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12,749 results for “Blood”
The organophosphate pesticide methamidophos opens the blood-testis barrier and covalently binds to ZO-2 in mice
<p>We studied biological effects and post-translational modifications of proteins after treating mice with the pesticide methamidophos.</p> <p>This data set provides evidence for the modification of ZO-2, indicating that the blood-testis barrier in mouse was crossed.</p>
Dynamics of individual T cell repertoires: from cord blood to centenarians
<p>The dataset contains processed T-cell receptor repertoire sequencing data from 79 individuals of different sex and age, originally published in [1] and [2]. Note that [1] describes only a subset of samples, while [2] describes the full cohort.</p> <p>The libraries were prepared using a 5'RACE protocol and sequenced on HiSEQ. The libraries incorporate unique molecular identifier (UMI) tags that were mainly used for counting cDNA molecules. Preprocessing was performed using the MIGEC software [3] as follows: all UMI tags represented by a single sequencing read were discarded, the remaining UMI tags were used to assemble cDNA consensus sequences. Note that this procedure eliminates most of cross-sample contamination (batch effect) as described in [2]. VDJ partitioning and CDR3 extraction was performed using MiTCR software [4], sequencing error correction was performed using ETE option in MiTCR. All datasets are converted into VDJtools [5] format, see http://vdjtools-doc.readthedocs.io/en/latest/input.html#vdjtools-format.</p> <p><strong>Sample description:</strong></p> <ul> <li>The A* in sample identifier is the batch ID.</li> <li>Age and sex data is provided in the metadata.txt file.</li> <li>Samples having age "0" are umbilical cord blood samples.</li> </ul> <p><strong>Contributors:</strong></p> <ul> <li>The T-cell repertoire aging study was a project ran in the Genomics of Adaptive Immunity Lab (Prof. Dmitry Chudakov)</li> <li>The samples were acquired, prepared and sequenced by Dr. Olga Britanova</li> <li>The data was analyzed and uploaded by Dr. Mikhail Shugay</li> </ul> <p><strong>Citations:</strong></p> <ul> <li>[1] OV Britanova, EV Putintseva, M Shugay, EM Merzlyak, MA Turchaninova, et al. Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. The Journal of Immunology 2014; 192 (6), 2689-2698</li> <li>[2] OV Britanova, M Shugay, EM Merzlyak, DB Staroverov, EV Putintseva, et al. Dynamics of individual T cell repertoires: from cord blood to centenarians. The Journal of Immunology 2016; 196 (12), 5005-5013</li> <li>[3] M Shugay, OV Britanova, EM Merzlyak, MA Turchaninova, IZ Mamedov, et al. Towards error-free profiling of immune repertoires. Nature methods 2014; 11 (6), 653-655</li> <li>[4] DA Bolotin, M Shugay, IZ Mamedov, EV Putintseva, MA Turchaninova, et al. MiTCR: software for T-cell receptor sequencing data analysis. Nature methods 2013; 10 (9), 813-814</li> <li>[5] M Shugay, DV Bagaev, MA Turchaninova, DA Bolotin, OV Britanova, et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS computational biology 2015; 11 (11), e1004503</li> </ul>
Transcriptional profiling of peripheral blood mononuclear cells identifies inflammatory phenotypes in ataxia telangiectasia
<p>This is an AnnData object in h5ad (hdf5) format containing de-identified bulk RNA-seq gene expression matrices from PBMCs. These data are related to the study entitled "Transcriptional profiling of peripheral blood mononuclear cells identifies inflammatory phenotypes in ataxia telangiectasia". </p><p>This AnnData object contains a table of sample-specific metadata (`obs`), gene-specific metadata (`var`), and multiple gene expression matrices stored as `layers`. These layers include raw counts, DEseq2 normalized counts, vst normalized counts, and rlog normalized counts. Some additional layers include regressed versions of the previously mentioned counts matrices, where sequencing batch (`cohort` in the obs table) has been regressed out using the `combat` tool. The layer `rlog_combat_regressed_batch` is recommended for downstream processing, and has been loaded into the `X` slot of the anndata object for convenience. </p><p>The md5sum of this h5ad file is listed here: 04d7c6c549fb37cf730a5dca7897f86f</p><p>Opening and working with AnnData objects in h5ad format requires the use of the `anndata` python library (https://github.com/scverse/anndata). </p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
