Find research datasets worth reusing

Identifying biomarkers for chronic obstructive pulmonary disease in the salivary metabolome.

Background: The lack of specificity in spirometry and accuracy in ‘pre-disease states’, and elderly indicates the need for other accurate, inexpensive and non-invasive tests for diagnosis, triage severity and particularly for choosing and monitoring response to treatments in chronic obstructive pulmonary disease (COPD). We compared salivary metabolomic signatures from patients with COPD and controls across a range of severity of airflow obstruction. <a name="_61k4y1plsv5"></a>Methods 45 people with COPD (mean age 66.1 ± 23 years, forced expiratory volume [FEV1] 47+15% predicted) and 48 healthy controls (mean age 56.0 +16.2 years, FEV1 90+23 % predicted) provided saliva that was assessed by flow infusion electrospray mass spectrometry (FIE-MS). Spectra were interrogated using an online library package. Data (patient data and metabolomic outputs) are available from this site.  <a name="_j99pcjszp0an"></a>Results Four potential biomarkers identified the presence of COPD with a sensitivity of 73%, specificity of 72%.  Six metabolites predicted FEV1 % in the COPD cohort (P < 0.001, R2 > 0.3, AUC > 0.7) whilst a range of multivariate approaches targeted six metabolites linked to GOLD stage (P < 0.001, AUC > 0.7). Identification of the metabolites suggested changes in pterin biosynthesis, lipid processing, nucleotide metabolism and melatonin in COPD patients. <a name="_2n4ucvxs4l1n"></a>Conclusions Metabolic fingerprinting of saliva samples could differentiate COPD from age matched controls and inform COPD severity of airflow obstruction. Potential biomarkers are suggested which could inform the diagnosis and monitoring of COPD.   <h2> </h2>

openmit-licenseDec 2023View details →

Pathformer: a biological pathway informed Transformer for disease diagnosis and prognosis using multi-omics data

Integrating multi-omics data offers a more comprehensive view of gene regulation, which would be helpful in achieving accurate diagnosis of diseases like cancer. To improve the accuracy of disease diagnosis and prognosis, we developed Pathformer, a multi-omics integration method for both tissue and liquid biopsy data. We implemented Pathformer's network architecture using the “PyTorch” package in Python v3.6.9, and our codes can be found in the GitHub repository (https://github.com/lulab/Pathformer). This repository contains preprocessed TCGA dataset data, preprocessedliquid biopsy dataset data, result of Pathformer and comparison_methods, mentioned in GitHub project and article.

Voxel-wise maps for the paper: Longitudinal associations of magnetic susceptibility with clinical severity in Parkinson's disease

This upload contains voxel-wise group level QSM data, and statistical maps for group level results associated with the paper: Longitudinal associations of magnetic susceptibility with clinical severity in Parkinson's disease. The assocaited code for reproducing these statistical maps can be found here: <a href="https://github.com/gecthomas/QSM_PD_longitudinal">gecthomas/QSM_PD_longitudinal (github.com)</a>

Rare Diseases hand-annotated news articles: Angelman, De Lange, Fragile X, Kleefstra

This dataset was produced in 2023 from the data collected throughout 2023 from Event Registry (news) for the development of the Rare Diseases Mining project (https://idefine-europe.org/medline) The data is distributed across 4 specific diseases supporting the research paper "Automatic text classification and interactive data visualization of published scientific and news articles on Rare Diseases" The available data comes in the file formats: CSV - the hand annotation of the news articles in TXT with 5 to 10 MeSH headings This work was prepared by Joao Pita Costa (researcher) and curated by Tanja Zdol&scaron;ek Draksler (domain expert)

opencc-byApr 2018View details →

Model of a diseased eye

A list describing the various pathologies. This eye could be a model of allergic conjunctivitis. From the collection of the museum library at the UCL institute of ophthalmology. Source: Objaverse 1.0 / Sketchfab

Data and code for "Shrubs inhibit plant diseases through reducing herbaceous biomass in alpine meadows"

Supplementary Data and code for "Shrubs inhibit plant diseases through reducing herbaceous biomass in alpine meadows"

