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3,693 results for “cardiovascular”
Establishing a robust genetic sequencing and gene expression data library in cardiovascularly healthy cats
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Data from: Effects of fermented vegetables on the gut microbiota for prevention of cardiovascular disease
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Assessment of cardiovascular risk factors in patients with Knee Osteoarthritis
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Risk factors for cardiovascular disease (CVD) in adults with type 1 diabetes: findings from prospective real-life T1D exchange registry
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Data from: Diverging trends in erythrocyte size elucidate cardiovascular evolution in stem dinosaurs and crocodilians
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Data from: Coactivator condensation drives cardiovascular cell lineage specification
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Kidney transplantation waiting times and risk of cardiovascular events and mortality: a retrospective observational cohort study in Taiwan
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Higher levels of cardiovascular biomarkers following hypertensive compared to normotensive pregnancy
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Associations of serum uric acid with cardiovascular disease risk factors: a retrospective cohort study in Southeastern China
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Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
<p><b>Objectives: </b>To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.</p> <p><b>Background: </b>Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.</p> <p><b>Methods: </b>Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split.</p> <p><b>Results: </b>The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups.</p> <p><b>Conclusions: </b>An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.</p>
Systematic review data of whole grains versus placebo or refined grains on cardiovascular disease risk factors.
<p class="Body"><span>This dataset comprises data extracted 29 publications which were included in a systematic review and meta-analysis of randomised controlled trials which compared whole grain versus refined grain dietary intake on cardiovascular disease risk factors. There were 80 cardiovascular disease risk factor outcome measures considered in the review. Data were extracted by one investigator and checked for accuracy twice. Study characteristics were extracted, including study design, sample population, and details of the intervention and control conditions. Outcome data were extracted for the intervention and comparator groups were baseline, change, and follow-up variables, variable units, and statistical significance over time and between groups. Adverse events were also recorded. The 29 included studies measured and reported on 40 outcomes. Extracted outcomes can be grouped as haemodynamics (reported by 12 studies), body composition (15 studies), blood lipids (18 studies), glycaemic and/or insulin markers (19 studies), inflammatory markers (21 studies), oxidative stress markers (6 studies), and cardiovascular comorbidity incidence (2 studies). Rigorous analysis, including meta-regression, may be carried out using this data to evaluate the cause and effect relationship of whole grains on cardiovascular risk by considering confounding variables on pooled outcomes. </span></p>
Data from: Metabolomics - Emory Cardiovascular Biobank
<p>Untargeted high-resolution plasma metabolomic profiling among patients with coronary artery disease. Patients recruited from the Emory Cardiovascular Biobank into independent discovery and validation cohorts. </p>
Modifiable cardiovascular risk factors in patients with sporadic cerebral cavernous malformations: obesity matters
Objective <p>To assess the influence of modifiable cardiovascular risk factors on hemorrhage risk of sporadic cerebral cavernous malformations (CCMs).</p> Methods <p>From 1219 consecutive CCM patients (2003-2018), adult subjects with sporadic CCM and complete MRI were included. We evaluated presence of intracerebral hemorrhage (ICH) as mode of presentation, occurrence of ICH during follow-up and risk factors arterial hypertension, diabetes mellitus, hyperlipidemia, nicotine abuse, and obesity (BMI>30kg/m²). Impact of risk factors on ICH at presentation was calculated using univariate and multivariate logistic regression with age- and sex-adjustment. We performed Kaplan Meier and Cox regression to analyze cumulative 5-year-risk for (re-)bleeding. </p> Results <p>We included 682 CCM patients. The univariate logistic regression showed a significant relationship (OR=1.938, 95% CI: (1.120–3.353), p=0.018) between obesity and ICH as mode of presentation. Multivariate adjusted logistic regression confirmed significant correlation with OR=1.902 (95% CI: 1.024–3.532, p=0.042). Cox regression did not identify predictors for occurrence of (re-)hemorrhage (p>0.05; Hazard ratios: arterial hypertension 1.112 (95% CI: 0.622–1.990), diabetes 0.850 (95% CI: 0.208–3.482), hyperlipidemia 0.719 (95% CI: 0.261–1.981), nicotine abuse 1.123 (95% CI: 0.591–2.134), and obesity 0.928 (95% CI: 0.416–2.070)). </p> Conclusions <p>This study provides evidence that obesity may be a risk factor for CCM hemorrhage. It was significantly associated with ICH as mode of presentation. Other risk factors (arterial hypertension, diabetes mellitus, hyperlipidemia and current nicotine abuse) showed no such effect. None of the factors showed to be independent predictors for cumulative 5-year-risk of (re-)bleeding. </p>
Data from: Healthy cardiovascular biomarkers across the lifespan in wild-born chimpanzees (Pan troglodytes)
<p>Chimpanzees (<em>Pan troglodytes</em>) are a crucial model for understanding the evolution of human health and longevity. Cardiovascular disease is a major source of mortality during ageing in humans and therefore a key issue for comparative research. Current data indicate that compared to humans, chimpanzees have proatherogenic blood lipid profiles, an important risk factor for cardiovascular disease in humans. However, most work to date on chimpanzee lipids come from laboratory-living populations where lifestyles diverge from a wild context. Here, we examined cardiovascular profiles in chimpanzees living in African sanctuaries, who semi-free-range in large forested enclosures, consume a naturalistic diet, and generally experience conditions more similar to a wild chimpanzee lifestyle. We measured blood lipids, body weight and body fat in 75 sanctuary chimpanzees and compared them to publicly available data from laboratory-living chimpanzees from the Primate Ageing Database. We found that semi-free-ranging chimpanzees exhibited lower body weight and lower levels of lipids that are risk factors for human cardiovascular disease, and that some of these disparities increased with age. Our findings support the hypothesis that lifestyle can shape health indices in chimpanzees, similar to effects observed across human populations, and contribute to an emerging understanding of human cardiovascular health in evolutionary context.</p>
Ionic Homeostasis, Acid-Base Balance and the Risk of Citrate Accumulation in Patients After Cardiovascular Surgery Treated with Continuous Veno-venous Haemofiltration with Post-Dilution Regional Citrate Anticoagulation.