<p><span>Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used </span><span>8–23</span><span> selected CpG sites to build age estimation models with machine learning methods </span><span>with about only $3–7 per sample</span><span>, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (<em>Felis catus</em>, 139 samples), Tsushima leopard cats (<em>Prionailurus bengalensis euptilurus</em>, 84 samples), and five<em> Panthera </em>species (96 samples). </span><span>The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and <em>Panthera</em> spp., respectively.</span><span> Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five<em> Panthera</em> spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.</span></p>
Tumor and Blood B Cell Abundance Outperforms Established ICB Response Prediction Signatures in Head and Neck Cancer
<div> <div> <div> <div> <p>This dataset contains processed flow cytometry data and clinical information for deidentified patients from Cohort 11, as well as deconvoluted cell abundances and clinical data for deidentified patients from Cohort 10, associated with the study titled <em>"Tumor and Blood B Cell Abundance Outperforms Established Immune Checkpoint Blockade Response Prediction Signatures in Head and Neck Cancer"</em> published in <strong>Annals of Oncology (2024)</strong>. <a href="https://doi.org/10.1016/j.annonc.2024.11.008" target="_new" rel="noopener">DOI: https://doi.org/10.1016/j.annonc.2024.11.008</a>.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Dataset for multiple blood feeding bouts in mosquitoes
<p>Dataset to accompany R code and findings in the Holmes et al., 2024 publication entitled, "Multiple blood feeding bouts in mosquitoes allow for prolonged survival and are predicted to increase viral transmission during dry periods."</p>
Classification of blood cells dynamics with convolutional and recurrent neural networks: a sickle cell disease case study
<p>The fraction of red blood cells (RBC) adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease (SCD). Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples(out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. These videos are of different durations (from 6 to more than 100 frames).</p> <p>This dataset contains four adult patients with SCD. They were enrolled in the study Drepaforme (approved by the institutional review board CPP Ouest 6 under the reference n°2018A00679-46) and were sampled weekly for several months. The movies were processed using in-house routines in Matlab (Matlab, R2016a) and RBC were detected individually and tracked over time. The database provided in this repository are already pre-processed sequences of tracked and centered RBC over time, each time step image being normalized to 31x31 pixels. Within the 32 experiments, the total number of sequences (or samples) is nearly 150 000. All sequences were semi-automatically labelled into 3 classes, depending on the dynamic of the cell: tank-treading, flipping and unreliable (140 000 are unreliable). The percentage of tank-treading cells with respect to all reliable cells (tank-treading+flipping) in every experiment is the final goal of this study.</p> <p>This dataset is very interesting to the community as it is a large database for cell dynamics classification: the class depends on the movement of the cell.</p> <p>An automatic processing of the database using a 2-stage deep learning model is available here https://github.com/icannos/redbloodcells_disease_classification</p> <p>For opening the data in python:</p> <p> from scipy.io import loadmat<br> x=loadmat('BG20191003shear10s01_Export.mat')</p> <p> * x['Norm_Tab'] is of size nb_samples x max_len_sequences x 31 x 31, where max_len_sequences is the length of the longest sequence of the series, typically ~150 to 180. The other sequences are padded with 31x31 zero matrices at the end in order to fill this maximal length.</p> <p> * x['Labels_Num'] is the corresponding label of each sequence, of size nb_samples. Label can be:<br> - 0 : "tank-treading" (or healthy)<br> - 1 : "flipping" (or tumbling, i.e. related to a SCD)<br> - 2 : "unreliable"</p>
Fig. 5 in Weight-length relationship, condition factor and blood parameters of farmed Cichla temensis Humboldt, 1821 (Cichlidae) in central Amazon
Fig. 5. Blood cells in tucunaré C. temensis stained by MGGW. A - Erythrocytes, B - Polychromatic erythroblasts, C - Neutrophil, D - Monocyte, E - Lymphocyte, and F - Thrombocytes. Scale bars = 5 μm.
