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6,423 results for “Biomarkers”
Outdoor mesocosm study evaluating how mass, NaCl tolerance, and pesticide tolerance affect oxidative stress biomarkers (CAT, SOD, GR, GPx, TBARS) in larval wood frogs (Rana sylvatica) exposed to baseline and NaCl-contaminated conditions, 2019
Biomarkers of oxidative stress can aid in wildlife monitoring by allowing conservationists to detect sublethal environmental shifts. However, interpretation of stress responses can be complicated by multiple interacting factors (e.g., individual development, evolved physiological tolerance to stressors) which alter biomarker expression. Here, we investigated how individual ontogenetic traits and population-level tolerance traits influence oxidative stress responses under baseline and contaminated environmental conditions. For our model contaminant, we used NaCl (common freshwater contaminant due to factors such as coastal flooding, irrigation, airborne salt circulation, drought, runoff from road deicing salts). For our model wildlife populations, we used larval wood frogs (Rana sylvatica) from six noninteracting populations known to vary in two population-level tolerance traits: NaCl tolerance (calculated as average time to death from lethal NaCl exposure) and pesticide tolerance (determined by proxy of distance to agriculture - a consistent and highly repeatable relationship). At an outdoor research facility, R. sylvatica tadpoles were exposed to either baseline conditions (0 g/L NaCl added) or NaCl-contaminated conditions (1 g/L NaCl added for 21 days, then reduced to 0.5 g/L NaCl). Exposures were conducted in individual units with 40 replicates per population for each treatment. The experiment was terminated per individual to capture the full term of larval development (Developmental stage: Gosner stage 36), lasting between 33-48 days. For each individual, we measured mass, Snout-Vent-Length, and developmental stage before processing for biomarker expression. Individual homogenates were assayed for oxidative stress biomarkers superoxide dismutase (SOD; responsible for Reactive Oxygen Species capture and peroxide production), glutathione peroxidase (GPx; responsible for high-affinity peroxide reduction), catalase (CAT; responsible for low-affinity peroxide reducti
Arctic fish biomarker profiles from Beaufort Sea coastal lagoons, 2017–2022
Fish sampling occurred in three regions across the Beaufort Sea coast: Elson Lagoon in Utqiaġvik, Stefansson Sound in Prudhoe Bay, and Kaktovik and Jago lagoons in Barter Island (city of Kaktovik). Arctic fishes were collected to determine trophic niche overlap by determining stomach contents, bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA) and their fatty acid (FA) profiles. Fishes were collected in each of the three regions during the open water season in August 2021 and 2022, with supplemental samples collected in 2017 – 2019. The target fish species included three diadromous species: Arctic Cisco (Coregonus autumnalis), Least Cisco (Coregonus sardinella), and Dolly Varden (Salvelinus malma), and three marine fish species: Polar Cod (Boreogadus saida), Saffron Cod (Eleginus gracilis), and Fourhorn Sculpin (Myoxocephalus quadricornis). Up to ten individuals per species per region were sampled, but not all species could be collected in all regions. Stomach contents were reported as the total number of individuals per prey category for the following categories: Amphipoda, Polychaeta, Harpacticoidea, Saduria entomon, Nemertea, Priapulida, Cumacea, Mysidacea, Calanoidea, Larval fish, Chironomida, Insecta, Misc. Stable isotope values of δ13C and δ15N are reported as “del_13c” and “del_15n”, respectively. Individual fatty acids are reported as the percent relative to total fatty acids for 35 fatty acids: C8:0, C10:0, C11:0, C12:0, C13:0, C14:0, C14:1n5, C15:0, C15:1, C16:0, C16:1n7, C17:0, C17:1, C18:0, C18:1n9 trans, C18:1n7, C18:2n6 trans, C18:2n6 cis, C18:3n3, C18:3n6, C20:0, C20:1n9, C20:2n6, C21:0, C20:3n6, C22:0, C20:4n6, C20:3n3, C20:5n3, C22:1n9, C22:2n6, C23:0, C24:0, C22:6n3, C24:1n9. Stable isotope values of δ13C are reported in the following essential amino acids: Valine (Val), Leucine (Leu), iLeu (isoleucine), Methionine (Met), Phenylalanine (Phe), Lysine (Lys).
