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6,423 results for “biomarkers”
Preprocessing scripts and data for study: Pathway-Based Subnetworks Enable Cross-Disease Biomarker Discovery
<p>Supplementary File 1 containing preprocessing scripts and data for converting pathway databases into subnetworks.</p>
S34 | EXPOSOMEXPL | Biomarkers from Exposome Explorer
<p>This is the collection associated with list S34 EXPOSOMEXPL on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S34 | EXPOSOMEXPL | <strong>Biomarkers from Exposome Explorer</strong></p> <p>Exposome Explorer Biomarkers Download <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/ExposomeExplorer-biomarkers.xlsx">XLSX</a> (24/01/2019)<br> EXPOSOMEXPL Mapped <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/EXPOSOMEXPL_wDTXSIDs_24012019.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/EXPOSOMEXPL_wDTXSIDs_24012019.xlsx">XLSX</a> (24/01/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/exposomexpl">EXPOSOMEXPL List</a></p> <p>EXPOSOMEXPL <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/EXPOSOMEXPL_InChIKeys_24012019.txt">InChIKeys</a> (24/01/2019)</p> <p>The Exposome-Explorer (<a href="http://exposome-explorer.iarc.fr/">http://exposome-explorer.iarc.fr/</a>) is dedicated to biomarkers of exposure to environmental risk factors for diseases (Neveu <em>et al</em> 2017, DOI: <a href="http://doi.org/10.1093/nar/gkw980">10.1093/nar/gkw980</a>). Provided by Reza Salek and Vanessa Neveu (IARC), mapping files to all discrete chemicals by A. Williams/E. Schymanski. </p>
Dataset to the article: Effects of toxicgenic cyanobacteria and elodea on physiological biomarkers in the amphipod Gmelinoides fasciatus and bivalve Unio pictorum (mesocosm study)
<p>Table of source data to the manuscript titled “Effects of toxicgenic cyanobacteria and elodea on physiological biomarkers in the amphipod <em>Gmelinoides fasciatus</em> and bivalve <em>Unio pictorum</em> (mesocosm study)”.</p>
Source ELISA data for the manuscript "Restrained expansion of the recall germinal center response as biomarker of protection for influenza vaccination in mice"
<p>This repository contains the source ELISA data for the manuscript "Restrained expansion of the recall germinal center response as biomarker of protection for influenza vaccination in mice" currently under review by PLOS ONE.</p> <p>It supports the following figures:</p> <p>Fig 4A: rHA ELISA data miniHA study.xlsx<br> Fig 4B: Competition ELISA data miniHA study.xlsx<br> S7 Fig: Competition ELISA data POC study.xlsx</p> <p> </p> <p>Files include Raw OD's per plate and reported values analysis. </p> <p> </p>
Original Data of Paper: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis
<p>Paper title: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis. Our paper was submitted to PeerJ recently. This data file is the original data of this study which contains all the original data involved in this work.</p>
Figure 1 in Investigations of the nervous system biomarkers in the brain and muscle of freshwater fish (Oreochromis niloticus) following accumulation of nanoparticles in the tissues
Figure 1. TEM images of muscle (A) and brain (B) tissue samples of control fish (O. niloticus).
TNM classification-based framework for the identification of metastatic progression biomarkers in pancreatic cancer dataset
<p>This data was curated from TCGA and used in the research article titled "TNM classification-based framework for the identification of metastatic progression biomarkers in pancreatic cancer". Gene sets used from MSigDB were also included.</p>
Validation of urine p-cresol glucuronide as renal cell carcinoma non-invasive biomarker
<p><strong>Description of the study</strong>: Renal cell carcinoma (RCC) stands among the most lethal urological malignancies. Most RCCs are incidentally diagnosed as initial symptoms are unspecific. Novel, minimally-invasive diagnostic and prognostic methods for RCC are needed, ideally in urine.</p> <p>Using UPLC-Q-ToF MS untargeted metabolomic analysis in urine, we previously revealed p-cresol glucuronide as potential RCC diagnostic marker. Additionally, urine samples one-year post-nephrectomy revealed isobutyryl-L-carnitine and L-proline betaine as potential RCC prognostic markers. Our present aim was to validate these differences in an independent cohort of RCC patients and healthy controls to strengthen their value as non-invasive biomarkers.</p> <p>In an independent cohort of 69 RCC patients and 52 controls we validated an increase in p-cresol glucuronide in urine from patients at diagnosis compared to controls (<em>P</em>=0.0043). It remained increased one-year post-nephrectomy (<em>P</em>=0.0288). The value of p-cresol glucuronide for RCC diagnosis was assessed with ROC curves analysis (AUC=0.66, 95% Confidence Interval 0.56-0.76). The role of isobutyryl-L-carnitine and <a name="_Hlk172707445"></a>L-proline betaine as prognostic markers could not be validated and will require a larger cohort.</p> <p>Our findings confirm the value of p-cresol glucuronide in urine as diagnostic marker for RCC in an independent cohort. This non-invasive method holds promise for enhancing patient care by reducing the need for potentially risky diagnostic procedures. Further metaproteomics-oriented approaches towards the tyrosine oxidation pathway and microbiota metagenomics studies may promote a holistic management of RCC.</p> <p><strong>Description of the data:</strong></p> <p>We provided data in .d format adquired with Agilent.</p> <p> </p> <p> </p>