Fig. 3 in Weight-length relationship, condition factor and blood parameters of farmed Cichla temensis Humboldt, 1821 (Cichlidae) in central Amazon
Fig. 3. Relationshipbetweenhematocritandredbloodcells (r = 0.950; p<0.001) in C. temensis (n = 40) farmed in central Amazon.
Fig. 4 in Weight-length relationship, condition factor and blood parameters of farmed Cichla temensis Humboldt, 1821 (Cichlidae) in central Amazon
Fig. 4. Relationship between hematocrit and hemoglobin concentration (r = 0.860; p<0.001) in C. temensis (n = 40) farmed in central Amazon.
BIOPEP-UWM: Calculations for top 23 selected peptides from ATHpin ranking of Cynara cardunculus swine blood hydrolysate FNF
<p>Calculations by BIOPEP (https://biochemia.uwm.edu.pl/biopep-uwm/) for top 23 selected peptides from ATHpin ranking of Cynara cardunculus swine blood hydrolysate FNF.</p>
Terminal density reversal and the role of Ca2+ in red blood cells clearance of healthy individuals
<p>Density reversal of senescent red blood cells (RBCs) has been known for more than ten years, yet the identity of the candidate protein(s) is still elusive. While performing Percoll density gradient separation of RBCs from healthy individuals and their subsequent characterization, we identified a fraction of cells in the low-density fraction (~0.025% compared to total RBCs population) which shows reversal in their densities along with the characteristics of cellular senescence such as loss of membrane Band 3 protein and the phosphatidylserine exposure to the outer membrane leaflet. Our subsequent analysis showed that these cells are overloaded with Ca<sup>2+</sup>. We further measured intracellular [Na<sup>+</sup>] in individual RBCs by flow cytometry utilizing the dye CoroNa Green-AM. Our findings showed that the cells with senescent characteristics lost their transmembrane Na<sup>+</sup> gradient despite maintaining the membrane integrity. Consequently, these findings lead us to designate these cells as “senescent-like” cells. Our data further demonstrated altered activities of nonselective cation channels and pumps in these cells. In addition to a facilitated Na+ extrusion by Na<sup>+</sup>, K+-ATPase, our findings indicated altered ion transport via Piezo1 in these cells. Pharmacological modulation of Piezo1 with Yoda1/GsMTx4 showed that Piezo1 and, possibly, other nonselective cation channels by promiscuously transporting Na<sup>+</sup> and Ca<sup>2+</sup> play an important role in producing these low density “senescent like” cells.</p>
The protein organization of a red blood cell
<p>Elution profiles, feature matrix, train and test ppis, and metadata for the paper "The protein organization of a red blood cell". Sae-Lee et al., </p>
A subset dataset of COVID-19 Blood Atlas for CellDrift input
<p>A subset dataset of COVID-19 Blood Atlas for CellDrift input. The original data can be found in this paper: <a href="https://doi.org/10.1016/j.cell.2022.01.012">https://doi.org/10.1016/j.cell.2022.01.012</a>. We did subsetting on the data and extracted 116,124 cells covering 8 disease conditions, 6 PBMC cell types and a series of time points (days since onset) ranging from day 0 to day 25. </p>
Data from: Ecology and evolution of blood oxygen-carrying capacity in birds
Blood oxygen-carrying capacity is one of important determinants of oxygen amounts supplied to the tissues per unit time and plays a key role in oxidative metabolism. In wild vertebrates, blood oxygen-carrying capacity is most commonly measured with the total blood haemoglobin concentration (Hb) and haematocrit (Hct), which is the volume percentage of red blood cells in blood. Here, I used published estimates of avian Hb and Hct (nearly one thousand estimates from 300 species) to examine macroevolutionary patterns in blood oxygen-carrying capacity of blood in birds. Phylogenetically-informed comparative analysis indicated that blood oxygen-carrying capacity was primarily determined by species distribution (latitude and elevation) and morphological constraints (body mass). I found little support for the effect of life history components on blood oxygen-carrying capacity, except for a positive association of Hct with clutch size. Hb was also positively associated with diving behaviour, but I found no effect of migratoriness on either Hb or Hct. Fluctuating selection was identified as the major force shaping the evolution of blood oxygen-carrying capacity. The results offer novel insights into the evolution of Hb and Hct in birds, as well as they provide a general, phylogenetically-robust support for some long-standing hypotheses in avian ecophysiology.