<p>Data sets analyzed and described in the paper: "Ionic Homeostasis, Acid-Base Balance and the Risk of Citrate Accumulation in Patients After Cardiovascular Surgery Treated with Continuous Veno-venous Haemofiltration with Post-Dilution Regional Citrate Anticoagulation."</p>
Data from: Tek/Tie2 is not required for cardiovascular development in zebrafish
<p>Angiopoietin/TIE signalling plays a major role in blood and lymphatic vessel development. In mouse, <i>Tek/Tie2</i> mutants die prenatally due to a severely underdeveloped cardiovascular system. In contrast, in zebrafish, previous studies have reported that while embryos injected with <i>tek</i> morpholinos (MOs) exhibit severe vascular defects, <i>tek</i> mutants display no obvious vascular malformations. To further investigate the function of zebrafish Tek, we generated a panel of loss-of-function <i>tek</i> mutants, including RNA-less alleles, an allele lacking the MO-binding site, an in-frame deletion allele, and a premature termination codon-containing allele. Our data show that all these mutants survive to adulthood with no obvious cardiovascular defects. MO injections into <i>tek</i> mutants lacking the MO-binding site or the entire <i>tek</i> locus cause similar vascular defects as those observed in MO-injected <i>+/+</i> siblings, indicating off-target effects of the MOs. Surprisingly, comprehensive phylogenetic profiling and synteny analyses reveal that <i>Tek</i> was lost in the largest teleost clade, suggesting a lineage-specific shift in the function of TEK during vertebrate evolution. Altogether, these data show that Tek is dispensable for zebrafish development, and probably dispensable in most teleost species.</p>
Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods
<p>Electrocardiogram (ECG) and Phonocardiogram (PCG) play important roles in early prevention and diagnosis of cardiovascular diseases. As the development of machine learning technique, detection of cardiovascular diseases from ECG and PCG has been attracted much attention. However, current available methods are mostly based on single data resource. It is desirable to develop efficient multi-modal machine learning methods to predict and diagnose cardiovascular diseases. In this study, we propose a novel multi-modal method for predicting cardiovascular diseases based on ECG and PCG features. By building up conventional neural networks, we extract ECG and PCG deep coding features respectively. The genetic algorithm is used to screen the combined features and obtain the best feature subset. Then support vector machine makes classification decision. Experimental results show that compared with using single-modal features ECG and PCG, the performance of this method reaches an AUC value of 0.936 when using multi-modal data resources.</p> <p>This dataset is developed from a real-world dataset which was assembled by PhysioNet/CinC Challenge in 2016. The original dataset can be downloaded from website (<a href="http://www.physionet.org/challenge/2016/">http://www.physionet.org/challenge/2016/</a>).</p>
Data associated with the study, "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model".
