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4,021 results for “Diagnoses”
Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes
<p>Type 1 diabetes is a chronic autoimmune disease resulting in an immune-mediated loss of pancreatic β-cells; however, an unbiased and reproducible profiling of type 1 diabetes-specific circulating immunome at disease onset has yet to be explored. In this study, fresh whole blood was collected from a pediatric cohort of 107 patients with new-onset type 1 diabetes, 85 relatives of patients with type 1 diabetes with 0-1 islet autoantibodies, 58 patients with celiac disease or autoimmune thyroiditis and 76 healthy controls. Up to 6 mL of blood was collected from each subject into a VACUETTE® TUBE 6 ml ACD-B (Greiner). Fresh whole blood underwent red blood cell lysis, was washed and stained with specific monoclonal antibodies. Fresh whole blood samples were stained with five panels of antibodies labelled as T cells, T&NK cells, B cells, Tregs and DCs/monos encompassing main subsets of T cells, NK cells, B cells, Tregs, DCs and monocytes detected using 26 surface markers and the intracellular marker forkhead box P3 (FoxP3); for the Treg panel, intracellular staining was performed after fixation and permeabilization. Cells were acquired on a BD FACSCanto-II flow cytometer equipped with FACSDiva software (Becton Dickinson, Franklin Lakes, NJ). </p>
GERDAT010 Dataset for literature search linked to publication "Information needs of older patients newly diagnosed with cancer"
<p>Dataset of the literature search belonging to the publication "Information needs of older patients newly diagnosed with cancer"</p>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p><span>Summary: </span></p> <p><span><span> </span>Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</span></p> <p><span><span> </span>The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</span></p>
Comparison of conventional IgE assay and measurement of specific IgE to hemocyanin for the diagnosis of adult crab allergy (Running tile: Utility of crab extracts and hemocyanin in diagnosing crab allergy)
<p>Summary:</p> <p> Specific IgE to hemocyanin was elevated in crab-allergic as compared to crab-tolerant patients.</p> <p> The combination of specific IgE to hemocyanin and conventional IgE assays improved specificity.</p>
Scripts from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms
<ol> <li>River assessments are predominantly based upon biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.</li> <li>Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various <i>symptoms</i> (macroinvertebrate metrics) and probabilistically link them to various potential <i>causes</i> of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian Network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. </li> <li>Water quality-related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. </li> <li> <i>Synthesis and applications.</i> Our results confirm that Bayesian inference can greatly assist the diagnosis of potential causes of river deterioration based upon a selection of diagnostic biological metrics. If integrated into a Bayesian Network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve ecological quality. Diagnostic Bayesian Networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. 28 July 2020</li> </ol>
Data from: A framework to diagnose the causes of river ecosystem deterioration using biological symptoms
<ol> <li>River assessments are predominantly based upon biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.</li> <li>Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various <i>symptoms</i> (macroinvertebrate metrics) and probabilistically link them to various potential <i>causes</i> of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian Network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. </li> <li>Water quality-related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. </li> <li> <i>Synthesis and applications.</i> Our results confirm that Bayesian inference can greatly assist the diagnosis of potential causes of river deterioration based upon a selection of diagnostic biological metrics. If integrated into a Bayesian Network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve ecological quality. Diagnostic Bayesian Networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures. 28-Jul-2020</li> </ol>
Identification of co-infections in a cohort of patients diagnosed with Lyme Disease
<p>Serlogy test data used in the study: Identification of co-infections in a cohort of patients diagnosed with Lyme Disease</p>
Fig. 2 in Morphological diagnoses of higher taxa in Ophiuroidea (Echinodermata) in support of a new classification
Fig. 2. Examples of skeletal structures of brittlestars. A. Dorsal disc with scales and plates in Amphiura Forbes, 1843 (P). B. Oral frame with teeth (T) and apical tooth cluster (APC) in Ophiocoma L. Agassiz, 1836. C. Oral frame with infradental oral papillae (IP) in Ophioplax Lyman, 1875. D. Disc spines in Ophiacantha Müller & Troschel, 1842. E. Disc granules in Ophiocoma L. Agassiz, 1836. F. Disc tubercles in Acrocnida Gislén, 1926. G. Dental plate (DP) in Ophiura Lamarck, 1801, with teeth attached. H. Dental plate with sockets for regular teeth (TS) and for apical tooth cluster (APC) in Ophiocoma L. Agassiz, 1836. I–J. Lateral arm plate in Amphiura Forbes, 1843. I. External view with arm spine articulations (ASA). J. Internal view. Scale bars in millimetres.