Primary producer biomarker profiles of bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA) and their fatty acid (FA) collected from the Beaufort Sea coastal lagoons,2021-2024
Within Stefansson Sound in Prudhoe Bay, AK various organic matter sources were collected to determine multiple biomarker baseline profiles (i.e., bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA), fatty acids (FA)). Some organic matter sources were collected from Elson lagoon in Utqiaġvik, AK and Kaktovik and Jago lagoons in Kaktovik, AK to supplement low sample sizes in some organic matter source groups. Kelp, red algae, terrestrial plants, phytoplankton, and ice algae were collected in 2024 with some supplement samples collected in 2021 - 2023. Stable isotope values of δ13C and δ15N are reported as “del_13c” and “del_15n”, respectively. Individual fatty acids are reported as the percent relative to total fatty acids for 23 fatty acids: C11:0, C12:0, C14:0, C15:1, C15:0, C16:0, C16:1n7, C17:0, C17:1, C18:0, C18:1n9 trans, C18:2n6 cis, C18:1n7, C18:3n3, C20:0, C18:3n6, C20:4n6, C21:0, C22:0, C22:1n9, C23:0, C24:0, C22:6n3. Stable isotope values of δ13C are reported in the following essential amino acids: Valine (Val), Leucine (Leu), iLeu (isoleucine), Methionine (Met), Phenylalanine (Phe). Additionally, we used ice algal diatoms collected in the Arctic (landfast ice near Utqiaġvik, Alaska) and cultured in a laboratory setting at the University of Alaska Fairbanks to compare the CSIA-EAA fingerprints of field (composites) ice algal samples and isolate diatoms samples.
Bulk, biomarker and mineralogy data of grain size fractions along a land-sea transect offshore the Atchafalaya river, northern Gulf of Mexico
<p>This dataset comprises the bulk, biomarker and mineralogy data of partitioned surface sediments along a land-sea transect offshore the Atchafalaya River, northern Gulf of Mexico. It includes the total concentrations of the biomarkers and proxies as presented in the accompanied publication, as well as concentrations of single isomers. Supplement to: Yedema et al., (2024); Influence of Organo-mineral Associations on Terrestrial Particulate Organic Matter Dispersal in the northern Gulf of Mexico (doi.)</p> <p> </p> <p><strong>This research has been supported by the Netherlands Earth System Science Centre (grant no. 024.002.001)</strong></p> <p> </p>
Biomarker assessment of spatial and temporal changes in the composition of flocculent material (floc) in the subtropical wetland of the Florida Coastal Everglades (FCE) from May 2007 to December 2009
Flocculent material (floc) is an important energy source in wetlands. In the Florida Everglades, floc is present in both freshwater marshes and coastal environments and plays a key role in food webs and nutrient cycling. However, not much is known about its environmental dynamics, in particular its biological sources and bio-reactivity. We analysed floc samples collected from different environments in the Florida Everglades and applied biomarkers and pigment chemotaxonomy to identify spatial and seasonal differences in organic matter sources. An attempt was made to link floc composition with algal and plant productivity. Spatial differences were observed between freshwater marsh and estuarine floc. Freshwater floc receives organic matter inputs from local periphyton mats, as indicated by microbial biomarkers and chlorophyll-a estimates. At the estuarine sites, the floc is dominated by mangrove as well as diatom inputs from the marine end-member. The hydroperiod (duration and depth of inundation) at the freshwater sites influences floc organic matter preservation, where the floc at the short-hydroperiod site is more oxidised likely due to periodic dry-down conditions. Seasonal differences in floc composition were not consistent and the few that were observed are likely linked to the primary productivity of the dominant biomass (periphyton in the freshwater marshes and mangroves in the estuarine zone). Molecular evidence for hydrological transport of floc material from the freshwater marshes to the coastal fringe was also observed. With the on-going restoration of the Florida Everglades, it is important to gain a better understanding of the biogeochemical dynamics of floc, including its sources, transformations and reactivity.
EEG: Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms
Open the record for dataset details and reuse information.