<p>Raw data associated with the study entitled "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model". There were 16 subjects with 1. electrocardiogram (ECG), 2. mean arterial pressure (MAP), 3) photoplethysmography (pleth), 4) Bioimpedance Cardiography measured via a Cheetah (Startling/Medtronic) system referred to as cheetah, 5) electrical impedance from a Sentec electrical impedance tomography system, referred to as ST, and electrical impedance from a sciospec impedance analyzer, referred to as SS. Each subject underwent a lower body negative pressure (LBNP) procedure, where the LBNP was increased modeling a small hemorrhage by drawing blood to their lower extremeties. The dataset contains 5 mat (MATLAB data files), with time reported in minutes on the day of the study, i.e.10 am = 600 minutes. The file details are as follows:</p><ul><li><strong>LBNP data</strong>: LBNP_level_times.mat. The data contains 1 structure (LBNPinf) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>lbnp: LBNP value at each noted time</li></ul></li><li><strong>Vital Sign data</strong>: raw_labchart_data.mat. The data contains 1 structure array (Labchart) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>ecgs: ECG data</li><li>MAP: MAP data</li><li>pleth: pleth data recorded from a single channel (V)</li></ul></li><li><strong>Sentec EIT data</strong>: raw_av_ST_impedance_data.mat. The data contains 1 structure array (STout) of length 16 (corresponding to each subject) with the following fields<ul><li>t_thx: time vector in minutes corresponding to thorax data</li><li>Z_thx: average impedance data at each time over the thorax</li><li>t_spl: time vector in minutes corresponding to abdomen data</li><li>Z_spl: average impedance data at each time over the abdomen</li></ul></li><li><strong>Sciospec EIS data</strong>: raw_sciospec_dat.mat. The data contains 1 cell array & 1 structure array (sciodat) of length 16 (corresponding to each subject).<ul><li>Cell array: Locations of the Sciospec measurements: 'Thoracic', 'Abdominal', 'Arm'</li><li>Sciodat fields:<ul><li>sciodat structure array of length 3 corresponding to the 'Thoracic', 'Abdominal', 'Arm' locations, respectively. Each component has the following fields<ul><li>tvec: time vector in minutes</li><li>fs: frequencies that the impedance is recorded over (Hz)</li><li>Zmat: matrix of impedance data size time versus frequency</li><li>erflg: not used</li></ul></li></ul></li></ul></li><li><strong>Bioimpedance cardiography data</strong>: raw_cheetah_bioimpedance.mat. The data contains a cell array of column labels (col_labs, 1x11) and a matrix (cheetah_db) of the BC data.</li></ul><p> </p><p> </p><p> </p>
Clinical and physical parameters of cardiovascular disease patients and occurence of adverse events during exercise
<h2>Supplementary data for the publication available at doi: 10.1038/s41598-024-68223-y.</h2> <p>The article aimed to evaluate the predictive power of physical and clinical parameters to predict the rate of adverse events during exercise-based cardiac rehabilitation.</p> <p>Here the reader can find all anonymized data and complete analysis of results that support the findings reported in the cited publication.</p>
Correlation between estimated pulse wave velocity values from two equations in healthy and under cardiovascular risk populations
<p><strong>Introduction </strong><strong>: </strong>Equations can calculate pulse wave velocity (ePWV) from blood pressure values (BP) and age. The ePWV predicts cardiovascular events beyond carotid-femoral PWV. We aimed to evaluate the correlation between four different equations to calculate ePWV.</p> <p><strong>Methods: </strong>The ePWV was estimated utilizing mean BP (MBP) from office BP (MBP<sub>OBP</sub>) or 24-hour ambulatory BP (MBP<sub>24-hBP</sub>). We separated the whole sample into two groups: individuals with risk factors and healthy individuals. The e-PWV was calculated as follows: </p> <p>We calculated the concordance correlation coefficient (Pc) between e1-PWV<sub>OBP</sub> vs e2-PWV<sub>OBP</sub>, e1-PWV<sub>24-hBP</sub> vs e2-PWV<sub>24-hBP</sub>, and mean values of e1-PWV<sub>OBP</sub>, e2-PWV<sub>OBP</sub>, e1-PWV<sub>24-hBP, </sub>and e2-PWV<sub>24-hBP </sub>. The multilevel regression model determined how much the ePWVs are influenced by age and MBP values.</p> <p><strong>Results:</strong> We analyzed data from 1541 individuals; 1374 ones with risk factors and 167 healthy ones. The values are presented for the entire sample, for risk-factor patients and for healthy individuals, respectively. The correlation between e1-PWV<sub>OBP</sub> with e2-PWV<sub>OBP</sub> and e1-PWV<sub>24-hBP </sub>with e2-PWV<sub>24-hBP</sub> was almost perfect. The Pc for e1-PWV<sub>OBP</sub> vs e2-PWV<sub>OBP</sub> was 0.996 (0.995-0.996), 0.996 (0.995-0.996), and 0.994 (0.992-0.995); furthermore, it was 0.994 (0.993-0.995), 0.994 (0.994-0.995), 0.987 (0.983-0.990) to the e1-PWV<sub>24-hBP </sub>vs e2-PWV<sub>24-hBP</sub>. There were no significant differences between mean values (m/s) for e1-PWV<sub>OBP</sub> vs e2-PWV<sub>OBP</sub> 8.98±1.9 vs 8.97±1.8; p=0.88, 9.14±1.8 vs 9.13±1.8; p=0.88, and 7.57±1.3 vs 7.65±1.3; p=0.5; mean values are also similar for e1-PWV<sub>24-hBP </sub>vs e2-PWV<sub>24-hBP</sub>, 8.36±1.7 vs 8.46±1.6; p=0.09, 8.50±1.7 vs 8.58±1.7; p=0.21 and 7.26±1.3 vs 7.39±1.2; p=0.34. The multiple linear regression showed that age, MBP, and age² predicted more than 99.5% of all four e-PWV.</p> <p><strong>Conclusion: </strong>Our data presents a nearly perfect correlation between the values of two equations to calculate the estimated PWV, whether utilizing office or ambulatory blood pressure.</p>
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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.