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10,068 results for “Heart”
A Study of Limited Heart Monitoring During Non-anthracycline Trastuzumab-based Therapy in Breast Cancer Patients
ClinicalTrials.gov study NCT03983382. IPD Sharing: YES. Countries: 1. Publications: 1.
A Study to Investigate the Efficacy, Safety, and Tolerability of DFV890 and MAS825 for Inflammatory Marker Reduction in Adult Participants With Coronary Heart Disease and Clonal Hematopoiesis of Indet
ClinicalTrials.gov study NCT06097663. IPD Sharing: YES. Countries: 3. Publications: 1.
Implementing HEARTS in Guatemala
ClinicalTrials.gov study NCT06080451. IPD Sharing: YES. Countries: 1. Publications: 2.
SYNTAX III REVOLUTION Trial: A Randomized Study Investigating the Use of CT Scan and Angiography of the Heart to Help the Doctors Decide Which Method is the Best to Improve Blood Supply to the Heart i
ClinicalTrials.gov study NCT02813473. IPD Sharing: YES. Countries: 5. Publications: 2.
Perceived Social Support, Heart Rate Variability, and Hopelessness in Patients With Ischemic Heart Disease
ClinicalTrials.gov study NCT05003791. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Edoxaban Compared to Standard Care After Heart Valve Replacement Using a Catheter in Patients With Atrial Fibrillation (ENVISAGE-TAVI AF)
ClinicalTrials.gov study NCT02943785. IPD Sharing: YES. Countries: 14. Publications: 5.
Data deposition for Complex electrophysiological remodeling in postinfarction ischemic heart failure
Open the record for dataset details and reuse information.
Size of brain, heart, liver, alimentary tract, and kidneys along altitudinal gradients in Asiatic toad
Open the record for dataset details and reuse information.
HEartS Professional Survey: Charting the effects of COVID-19 lockdown 1.0 on working patterns, income, and wellbeing among performing arts professionals in the United Kingdom (April–June 2020)
Open the record for dataset details and reuse information.
Online supplement for "T cell costimulation blockade blunts age-related heart failure"
<p><strong>ONLINE SUPPLEMENT FOR:</strong></p> <p><strong>Research Letter:</strong></p> <p><strong>T cell costimulation blockade blunts age-related heart failure</strong></p>
The Ubiquitin Ligase WWP1 Contributes to Shifts in Matrix Proteolytic Profiles and a Myocardial Aging Phenotype with Diastolic Heart Failure
<p><strong><em>Aims</em></strong>. Ubiquitylation is a key event that regulates protein turnover, and induction of the ubiquitin ligase E3 WWP1 has been associated with age. Left ventricular hypertrophy (LVH) commonly occurs as a function of age and can cause heart failure with a preserved ejection fraction (EF; HFpEF). We hypothesized that overexpression (O/E) of WWP1 in the heart would cause LVH as well as functional and structural changes consistent with the aging HFpEF phenotype.</p> <p><strong><em>Methods and Results.</em></strong> Global WWP1 O/E was achieved in mice (n=11) and echocardiography (40 MHz) performed to measure LV mass, EF, Doppler velocities (early-E, late/atrial-A), myocardial relaxation (E’), and isovolumetric relaxation time (IVRT) at 4, 6, and 8 weeks. Age matched wild type animals (n=15) were included as referent controls. LV EF was identical (60+1% vs 60+1%, p>0.90) with no difference in LV mass (67+3 vs 75+5, p>0.25) at 4 weeks. However, at 8 weeks of age, LV mass increased by over two-fold, E/A fell (impaired passive filling), and E/E’ was lower and IVRT prolonged (impaired LV relaxation) - all p<0.05. Collagen percent area increased by over two-fold and fibrillar collagen expression (rtPCR) by over 1.5 fold (p<0.05) with WWP1 O/E. WWP1 with an anti-WWP1 antibody could be identified in isolated cardiac fibroblasts with WWP1 increased by over two-fold in O/E fibroblasts (p<0.05).</p> <p><strong><em>Conclusion.</em></strong> Inducing WWP1 expression caused LVH, preserved systolic function, but impaired diastolic dysfunction, consistent with the HFpEF phenotype. Targeting the WWP1 pathway may be a novel therapeutic target for this intractable form of HF associated with aging.</p>
Does the preferred walk-run transition speed on steep inclines minimize energetic cost, heart rate or neither?
Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.
Data for "Does the Preferred Walk-Run Transition Speed on Steep Inclines Minimize Energetic Cost, Heart Rate or Neither?"