Extended data for behavioural change for Parkinson's disease: A randomised controlled feasibility study to promote physical activity and exercise adherence among people with Parkinson's, study protocol

Background: Parkinson's is a common progressive neurological condition characterized by motor and non-motor deficits. Physical activity and exercise can improve health, but many people with Parkinson's have trouble reaching the recommended dosage. Our recent literature review found improvements in exercise adherence with behavioural change interventions, but it remains unclear which are most effective. Further qualitative research and patient and public involvement has informed a novel behavioural change support intervention to be tested alongside an existing exercise program. Objective: To examine the feasibility of behavioural change support techniques delivered alongside an exercise programme to improve physical activity, function, and self-efficacy in PwP (and study procedures) to inform a future pilot RCT trial. Methods: A parallel-arm single blinded randomised feasibility study. Twenty participants with Parkinson's (Hoehn and Yahr stage 1-3) will be recruited from a physiotherapy primary-care waiting list. Following written consent, and baseline assessment, the participants will be randomly allocated to the intervention (n=10) or the control group (n=10). Both groups will receive usual care, which includes a weekly program of a multidisciplinary education, a supervised exercise class and a prescribed home exercise program. The intervention group will receive additional behavioural change techniques, targeting behaviour regulation, belief about capabilities and social influences. Class and home exercise adherence, behavioural component uptake and adherence, and negative events will be recorded. Outcomes will include enrolment and maintenance rates, physical function, falls, physical activity, and exercise self-efficacy measured pre- and post- the 12- week program (in-person). Surveys will be used to compare experiences and satisfaction between groups. Exit interviews will be completed with the intervention group only, exploring their experience of the behavioural change techniques. Discussion: The results will help inform a future pilot RCT, based on the intervention acceptability, consent rate, maintenance, and protocol integrity.

Impaired intracellular Ca2+ signaling contributes to age-related cerebral small vessel disease in Col4a1 mutant mice

Humans and mice with mutations in COL4A1 and COL4A2 manifest hallmarks of cerebral small vessel disease (cSVD). Mice with a missense mutation in Col4a1 at amino acid 1344 (Col4a1+/G1344D) exhibit age-dependent intracerebral hemorrhage (ICH) and brain lesions. Here we report that this pathology was associated with the loss of myogenic vasoconstriction, an intrinsic vascular response essential for the autoregulation of cerebral blood flow. Electrophysiological analyses showed that the loss of myogenic constriction resulted from blunted pressure-induced smooth muscle cell (SMC) membrane depolarization. Further, we found that dysregulation of membrane potential was associated with impaired Ca2+-dependent activation of large-conductance Ca2+-activated K+ (BK) and transient receptor potential melastatin 4 (TRPM4) cation channels linked to disruptions in sarcoplasmic reticulum (SR) Ca2+ signaling. Treating Col4a1+/G1344D mice with 4-phenylbutyrate, a compound that promotes the trafficking of misfolded proteins and alleviates SR stress, restored SR Ca2+ signaling and BK and TRPM4 channel activity, prevented loss of myogenic tone, and reduced ICH. We conclude that alterations in SR Ca2+ handling that impair membrane potential regulating ion channel activity result in dysregulation of SMC membrane potential and loss of myogenic tone contributing to age-related cSVD in Col4a1+/G1344D mice.

Individual and temporal variation in movement patterns of wild alpine reindeer and implications for disease management

Animal behaviour is important for prevalence and outbreaks of infectious diseases, for instance by affecting individual interactions. Increasing the knowledge of individual movement patterns can provide better insight into disease prevalence and spread, helping to target efforts to minimise disease outbreaks. Chronic wasting disease (CWD) is a fatal prion disease affecting cervids. CWD is transmitted by animal-to-animal contact and through the environment, thus individual variation in space use and social associations may influence disease transmission patterns and infection risk. CWD was detected in Norwegian alpine reindeer (Rangifer tarandus) in 2016, and eradication of the infected population was implemented. A 3:1 infection rate between males and females suggests sex-specific behavioural drivers. We utilised an extensive individual-based dataset of 149 GPS-marked wild reindeer to investigate individual variation in movement patterns in terms of inter- and intra-annual home range size and site fidelity, and variation in home range overlap and distances between individuals. We aimed to identify patterns which could indicate higher potential disease risk. Females had larger annual and seasonal home ranges than males, except during calving and rut. Greater home range overlaps and shorter between-individual distances were found between same-sex individuals than different-sex individuals, except during the rut. Accordingly, the rut season stands out with greater male home ranges, greater home range overlap and shorter distances between males and between males and females, which could indicate that this season is critical for disease transmission. Measures to prevent disease spread should lower contact rates, e.g., by reducing the abundance of adult males before they mix with other groups during the rut. This can be achieved for instance by allowing earlier hunt on adult males when they are distributed in small male groups, to reduce the transmission risk and keep disturbance of other individuals low.

