Fig. 3. Photomicrographs of organisms associated with bleached sample (a) black arrow is pointing to diatom (see striated frustule) on the surface of corals. The epidermis is atrophied and cuboidal and gastrodermis is bereft of zooXanthellae (atrophy) and markedly vacuolated; b, dead autolyzed tissue with another likely diatom (black arrow); c-d, dissociated tissues with fungal hyphae (black arrow); d, shows fungal hyphae (black arrow) invading coral tissue with necrosis.
Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection
<p>Integrating host movement and pathogen data is a central issue in wildlife disease ecology that will allow for a better understanding of disease transmission. We examined how adult female mule deer (<em>Odocoileus hemionus</em>) responded behaviorally to infection with chronic wasting disease (CWD). We compared movement and habitat use of CWD-infected deer (<em>n</em> = 18) to those that succumbed to starvation (and were CWD-negative by ELISA and IHC; <em>n</em> = 8) and others in which CWD was not detected (<em>n</em> = 111, including animals that survived the duration of the study) using GPS collar data from two distinct populations collared in central Wyoming, USA during 2018–2022. CWD and predation were the leading causes of mortality during our study (32 of 91 deaths attributed to CWD and 27 of 91 deaths attributed to predation). Deer infected with CWD moved slower and used lower elevation areas closer to rivers in the months preceding death compared with uninfected deer that did not succumb to starvation. Although CWD-infected deer and those that died of starvation moved at similar speeds during the final months of life, CWD-infected deer used areas closer to streams with less herbaceous biomass than deer that died of starvation. These behavioral differences may allow for the development of predictive models of disease status from movement data, which will be useful to supplement field and laboratory diagnostics or when mortalities cannot be quickly retrieved to assess cause-specific mortality. Furthermore, identifying individuals that are sick before predation events could help to assess the extent to which disease mortality is compensatory with predation. Finally, infected animals began to slow down around four months prior to death from CWD. Our approach for detecting the timing of infection-induced shifts in movement behavior may be useful in application to other disease systems to better understand the response of wildlife to infectious disease.</p>
FIGURE 3 in A review of "pyrite disease" for paleontologists, with potential focused interventions
FIGURE 3. Thermal expansion of the pyrite and hydroxylapatite unit cell volume, expressed as percent change in molar volume, using data from Hovis et al. (2014), Fei (1995) and Chrystall (1965). Data for the thermal dehydration and desulfidation of melanterite are overlaid (Swami and Prasad, 1980). Phase changes that occur at distinct temperatures are shown as a line. Changes that occur over a range of temperatures are shown as shaded areas with the darkest part at the greatest amount of dehydration.
Computational Investigation of Co-Aggregation and Cross-Seeding between Aβ and hIAPP Underpinning the Crosstalk in Alzheimer's Disease and Type-2 Diabetes
<p><span>The coexistence of Amyloid-β (Aβ) and human Islet Amyloid Polypeptide (hIAPP) in the brain and pancreas is associated with an increased risk of Alzheimer’s disease (AD) and type-2 diabetes (T2D) due to their co-aggregation and cross-seeding. Despite this, the molecular mechanisms underlying their interaction remain elusive. Here, we systematically investigated the cross-talk between Aβ and hIAPP using atomistic discrete molecular dynamics (DMD) simulations. Our results revealed that the amyloidogenic core regions of both Aβ (Aβ<sub>10–21</sub> and Aβ<sub>30–41</sub>) and hIAPP (hIAPP<sub>8-20</sub> and hIAPP<sub>22-29</sub>), driving their self-aggregation, also exhibited a strong tendency for cross-interaction. This propensity led to the formation of β-sheet-rich hetero-complexes, including potentially toxic β-barrel oligomers. The formation of Aβ and hIAPP hetero-aggregates did not impede the recruitment of additional peptides to grow into larger aggregates. Our cross-seeding simulations demonstrated that both Aβ and hIAPP fibrils could<a name="_Hlk163119646"></a> mutually act as seeds, assisting each other's monomers in converting into β-sheets at the exposed fibril elongation ends. The amyloidogenic core regions of Aβ and hIAPP, in both oligomeric and fibrillar states, exhibited the ability to recruit isolated peptides, thereby extending the β-sheet edges, with limited sensitivity to the amino acid sequence. These findings suggest that targeting these regions by capping them with amyloid-resistant peptide drugs may hold potential as a therapeutic approach for addressing AD, T2D, and their co-pathologies.</span></p>
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Fig. 3 in Molecular characterization of the re-emerging West Nile virus in avian species and equids in Israel, 2018, and pathological description of the disease
Fig. 3 Brain histopathology of WNV-infected horses. Perivascular cuffs composed of lymphocytes and plasma cells in the brain of two horses, characteristic of viral encephalitis (marked by arrows). a Horse no. Eq111 (324085). b Horse no. Eq117 (325903). 100× magnification
Fig. 2 in Molecular characterization of the re-emerging West Nile virus in avian species and equids in Israel, 2018, and pathological description of the disease
Fig. 2 Brain histopathology of WNV-infected long-eared owl (Asio otus) AV156. a A glial nodule in the brain stem (marked by an arrow). 100× magnification. b A glial nodule in the brain stem with few adjacent necrotic neurons, 400× magnification
Gene expression QTL mapping in stimulated iPSC-derived macrophages provides insights into common complex diseases.
<p>Many disease-associated variants are thought to be regulatory but are not present in existing catalogues of expression quantitative trait loci (eQTL). We hypothesise that these variants may regulate expression in specific biological contexts, such as stimulated immune cells. Here, we used human iPSC-derived macrophages to map eQTLs across 24 cellular conditions. We found that 76% of eQTLs detected in at least one stimulated condition were also found in naive cells. The percentage of response eQTLs (reQTLs) varied widely across conditions (3.7% - 28.4%), with reQTLs specific to a single condition being rare (1.11%). Despite their relative rarity, reQTLs were overrepresented (p=0.05, Fisher's exact test) among disease-colocalizing eQTLs. We nominated an additional 21.7% of disease effector genes at GWAS loci via colocalization of reQTLs, with 38.6% of these not found in the Genotype–Tissue Expression (GTEx) catalogue. Our study highlights the diversity of genetic effects on expression and demonstrates how condition-specific regulatory variation can enhance our understanding of common disease risk alleles.</p>
Iron Trace Elements Concentration in PM10 and Alzheimer's Disease in Lima, Peru: Ecological Study - dataset
<p>This dataset was created to evaluate the association between iron trace-elements concentration in PM10 with Alzheimer´s Disease cases in different districts in Lima, Peru. The database was constructed using open-access repositories of the Peruvian Ministry of Health and the Peruvian CDC.</p> <p>The uploaded datasets are in .dta and .csv formats.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.