blood_clotting_data
<p>Contains data belonging to the manuscript "Domain Evolution of Vertebrate Blood Coagulation Cascade Proteins"</p>
Gastruloids as in vitro models of embryonic blood development with spatial and temporal resolution
<p>Rawdata (images and flow cytometry) for the manuscript "Gastruloids as in vitro models of embryonic blood development with spatial and temporal resolution". <br> </p>
Carbon dioxide and blood-feeding shift visual cue tracking during navigation in Aedes aegypti mosquitoes
<p>Hematophagous mosquitoes need a blood meal to complete their reproductive cycle. To accomplish this, female mosquitoes seek vertebrate hosts, land on them, and bite. As their eggs mature, they shift attention away from hosts and towards finding sites to lay eggs. We asked whether females were more tuned to visual cues when a host-related signal, carbon dioxide, was present, and further examined the effect of a blood meal, which shifts behavior to ovipositing. Using a custom, tethered-flight arena that records wing stroke changes while displaying visual cues, we found the presence of CO2 enhances visual attention towards discrete stimuli and improves contrast sensitivity for host-seeking <em>Aedes aegypti</em> mosquitoes. Conversely, intake of a blood meal reverses vertical bar tracking, a stimulus that non-fed females readily follow. This switch in behavior suggests that physiological status modulates visual attention in mosquitoes, a phenomenon that has been described before in olfaction but not in visually-driven behaviors.</p>
Incidence and predictor of diabetic foot ulcer and its association with change in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia, 2021
<p>Abstract</p> <p> </p> <p><strong>Background</strong></p> <p>Diabetes mellitus is one of the global public health problems and fasting blood sugar is an important indicator of diabetes management. Uncontrolled diabetes can lead to diabetic foot ulcers, which is a common and disabling complication. The association between fasting blood glucose level and the incidence of diabetic foot ulcers is rarely considered, and knowing its predictors is good for clinical decision-making. Therefore, the aim of this study was to determine the incidence and predictors of diabetic foot ulcers and its association with changes in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia.</p> <p><strong>Methods</strong></p> <p>A multicenter retrospective follow-up study was conducted at a referral hospital in Northwest Ethiopia. A total of 539 newly diagnosed DM patients who had follow-up from 2010 to 2020 were selected using a computer-generated simple random sampling technique. Data was entered using Epi-Data 4.6 and analyzed in R software version 4.1. A Cox proportional hazard with a linear mixed effect model was jointly modeled and 95% Cl was used to select significant variables. AIC and BIC were used for model comparison.</p> <p><strong>Result</strong></p> <p>A total of 539 diabetes patients were followed for a total of 28727.53 person-month observations. Overall, 65 (12.1%) patients developed diabetic foot ulcers with incidence rate of 2.26/1000-person month observation with a 95% CI of [1.77, 2.88]. Being rural (AHR= 2.30, 95%CI: [1.23, 4.29]), being a DM patient with Diabetic Neuropathy (AHR= 2.61, 95%CI: [1.12, 6.06]), and having peripheral arterial disease(PAD) (AHR= 2.96, 95%CI: [1.37, 6.40]) were significant predictors of DFU. The time-dependent lagged value of fasting blood sugar change was significantly associated to the incident of DFU (α = 1.85, AHR=6.35, 95%CI [2.40, 16.79]).</p> <p><strong>Conclusion and recommendation</strong></p> <p>In this study, the incidence of DFU was higher than in previous studies and was influenced by multiple factors like rural residence, having neuropathy, and PAD were significant predictors of the incidence of DFU. In addition, longitudinal changes in fasting blood sugar were associated with an increased risk of DFU. Health professionals and DM patients should give greater attention to the identified risk factors for DFU were recommended.</p>
Introduction to the Future Blood Testing Network Plus - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the first talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ </p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/YuBsU3NDdB0</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.