opencc-by-4.0Aug 2023View details →

Single-cell RNA sequencing reveals dysregulated cellular programs in the inflamed epithelium of Crohn's disease patients.

Crohn’s disease (CD) is a complex inflammatory disorder of incompletely understood molecular aetiology. We generated a large single-cell RNA sequencing dataset from the terminal ileal biopsies of two independent cohorts comprising a total of 50 CD patients and 71 healthy controls. We performed transcriptomic analyses to reveal genes, cell types and mechanisms perturbed in CD, leveraging the power of the two cohorts to confirm our findings and assess replicability. In addition to mapping widespread alterations in cytokine signalling, we provide evidence of pan-epithelial upregulation of MHC class I genes and pathways in CD. Using non-negative matrix factorization we revealed intra- and inter-cellular upregulation of expression programs such as G-protein coupled receptor signalling and interferon signalling, respectively, in CD. We observed an enrichment of CD heritability among marker genes for various activated T cell types and myeloid cells, supporting a causal role for these cell-types in CD aetiology. Comparisons between our discovery and replication cohort revealed significant variation in differential gene-expression replicability across cell types. B, T and myeloid cells showed particularly poor replicability, suggesting caution should be exercised when interpreting unreplicated differential gene-expression result in these cell types. Overall, our results provide a rich resource for identifying cell-type specific biomarkers of Crohn’s disease and identifying genes, cell types and pathways that are causally and replicably associated with disease.

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

Hereditary diseases manifest clinically in certain tissues, however their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in 1,459 hereditary diseases. Overall, we identified 114 cell types affected by 1,140 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechanisms and cellular vulnerability.

opencc-zeroFeb 2024View details →

MERFISH and snRNAseq analysis of healthy and disease human liver

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cell types and their heterogeneity within the human liver, but the spatial organization at single-cell resolution has not yet been described. Here we apply multiplexed error robust fluorescent in situ hybridization (MERFISH) to map the zonal distribution of hepatocytes, resolve subsets of macrophage and mesenchymal populations, and investigate the relationship between hepatocyte ploidy and gene expression within the healthy human liver. We next integrated spatial information from MERFISH with the more complete transcriptome produced by single- nucleus RNA sequencing (snRNA-seq), revealing zonally enriched receptor-ligand interactions. Finally, analysis of fibrotic liver samples identified two hepatocyte populations that are not restricted to zonal distribution and expand with injury. Together these spatial maps of the healthy and fibrotic liver provide a deeper understanding of the cellular and spatial remodeling that drives disease which, in turn, could provide new avenues for intervention and further study.

opencc-zeroFeb 2024View details →

Low-dose biliatresone treatment of pregnant mice causes subclinical biliary disease in their offspring: Evidence for a spectrum of neonatal injury

Biliary atresia is a neonatal disease characterized by damage, inflammation, and fibrosis of the liver and bile ducts and by abnormal bile metabolism. It likely results from a prenatal environmental exposure that spares the mother and affects the fetus. Our aim was to develop a model of fetal injury by exposing pregnant mice to low-dose biliatresone, a plant toxin implicated in biliary atresia in livestock, and then to determine whether there was a hepatobiliary phenotype in their pups. Pregnant mice were treated orally with 15 mg/kg/d biliatresone for 2 days. Histology of the liver and bile ducts, serum bile acids, and liver immune cells of pups from treated mothers were analyzed at P5 and P21. Pups had no evidence of histological liver or bile duct injury or fibrosis at either timepoint. In addition, growth was normal. However, serum levels of glycocholic acid were elevated at P5, suggesting altered bile metabolism, and the serum bile acid profile became increasingly abnormal through P21, with enhanced glycine conjugation of bile acids. There was also immune cell activation observed in the liver at P21. These results suggest that prenatal exposure to low doses of an environmental toxin can cause subclinical disease including liver inflammation and aberrant bile metabolism even in the absence of histological changes. This finding suggests a wide potential spectrum of disease after fetal biliary injury.