Dataset related to the article "Cardiac Biomarkers and Autoantibodies in Endurance Athletes: Potential Similarities with Arrhythmogenic Cardiomyopathy Pathogenic Mechanisms"
<p>This record contains raw data related to the article “Cardiac Biomarkers and Autoantibodies in Endurance Athletes: Potential Similarities with Arrhythmogenic Cardiomyopathy Pathogenic Mechanisms". </p> <p>The "Extreme Exercise Hypothesis" states that when individuals perform training beyond the ideal exercise dose, a decline in the beneficial effects of physical activity occurs. This is due to significant changes in myocardial structure and function, such as hemodynamic alterations, cardiac chamber enlargement and hypertrophy, myocardial inflammation, oxidative stress, fibrosis, and conduction changes. In addition, an increased amount of circulating biomarkers of exercise-induced damage has been reported. Although these changes are often reversible, long-lasting cardiac damage may develop after years of intense physical exercise. Since several features of the athlete's heart overlap with arrhythmogenic cardiomyopathy (ACM), the syndrome of "exercise-induced ACM" has been postulated. Thus, the distinction between ACM and the athlete's heart may be challenging. Recently, an autoimmune mechanism has been discovered in ACM patients linked to their characteristic junctional impairment. Since cardiac junctions are similarly impaired by intense physical activity due to the strong myocardial stretching, we propose in the present work the novel hypothesis of an autoimmune response in endurance athletes. This investigation may deepen the knowledge about the pathological remodeling and relative activated mechanisms induced by intense endurance exercise, potentially improving the early recognition of whom is actually at risk.</p>
APOE4 is associated with elevated blood lipids and lower levels of innate immune biomarkers in a tropical Amerindian subsistence population
<p>In post-industrial settings, <i>APOE4</i> is associated with increased cardiovascular and neurological disease risk. However, the majority of human evolutionary history occurred in environments with higher pathogenic diversity and low cardiovascular risk. We hypothesize that in high-pathogen and energy-limited contexts, the <i>APOE4</i> allele confers benefits by reducing innate inflammation when uninfected, while maintaining higher lipid levels that buffer costs of immune activation during infection. Among Tsimane forager-farmers of Bolivia (N=1266), <i>APOE4</i> is associated with 30% lower C-reactive protein, and higher total cholesterol and oxidized-LDL. Blood lipids were either not associated, or negatively associated with inflammatory biomarkers, except for associations of oxidized-LDL and inflammation which were limited to high BMI adults. Further, <i>APOE4</i> carriers maintain higher levels of total and LDL cholesterol at low BMIs. These results suggest the relationship between <i>APOE4</i> and lipids may be beneficial for pathogen-driven immune responses, and unlikely to increase cardiovascular risk in an active subsistence population.</p>
Raw data and media: Tetraspanins are unevenly distributed across single extracellular vesicles and bias sensitivity to multiplexed cancer biomarkers
<p>Raw datasets and media accompanying the manuscript: T<strong>etraspanins are unevenly distributed across single extracellular vesicles and bias sensitivity to multiplexed cancer biomarkers</strong>, published in the Journal of Nanobiotechnology </p>
Research data supporting "Detection of microRNA biomarkers via inhibition of DNA-mediated liposome fusion"
<p>Raw research data supporting Jumeaux, C. et al., Nanoscale (2018), DOI: 10.1039/C8NA00331A .</p>
Predictive modelling of brain metastasis risk and non-invasive biomarker detection using DNA methylation signatures
<p>Methylated cell-free DNA was sequenced for 123 BM plasma and compared to plasma methylomes from 107 gliomas, central nervous system (CNS) lymphomas (CNSL), and non-CNS tumor controls. Plasma methylome-based classifiers of BM from other entities were built in fifty 80% discovery set iterations of 92/123 BM samples. External publicly-available tissue methylation data on 442 LUAD, 85 BM, and 146 glioma/CNSL/control samples were acquired for validation and the remaining 31/123 BM plasma samples were used for additional validation.</p>
Biomarker indices and concentrations and biomarker-based temperature estimates from the Iberian Margin core MD95-2042, composite atmospheric temperature record from Greenland, and stacks of delta 18Oice and atmospheric temperature records from three Antarctic sites