MiRoR2 - P1 - Shortcomings in the evaluation of biomarkers in ovarian cancer: a systematic review
<p>Data set for the study “Shortcomings in the evaluation of biomarkers in ovarian cancer: a systematic review”, including search strategy, extraction form, extracted data with summary of results, and protocol</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1967 (Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial)
Title: Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial <br>Species: Homo sapiens <br>Number of samples: 1085 <br>Number of named analytes: 8 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=36 <br>
Inter-Chemical Correlation results for the study: HHEARx2017-1740 (Mitochondrial DNA biomarkers of prenatal metal mixture exposure: intergenerational inheritance and infant growth)
Title: Mitochondrial DNA biomarkers of prenatal metal mixture exposure: intergenerational inheritance and infant growth <br>Species: Homo sapiens <br>Number of samples: 1423 <br>Number of named analytes: 20 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=19 <br>
Is there a non-invasive biomarker for the early detection of ovarian torsion? A systematic review and meta-analysis
<p>We have performed a systematic review and meta-analysis and identified multiple biomarkers that warrant further study as part of a broader diagnostic panel for ovarian torsion. These include SCUBE1, s-DD, IL-6, IMA and TNF-a. </p>
Delta Smelt (Hypomesus transpacificus) biomarker and genetic data from supplemental release into the San Francisco Estuary, 2022
Delta Smelt (Hypomesus transpacificus) is an endangered fish that is endemic to the San Francisco Estuary. As a conservation strategy, hatchery-reared Delta Smelt have been released into the San Francisco Estuary to supplement the wild population. State and federal agencies surveyed the abundance of Delta Smelt, collected fish specimens, and recorded associated environmental data from sampling sites. Delta Smelt specimens were preserved and transported to the University of California, Davis where a variety of biomarkers were assessed on individual fish. This project, including the supplementation of hatchery-reared Delta Smelt and data collection, is ongoing.
Minimal dataset for "Systematic review and meta-analysis of late auditory evoked potentials as a candidate biomarker in the assessment of tinnitus"
<p>This text file contains the minimal dataset necessary to reproduce the results and analyses in the paper: Cardon E et al., "Systematic review and meta-analysis of late auditory evoked potentials as a candidate biomarker in the assessment of tinnitus". Plos One;2020.</p>
Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers
<p>Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers</p>
Fluorescent Confocal Laser Scanning Microscopy of White Blood Cells, Cancer Cell Line MCF7, and Mixtures of these Cells: A Model System for Circulating Tumor Cell Biomarker Evaluation V.1
<p>This is a confocal laser scanning microscopy data set of white blood cells (leukocytes), the cancer cell line MCF7, and mixtures of these cells acquired on a Zeiss LSM 780 microscope in the University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core. Cells are fluorescently labeled for DNA with DAPI (Sigma D9542), lipids with Bodipy 495/503 (Thermo Fisher D3922), the filament protein cytokeratin (CK) with pan-cytokertain-alexa555 antibodies (Cell Signaling Technologies 3478S) and the surface membrane antigen CD45 with CD45-alexa647 antibodies (Biolegend 304020). Bodipy was excited with a continuous wave (CW) 488 nm laser, alexa555 was excited with CW 561 nm laser, and alexa647 was excited with a CW 633 nm laser. The acquiring instrument does not have a CW 405 nm source so DAPI was excited by two photon process using a Coherent Cameleon ultrafast pulsed laser tuned to 765 nm. The objective used was a Zeiss Plan-Apochromat 20x, 0.8 NA, air.</p> <p>The data consists of 4 channel 8x8 mosaic z-stacks. The Zeiss software performed stitching of the mosaics. These stitched data images are included and marked with _Stitched at the end. Those interested in performing the stitching themselves can do this with the raw data files (without the _Stitched). The jpeg images are processed from the stitched LSM images. The LSM files contain additional meta data on the experiment including power levels and acquisition settings.</p> <p>The _Stiched .lsm files will load in ImageJ (tested with V.1.49) as 4 channel 3 stack images.</p> <p>This data is a model system for evaluating the DNA/Lipids/CK/CD45 biomarker panel to identify circulating tumor cells (CTCs). The D- population of the model is the WBCs and the D+ population is the MCF7 cancer cell line. The amount of separation the biomarker panel plus analysis algorithm can produce between these populations (D+/D-) is an estimate the sensitivity and specificity of the biomarker panel plus algorithm to CTCs.