opencc-by-4.0Feb 2024View details →

Temporal increase in ticks and pathogen prevalence in the small mammal part of the Lyme disease cycle in northern Europe

Data and Scripts for our research article "Temporal increase in ticks and pathogen prevalence in the small mammal part of the enzootic Lyme disease cycle in northern Europe"

opencc-by-4.0Feb 2024View details →

ALAMEDA Data: Bridging the Early Diagnosis and Treatment Gaps of Brain Diseases (Parkinson's Disease, Multiple Slerosis and Stroke)

ALAMEDA is an Horizon 2020 Research and Innovation project that aims to bridge the early diagnosis and treatment gap of brain diseases via smart, connected, proactive and evidence-based technological interventions. Its vision is to research and prototype new generation Artificial Intelligence (AI) systems to support brain disorders patients' healthcare, focusing on Parkinson's Disease (PD), Multiple Sclerosis (MS) and Stroke. To this end, three (one for each disease) small scale validation pilots were performed in real world settings. Throughout these pilots, various types of data, such as accelerometer, gyroscopic, heart rate, etc., were collected via smart wearable sensors from the patients enrolled. The smart devices that were employed include: a Fitbit smartwatch, a GENEActiv smart bracelet, Novel Loadsol insole sensors and a prototype smart belt with triaxial accelerometers and gyroscopes embedded. Moreover, the patients underwent several clinical assessments and filled in numerous both disease-specific and non-disease-specific questionnaires. In this record, both raw and processed sensory data are combined with both clinical and patient reported outcomes (PROs) to form different disease-specific datasets. More specifically: <ul> <li>For Parkinson's disease: Three datasets are provided (one for tremor detection, one for dyskinesia detection, and one for Hoehn & Yahr score estimation) alongside the vertical ground reaction force recordings.</li> <li>For Multiple Sclerosis: Two datasets are provided (one for Expanded Disability Status Scale (EDSS) scores classification and one that accumulates clinical data and individual scores from various MS-related questionnaires) alongside the vertical ground reaction force and the smart belt recordings.</li> <li>For Stroke: Two datasets are provided (one for rehabilitation exercises' recognition and one for walking classification, both with and without manual annotations) alongside the smart belt recordings.</li> </ul> More information about the datasets provided can be found in the respective READ ME files that are included in the current record.

opencc-by-4.0Mar 2024View details →

Parkinson's Disease Tremor Dataset - ALAMEDA

The ALAMEDA_PD_tremor_dataset.csv contains 92 features extracted from raw accelerometer data after pre-processing, 4 tremor-related labels and some other metadata. In total, it includes 99 columns: <ol> <li> The first two columns correspond to the start_timestamp and the end_timestamp of the time window from which the respective features have been extracted. </li> <li> The third column corresponds to the subject_id, which is used to uniquely identify PD patients enrolled in the current study. </li> <li> The next 92 columns correspond to features extracted from raw triaxial accelerometer data collected with the GENEActiv smart bracelets throughout 30-min MDS-UPDRS assessment during in-clinic visits, after applying some preprocessing steps. First, the accelerometer signals were band-pass filtered [2.5 Hz, 12.5 Hz] to enable tremor detection. Then, the magnitude and the first principal component of the filtered signals were computed to attenuate the dependency on sensor placement and orientation. Finally, the transformed signals were segmented into time windows of 2048 samples (or 20.48 sec) with 50% overlap. Then, 92 features were extracted in both time and frequency domains. Spectral features were extracted after applying Fast Fourier Transform. The full list of the extracted features is demonstrated in the table below. These features can feed Machine Learning models to predict the presence/absence of PD tremor. </li> <li> The final 4 columns correspond to tremor-related labels (Constancy_of_rest, Kinetic_tremor, Postural_tremor and Rest_tremor). They derive from the respective MDS-UPDRS III annotations, after transforming them to make them suitable for binary classification. More specifically, zero scores remained 0 to indicate the absence of tremor while positive scores were transformed to 1 to indicate the presence of tremor. Each of these columns can be used as a target to be predicted with the help of Machine Learning models. </li> </ol>