Fig. 1 in Morphological diagnoses of higher taxa in Ophiuroidea (Echinodermata) in support of a new classification
Fig. 1. Summary phylogenetic tree of the ophiuroid higher taxonomy. Modified from O'Hara et al. (2017: fig. 1), with Ophiobyrsidae included based on O'Hara et al. (2017: fig. S3) sample Ophiuroidea_ sp_IE.2009.1713 (= Ophiophrixus_confinis) and additional unpublished exon-capture data on samples Ophiobyrsa_rudis_F222711 and Ophiosmilax_sp_IE.2207.6967. All-sites PLRS/RAxML tree with node support and age confidence intervals (coloured bars). Node support shows all-sites RAxML BS followed by the proportion of the data subset trees in agreement. For all other nodes support was 100/1. The root was fixed according to O'Hara et al. (2014) at 270 Ma.
Infrared Spectroscopy for Diagnosing Superlattice Minibands in Magic-angle Twisted Bilayer Graphene
Open the record for dataset details and reuse information.
Figure 7 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 7. Summary of Random Forests classifications for each empirical comparison. Each row shows results from the stratum with the smallest fraction of individuals correctly classified, with comparisons labeled by their taxonomic codes as listed in Table 2. Colors identify comparison type as species (blue), subspecies (green), and populations (red). Points show the fraction of individuals correctly classified with probabilities> 50% (PD50, circles), and> 95% (PD95, triangles). Thin colored lines show 95% confidence intervals (CI) around PD50 estimates. Gray bars show range of a priori random classification rates based on individual size (left) to maximum possible classification rates based on shared haplotypes (right).
Figure 6 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 6. Frequency distributions of the change in observed diagnosability (x-axis) in the simulated data for increasing levels of the probability of misstratification (vertical panels). Figures on the left and right columns are censored by data sets for original diagnosability ≤50% and>50%, respectively.
Figure 5 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 5. Two-dimensional GAM fits of theta (Ɵ), number of migrants (Nem), and divergence time in generations (T) from Model 2 simulated data. From left to right, columns show results from models without migration (m = 0), with migration and Nem <1, and Nem ≥ 1. Colors indicate model prediction of percent correctly classified.
Figure 4 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 4. GAM fit of number of migrants (Nem) from Model 2 parameters. Solid line shows median value of predicted percent correctly classified, and shaded area shows 95% CI. The switch from bimodal distribution to a normal distribution occurs at Nem = 1 (log10Nem = 0).
Figure 3 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 3. Two-dimensional GAM fits of effective population size (Ne), divergence time in generations (T), and mutation rate (µ) from Model 1 simulated data. Results from models without migration to the left and those with migration to the right. Colors indicate model prediction of percent correctly classified.
Figure 1 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 1. (A) Distribution of a hypothetical character for two putative subspecies (red and blue) demonstrating minimum overlap necessary to satisfy 75% rule of Amadon (1949). Character is continuous on the x-axis. Dashed lines indicate the point at which 75% of each distribution is outside of 99%+ of the other. Solid line indicates point of overlap where 97% of both distributions are outside one another. (B) Probability of membership to subspecies for specimens having values along the character axis. Probability is based on the ratio of the distribution frequencies at each point along the x-axis, with a 50:50 probability occurring at the threshold point.