</p> <p>Experiments generating the data were performed over the course of 15 days. Peripheral blood samples were collected from the Gynecological Tissue and Fluid Bank (COMIRB 07-0935 / COMIRB 05-1081) from consenting patients undergoing surgery at the University of Colorado Hospital. Blood samples were used the same day they were collected. Blood samples were collected from 3 patients with benign conditions, labeled WBBN#, and 3 patients with ovarian cancer, labeled WBCA#. We do not expect there to be any difference in the isolated white blood cells samples prepared from the cancer and benign patients. Samples were stored at room temperature until white blood cells were isolated. Mixed samples were prepared by passaging a MCF7 flask and mixing it with isolated white blood cells before fixation. A schedule showing the time duration between collection, processing and imaging is included as “experimental schedule.gif”.</p> <p>The MCF7 cancer cell line was a kind gift from Dr. Heide Ford. Genomic DNA was isolated from the MCF7 cell line after the experiment and sent for cell line authentication. The gDNA was a match to MCF7. The authentication report and data are included in this submission.</p> <p>CD45 antibodies were exhausted on day 7. New antibody was purchased and received on day 8. The day 7 images only has labels for DAPI and Bodipy. The samples prepared with the old antibodies on days 4 and 7 were relabeled and imaged with the new antibodies on days 14 and 15. This labeling was also done to confirm the pan-CK antibodies remained good since they are dim in the MCF7 cells imaged on days 12 and 13. The pan-CK on days 14 and 15 looks the same as it did on days 5 and 7 confirming the antibodies are good.</p> <p>Four of the filters containing cells were not sufficiently flat to be acquired with a 3 slice z-stack so a 5 slice z-stack was used. These files have been zipped to compress them under the 2 GB limit permitted by zenodo.org</p> <p>Further information on how these samples were prepared, processed, and analyzed can be found in our associated 2016 SPIE Photonics West BIOS conference proceeding titled, “Quantitative image cytometry measurements of lipids, DNA, CD45 and cytokeratin for circulating tumor cell identification in a model system”, http://dx.doi.org/10.1117/12.2222317.</p> <p>This work was supported by funding provided to the University of Colorado Cancer Center by the American Cancer Society and awarded as Institutional Research Grant Number 57-001-53, by funding provided by the Defense Advanced Research Projects Agency under grant number N66001-10-4035, and by funding provided by NIH/NCATS Colorado CTSI Grant Number TL1 TR001081. The University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core is also supported in part by NIH/NCATS Colorado CTSI Grant Number UL1 TR001082. The funders had no role in the study design, data collection, analysis, or decision to publish.</p>
Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis
<p>We sought to identify the best biomarkers for the early diagnosis of LC, using a systematic review of seven databases. We identified 79 articles that focused on the identification and assessment of diagnostic biomarkers and then performed a meta-analysis. This work has been submitted for publication.</p>
Discovery of South African plant-based biomarkers as potential flagships for SARS- CoV-2 receptor
<h1><span>Table S1: </span><span>Distinguished metabolites in the extracts of <em><span>Artemisia annua </span></em><span>and <em>Artemisia afra </em></span>using UPLC-MS/MS set in positive ionization mode.</span></h1> <h1><span>Table S2: Identified and docked compound-based biomarkers from <em><span>Artemisia annua </span></em><span>and <em>Artemisia afra </em></span>(ESI+ scan).</span></h1>
Metabolic alterations in Strongyloidiasis stool samples unveil potential biomarkers of infection_dataset
<p>Strongyloidiasis, a parasitosis caused by <em>Strongyloides stercoralis</em> in humans, is a very prevalent infection in tropical or subtropical areas. Gaps on public health strategies corroborates to the high global incidence of strongyloidiasis especially due to challenges involved on its diagnosis. Based on the lack of a gold-standard diagnostic tool, we aimed to present a metabolomic study for the assessment of stool metabolic alterations. Stool samples were collected from 25 patients segregated into positive for strongyloidiasis (n = 10) and negative control (n = 15) and prepared for direct injection high-resolution mass spectrometry analysis. Using metabolomics workflow, 18 metabolites were annotated increased or decreased in strongyloidiasis condition, from which a group of 5 biomarkers comprising caprylic acid, mannitol, glucose, lysophosphatidylinositol and hydroxy-dodecanoic acid demonstrated accuracy over 89% to be explored as potential markers. The observed metabolic alteration in stool samples indicates involvement of microbiota remodeling, parasite constitution, and host response during <em>S. stercoralis</em> infection.</p>
Integration of expression datasets to identify biomarkers for accurate Gleason scoring in Prostate Cancer -- Supplementary data