opencc-by-4.0Nov 2023View details →

Formalized information from the study protocols of three decentralized studies on rare diseases within the CORD project

The file contains formalized information from the study protocols of decentralized studies on rare diseases within the CORD project. This includes the diagnoses that are coded with ICD-10-GM. The following diseases or use cases are described:<ul><li>Cystic Fibrosis and Pregnancy or Delivery</li><li>Phenylketonuria, Comorbidities and Pregnancy or Delivery</li><li>Kawasaki and Pediatric Inflammatory Multisystem Syndrome (PIMS)</li></ul>

opencc-by-4.0Mar 2024View details →

In silico database of ~500 000 virtual patients with diverse cardiovascular disease

The presented dataset contains a large virtual cohort of more than 50'000 heart failure patients characterized by realistic traces of volumes, pressures, flows, and regional mechanics. We used the well-established CircAdapt model <a href="https://www.circadapt.org/">(https://www.circadapt.org/</a> ,  <a href="http://framework.circadapt.org/">http://framework.circadapt.org/</a>) of the human heart and circulation to simulate a large cohort of virtual patients, covering a wide range of HF-related disease heterogeneity and severity.  In the construction of virtual patients, generating a variety of parameter sets for the initial population is essential and can be accomplished using various sampling techniques. In our investigation, we utilized the Sobol-low discrepancy sequence to ensure uniformity across the high-dimensional parameter space. For a more detailed explanation please refer to the document "MARCIUS_deliverable_D1.5_VirtualDatabase.pdf" Acknowledgments: This work was supported by the European Union's Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie grant agreement No. 86074, "MARCIUS - MARie Curie Intelligent UltraSound"(<a href="https://www.marcius-project.com/">https://www.marcius-project.com/</a>). MARCIUS rationale was to develop a comprehensive in silico simulation platform comprising both the generation of virtual patients (presented dataset) and their associated realistic image data. Such approach would make it possible to learn the most relevant patterns within a wide representative set of patients to lead the training of ML-based image processing algorithms in order to analyze real-world clinical data.

opencc-zeroMar 2024View details →

Antibodies to Borrelia burgdorferi and Bartonella species in serum and synovial fluid from people with rheumatic diseases

Vector-borne infections may underlie some rheumatic diseases, particularly in people with joint effusions. The aim of this study was to compare serum and synovial fluid antibodies to B. burgdorferi and Bartonella spp. in patients with rheumatic diseases. This observational, cross-sectional study examined paired synovial fluid and serum specimens collected from 110 patients with joint effusion between October 2017 and January 2022. Testing for antibodies to B. burgdorferi (using CDC criteria) and Bartonella spp. via two Indirect Fluorescent Antibody [IFA] assays, was performed as part of routine patient care. There were 30 participants (27%) with positive two-tier B. burgdorferi serology, and 26 participants (24%) with IFA seroreactivity (≥1:256) to B. henselae and/or B. quintana. Both B. burgdorferi IgM and IgG were detected more frequently in synovial fluid than serum: 27% of patients were either IgM or IgG positive in synovial fluid, compared to 15.5% in serum (p = 0.048). Conversely, B. henselae and B. quintana antibodies were detected more frequently in serum than synovial fluid; overall only 2% of patients had positive IFA titers in synovial fluid, compared to 24% who had positive IFA titers in serum (p <0.001). There were no significant associations between B. burgdorferi or Bartonella spp. seroreactivity with any of the clinical rheumatological diagnoses. This study provides preliminary support for the importance of synovial fluid antibody testing for documenting exposure to B. burgdorferi, but not for documenting exposure to Bartonella spp.