WiDS mortality dataset - APACHE diagnoses enriched
<p>The WiDS mortality dataset was modified, adding the APACHE diagnoses using the original column "apache_3j_diagnosis_code".</p> <p>This dataset is a merge from:</p> <ol> <li><strong>Mortality data</strong>: https://www.kaggle.com/competitions/widsdatathon2020/data</li> <li><strong>APACHE</strong>: https://www.kaggle.com/datasets/danofer/apache-iiij-icu-diagnosis-codes?select=icu-apache-Subdiagnosis-codes-ANZICS.csv</li> </ol>
Brain functional connectivity data in anesthetized participants and patients with neuropathological or psychiatric diagnoses
<p>Five fMRI datasets were collected from independent research sites including: propofol deep sedation (PDS; drug effect site concentration= ~2.4 μg/ml) in Dataset-1, propofol general anesthesia (PGA; drug effect site concentration= 4.0 μg/ml) in Dataset-2, ketamine anesthesia (KA) in Dataset-3, unresponsive wakefulness syndrome (UWS) in Dataset-4, and schizophrenia (SCHZ), bipolar disorder (BD), and attentional deficit hyperactivity disorder (ADHD) in Dataset-5. Following fMRI data preprocessing, the fMRI time courses were extracted from 400 cortical areas according to a well-established brain parcellation scheme (Schaefer's 400 ROIs). A connectivity matrix was then calculated using Pearson correlation resulting in a 400x400 connectivity matrix for each participant and each condition.</p>
Data set - What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study
<p><strong>Data set from- What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study</strong></p> <p><strong>Abstract of the study: </strong>The treatment of cancer can have a significant impact on quality of life in older patients and this needs to be taken into account in decision making. However, quality of life can consist of many different components with varying importance between individuals. We set out to assess how older patients with cancer define quality of life and the components that are most significant to them. This was a single-centre, qualitative interview study. Patients aged 70 years or older with cancer were asked to answer open-ended questions: What makes life worthwhile? What does quality of life mean to you? What could affect your quality of life? Subsequently, they were asked to choose the five most important determinants of quality of life from a predefined list: cognition, contact with family or with community, independence, staying in your own home, helping others, having enough energy, emotional well-being, life satisfaction, religion and leisure activities. Afterwards, answers to the open-ended questions were independently categorized by two authors. The proportion of patients mentioning each category in the open-ended questions were compared to the predefined questions. Overall, 63 patients (median age 76 years) were included. When asked, “What makes life worthwhile?”, patients identified social functioning (86%) most frequently. Moreover, to define quality of life, patients most frequently mentioned categories in the domains of physical functioning (70%) and physical health (48%). Maintaining cognition was mentioned in 17% of the open-ended questions and it was the most commonly chosen option from the list of determinants (72% of respondents). In conclusion, physical functioning, social functioning, physical health and cognition are important components in quality of life. When discussing treatment options, the impact of treatment on these aspects should be taken into consideration.</p> <p><strong>Reference of research paper: </strong>Seghers PAL, Kregting JA, van Huis-Tanja LH, Soubeyran P, O'Hanlon S, Rostoft S, Hamaker ME, Portielje JEA. What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study. <em>Cancers</em>. 2022; 14(5):1123. https://doi.org/10.3390/cancers14051123</p> <p><strong>Content of the data set: </strong>The first Tab describes what questions were asked, the second tab shows all individual anonymised answers to the open questions, the fourth shows the definitions that were used to classify all answers. Q1-Q4 show how the answers were categorised. </p>
SLR - Diagnosing Tropical Diseases
<p>This file contains the dataset analyzed for the paper: </p> <p>A Systematic Review of Soft Computing Techniques and Their Application to the Diagnosis of Tropical Diseases</p>
ScienceDex guides
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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.