<p>Core MD95-2042 alkenone and GDGT data: This dataset provides the following information for core MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C<sub>37</sub> alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT-2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. Sediment samples were taken every 5 cm from core MD95-2042 and homogenized before lipid extraction. The lipid extracts were splitted into two fractions: one for alkenone analysis by gas chromatography coupled to a flame ionization detector, and the other for GDGT analysis by high-performance liquid chromatography coupled to mass spectrometry. All GDGT analyses were done in duplicate. The 1σ analytical uncertainties from 37 replicate analyses of the core catcher sample from core MD95-2042 are 0.007 (0.4 °C) for RI-OH, 0.008 (0.2 °C) for RI-OH′, 0.003 (0.2 °C) for TEX<sub>86</sub>, 0.238 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for U<sup>K′</sup><sub>37</sub>. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX<sub>86</sub><sup>H</sup>-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX<sub>86</sub><sup>H</sup> + 33.0 (Darfeuil et al., 2016). U<sup>K′</sup><sub>37</sub>-SST estimates are from the following global calibration: SST = 29.876 × U<sup>K′</sup><sub>37</sub> − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX<sub>86</sub>-SST and TEX<sub>86</sub>-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for U<sup>K′</sup><sub>37</sub>-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Alkenone data covering the 160–70 and 70–0 ka BP periods are from Davtian et al. (2021) and Darfeuil et al. (2016), respectively. GDGT data covering the 160–45 ka BP period are from Davtian et al. (2021). The age model of core MD95-2042 for the 160–43 and 43–0 ka BP periods was obtained by tuning to Chinese speleothems (Cheng et al., 2016) and by recalibrating existing <sup>14</sup>C ages with the Marine20 calibration curve (Heaton et al., 2020), respectively. MIS, Marine Isotope Stage; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available.</p> <p>Greenland atmospheric temperature record: This dataset consists in a composite Greenland atmospheric temperature record, which was built with the following records: the GISP2 atmospheric temperature record by Kobashi et al. (2017) for the 10–0 ka BP period, the NGRIP atmospheric temperature record by Kindler et al. (2014) for the 120–10 ka BP period, and the NEEM atmospheric temperature record by NEEM community members (2013) for the 129–120 ka BP period. The NEEM temperature anomalies obtained by NEEM community members (2013) were shifted by –31 °C to obtain absolute air temperatures. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p> <p>Antarctic δ<sup>18</sup>O<sub>ice</sub> and atmospheric temperature stacks: This dataset consists in two stacks of three Antarctic records (EDC, EDML, and WD), one for δ<sup>18</sup>O<sub>ice</sub> and the other for atmospheric temperature: both stacks are provided with their stacking uncertainties. To build the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were resampled every 10 years before centering to zero means and normalization to unit standard deviations over the 140–0 ka BP period (68–0 ka BP for WD). To optimize the continuity between the portions with and without the WD ice core, the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were centered to zero means over the 68–67 ka BP period. The resulting Antarctic δ<sup>18</sup>O<sub>ice</sub> records were then averaged and stacking uncertainties were calculated as the pooled standard deviation of the stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records divided by the square root of the number of stacked Antarctic δ<sup>18</sup>O<sub>ice</sub> records. The final Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, expressed in ‰, has the same standard deviation as the δ<sup>18</sup>O<sub>ice</sub> record from EDML over the 140–0 ka BP period, and has a zero mean over the 1–0 ka BP. The Antarctic atmospheric temperature stack was built like the Antarctic δ<sup>18</sup>O<sub>ice</sub> stack, except that the Antarctic δ<sup>18</sup>O<sub>ice</sub> records were corrected for seawater δ<sup>18</sup>O<sub>ice</sub> variations before conversion into atmospheric temperature. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.</p>
A potential biomarker of brain activity in ASD: a pilot fNIRS study in female preschoolers
<p>Looking for visual cortical activity as putative brain biomarker: a pilot fNIR study on autistic<br> female preschoolers.</p> <p>We performed an observational prospective monocentric study in a tertiary care University hospital<br> (IRCCS Stella Maris Foundation, Pisa, IT). We recruited 12 females with idiopathic ASD and 13 sex/age-matched control typically developed peers. Established inclusion criteria were: 1) diagnosis of ASD according to<br> the DSM-5 criteria (APA, 2013); 2) age range: between 3 and 6 years; 3) female gender; 4)<br> negative genetic testing for Fragile X and MECP2; 5) negative history of premature birth or<br> neurologic complications possibly related to delivery and 6) non-verbal IQ &gt; 70. ASD diagnosis has<br> been performed by a multidisciplinary team including a senior child psychiatrist, an experienced<br> clinically trained research child psychologist and a speech-language pathologist during 5-7 days of<br> extensive evaluation. Demographic characteristics and clinical features of experimental cohorts are<br> listed in Tables 1 and 2. Clinical measures such as Autism Diagnostic Observation Schedule<br> (ADOS) (Lord et al., 2012), Autism Questionnaire (AQ) – children’s version, cognitive and<br> adaptive profile were systematically collected for each subject using internationally validated<br> scales/interviews, such as Wechsler Preschool and Primary Scale of Intelligence (Wechsler, 2002)<br> and Vineland Adaptive Behavior Scales-II –VABS- (Sparrow et al., 2005).<br> All participants reported normal or corrected-to-normal vision.</p>