<p>This dataset contains expression data from multiple sources used to identify biomarker candidates for prostate cancer aggressiveness. The data includes transcriptional expression levels, patient metadata, and other relevant features utilized in our machine-learning models. The training dataset was extracted from the repository described at Matos-Filipe, et al. (2022) [1].</p> <p>Raw ML metrics from models gaussian Naïve Bayes classifiers using this dataset are available in </p> <p> </p> <p>[1] <span><span><span>Matos-Filipe, P, et al. "</span></span></span>The usage of transcriptomics datasets as sources of Real-World Data for clinical trialling". <span>bioRxiv (</span><span>2022). </span><span><span>doi:</span> https://doi.org/10.1101/2022.11.10.515995</span></p>
Datasets for KGDDP-biomarker
<p>The following Gene Expression Omnibus (GEO) accession numbers were used: GSE169568 and GSE126124. <br>The drug-target interaction (DTI) data was sourced from the DrugBank database, while the protein-protein interaction (PPI) and Gene Ontology data were obtained from the UniProt database. <br>Pathway data was retrieved from the Reactome database. </p> <p>1. **Knowledge Graph Data** (`kg`):<br> - **File**: `kg_del_selfloop.csv`<br> - **Description**: This dataset contains the knowledge graph data that represents relationships between biological entities, including drugs, proteins, and diseases. It is crucial for understanding the interconnections and enhancing the predictive capabilities of the model.</p> <p>2. **Negative Pathway-Protein Relationships** (`pro_path_neg_sp`):<br> - **File**: `human_neg_pathpro.csv`<br> - **Description**: This dataset provides information about negative relationships between pathways and proteins. It helps to identify potential non-relevant or inhibitory connections that may impact disease diagnosis.</p> <p>3. **Negative Disease-Protein Interactions** (`dpi_neg`):<br> - **File**: `neg_dpi_df_t10.csv`<br> - **Description**: This dataset includes negative interactions between diseases and proteins, which assists in refining the model by removing misleading associations that do not contribute positively to predictions.</p> <p>4. **Feature Profiles** (`fp_df`):<br> - **File**: `bdki_db_gdsc_fp.csv`<br> - **Description**: This dataset contains feature profiles of various samples, which are used to train the model. It includes a variety of biomarker data that is essential for accurate disease prediction.</p> <p>5. **Expression Triples** (`exp_triples`):<br> - **File**: `exp_triples.csv`<br> - **Description**: This dataset consists of expression triples representing relationships between genes and their expression levels. It is crucial for capturing the expression profiles of samples and understanding their role in disease pathology.</p> <p>6. **Expression Graph Triples** (`exp_triples_graph`):<br> - **File**: `exp_graph_triples.csv`<br> - **Description**: This dataset contains graph triples that depict relationships within the expression data. It is used to construct a graph representation of the data, which is essential for graph-based analysis techniques.</p> <p>7. **Expression Input Data** (`exp_input`):<br> - **File**: `se_exp_input.csv`<br> - **Description**: This dataset serves as the input for the expression data model, containing necessary information to perform predictions based on gene expression levels.</p> <p>8. **Sample Information** (`dls`):<br> - **File**: `sample_info.csv`<br> - **Description**: This dataset includes sample information, including diagnosis details. It is used to filter out samples without diagnosis data and plays a critical role in training and validating the model.</p>
Dataset of Molecular Dynamics Simulations for the Upregulated Biomarker PSMB8: 3UNF and its G210V Mutant in Experimental Autoimmune Encephalomyelitis
<p>This dataset contains molecular dynamics simulations data generated using GROMACS for the upregulated biomarker 3UNF and its G210V mutant in the context of Experimental Autoimmune Encephalomyelitis (EAE). EAE is a widely studied animal model for multiple sclerosis, and investigating the behavior of biomarkers in this model is crucial for understanding disease progression and potential therapeutic interventions.</p> <p>The dataset includes trajectory files, coordinate files, and relevant parameters used in the simulations. These simulations provide valuable insights into the structural dynamics, conformational changes, and interactions of the PSMB8 biomarker 3UNF and its G210V mutant within the EAE system. The data offers researchers an opportunity to analyze and explore the behavior of these biomarkers at the atomic level, aiding in the identification of potential binding partners, functional sites, and mechanisms associated with disease progression.</p> <p>By sharing this dataset, we aim to contribute to the scientific community by providing a valuable resource for further analysis, validation, and comparison of the molecular behavior of the upregulated biomarker 3UNF and its G210V mutant in Experimental Autoimmune Encephalomyelitis.</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.