Mass spectrometry- based biomarkers to detect prostate cancer: A multicentric study based on non- invasive urine collection without prior intervention
<p>These are datasets related to mass spectrometry proteomics analysis, in particular Capillary Electrophoresis coupled to Mass Spectrometery (CE-MS). CE-MS was employed to acquire proteomics profiles from 970 patients from two different clinical centers. with the aim to develop a biomarker model to detect Prostate Cancer. </p>
Raw Data for the article: High-Resolution Secretome Analysis of Chemical Hypoxia Treated Cells Identifies Putative Biomarkers of Chondrosarcoma
<p>Chondrosarcoma is the second most common bone tumor, accounting for 20% of all cases. Little is known about the pathology and molecular mechanisms involved in the development and in the metastatic process of chondrosarcoma. As a consequence, there are no approved therapies for this tumor and surgical resection is the only treatment currently available. Moreover, there are no available biomarkers for this type of tumor, and chondrosarcoma classification relies on operator-dependent histopathological assessment. Reliable biomarkers of chondrosarcoma are urgently needed, as well as greater understanding of the molecular mechanisms of its development for translational purposes. Hypoxia is a central feature of chondrosarcoma progression. The hypoxic tumor microenvironment of chondrosarcoma triggers a number of cellular events, culminating in increased invasiveness and migratory capability. Herein, we analyzed the effects of chemically-induced hypoxia on the secretome of SW 1353, a human chondrosarcoma cell line, using high-resolution quantitative proteomics. We found that hypoxia induced unconventional protein secretion and the release of proteins associated to exosomes. Among these proteins, which may be used to monitor chondrosarcoma development, we validated the increased secretion in response to hypoxia of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), a glycolytic enzyme well-known for its different functional roles in a wide range of tumors. In conclusion, by analyzing the changes induced by hypoxia in the secretome of chondrosarcoma cells, we identified molecular mechanisms that can play a role in chondrosarcoma progression and pinpointed proteins, including GAPDH, that may be developed as potential biomarkers for the diagnosis and therapeutic management of chondrosarcoma.</p>
A antibody-based array reveals a serum protein signature as biomarker for adolescent idiopathic scoliosis patients
<p>Evident adolescent idiopathic scoliosis (AIS) incurs high treatment costs, low quality of life, and many complications. Early screening of AIS is essential to avoid progressing to an evident stage. However, there is no valid serum biomarker for AIS for early screening. Antibody-based array is a large-scale study of proteins, which is expected to reveal a serum protein signature as biomarker for AIS. There are two segments of the research, including biomarkers screening and validation. In the biomarkers screening group, a total of 16 volunteers participated in this study, and we carried out differentially expressed proteins screening via protein array assay between No-AIS group and the AIS group.</p>
Protein biomarkers for early predictions of hypoxic ischemic encephalopathy
<p>Perinatal asphyxia is a temporary interruption of oxygen availability that results in neonatal morbidity and mortality. The exact burden of perinatal asphyxia is unknown due to the lack of valid and accepted diagnostic criteria applicable to resource-limited settings. After sepsis, it is the second most important cause of neonatal death. Though there are tests available that can indicate whether an infant is suffering from birth asphyxia or not, they are not the gold standard. There is a dire need for early detection biomarkers for birth asphyxia so that the outcome of birth asphyxia can be avoided. Urinary and serum proteomics has rapidly developed and standard collection and experiment protocols are now available. With the help of the ‘omics’ approach, we can speed up biomarker discovery and widely use it to facilitate diagnostic and therapeutic developments for many diseases. Some of the promising biomarkers found in urine and serum are APOC3, ORM1, SOD1, FABP1, and AZGP1.</p>
SomaScan dataset used to identify protein biomarkers for distinguishing between bacterial and viral infections in febrile children
<p>Protein profiles of children with confirmed bacterial infections (DB), confirmed viral infections (DV) in addition to healthy controls (HC). Protein profiles generated through the SomaScan 1.3k assay (SomaLogic, Colorado, USA).</p> <p>Data has been normalised already, including batch effect correction using COCONUT (https://cran.r-project.org/web/packages/COCONUT/COCONUT.pdf) and log2 transformed. </p> <p>Accompanying the protein abundance values is a separate .csv file containing information about the proteins, including UniProt ID and Entrez gene IDs associated with the proteins.</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.