<p>Abstract</p> <p>Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.</p> <p>Methods</p> <p>Subjects walked and ran on a classic Quinton 18-60 motorized treadmill with a rigid steel deck (Quinton Instrument Company, Bothell, WA).</p> <p><strong>Determination of PTS: </strong>The average of the walk-to-run transition speed (WRTS) and run-to-walk transition speed (RWTS) defined the PTS as per Hreljac et. al. (2007). We first determined the WRTS in the walk-first group and then their RWTS and <em>vice versa</em> for the run-first group. Based on pilot experiments, we selected starting speeds such that there was no doubt which gait would be preferred at the initial speed. Once the speed of the treadmill was correctly set, subjects mounted the treadmill and chose their gait <em>ad libitum</em>. After we determined the preferred gait at the particular speed, the subject straddled the treadmill belt while we changed the speed by 0.1 m/s (increased during WRTS trials, decreased during RWTS trials). The process repeated until a gait transition occurred and was sustained for 30 seconds.</p> <p><strong>Determination of EOTS and HROTS: </strong>For the energetics and heart rate trials, we set the initial speed based on pilot experiments that indicated it would be near the EOTS. Subjects in the walk-first group walked at the incline-specific initial speed for 5 min, rested for ∼5 min and then ran at that speed for 5 min. Subjects in the run-first group did the opposite. During the rest periods, we re-weighed the subject and they drank just enough water to compensate for the weight loss due mostly to sweating. Thus, each subject maintained a nearly constant weight throughout all the trials.</p> <p>To measure metabolic rate during walking and running, we used an open-circuit, expired gas analysis system (TrueOne 2400; ParvoMedics, Sandy, UT). Subjects wore a mouthpiece with a one-way breathing valve and a nose clip allowing us to collect their expired air. The ParvoMedics software calculated the STPD rates of oxygen consumption (V□O<sub>2</sub>) and carbon dioxide production (V□CO<sub>2</sub>) and we averaged the last 2 minutes of each 5-minute trial. We then calculated metabolic power using the equation of Péronnet and Massicotte (1991) equation, as clarified by Kipp et al. (2018). We only included trials with respiratory exchange ratios (RER) <1.0 to ensure that metabolic energy was predominantly being provided from oxidative pathways. We used an R7 Polar iWL (Polar Electro Oy, Kempele, Finland) to measure heart rate in beats per minute (bpm) and averaged the values for the last 2 min of each trial.</p> <p>Immediately after both gait trials were completed for the initial speed, we calculated and compared the metabolic power required for walking and running. If walking was the more economical gait, we increased the treadmill speed by 0.1 m/s, and the process repeated. If running was the more economical gait, we decreased the treadmill speed by 0.1 m/s, and the process repeated. Each subject performed three speeds, both walking and running at each incline. However, some subjects needed to complete walking and running trials at a fourth speed so that we could obtain energetics data for one speed faster and one speed slower than their EOTS.</p> <p>For the three speeds at which the differences between metabolic rates between walking and running were least, we calculated linear regression equations for both metabolic power and heart rate as functions of speed for both walking and running for each subject and incline. The speeds at which the two equations intersected defined the EOTS and HROTS for each subject.</p> <p>Overall, we analyzed ten subjects at four different inclines, i.e. 40 determinations of EOTS and HROTS. Of those 80 linear regression analyses, the walking vs. running regressions intersected at a speed < 3 m/sec for all but two subjects (one subject for EOTS at 15° and a different subject for HROTS at 10°). Essentially, those individuals’ regression lines were nearly parallel. We chose to exclude those two conditions from further statistical analysis and aggregate data compilation.</p> <p>Usage Notes</p> <p>There are two missing values, as noted in the methods: HROTS for subject 5 at 10 degrees and EOTS for subject 4 at 15 degrees.</p>
Semi-Siamese U-Net for separation of lung and heart bioimpedance images: a simulation study of thorax EIT
<p><span>Electrical impedance tomography (EIT) is widely used for bedside monitoring of lung ventilation status. Its goal is to reflect the internal conductivity changes and estimate the electrical properties of the tissues in the thorax. However, poor spatial resolution affects EIT image reconstruction to the extent that the heart and lung-related impedance images are barely <a name="_Hlk56106607">distinguishable</a>. Several studies have attempted to tackle this problem, and approaches based on decomposition of EIT images using linear transformations have been developed, and recently, U-Net has become a prominent architecture for semantic segmentation. In this paper, we propose a novel semi-Siamese U-Net specifically tailored for EIT application. It is <a name="_Hlk48748756">based on the state-of-the-art U-Net</a>, whose structure is modified and extended, forming shared encoder with parallel decoders and has multi-task weighted losses added to adapt to the individual separation tasks. The trained semi-Siamese U-Net model was evaluated with a test dataset, and the results were compared with those of the classical U-Net in terms of Dice similarity coefficient and mean absolute error. </span></p> <p><span>Results showed that compared with the classical U-Net, semi-Siamese U-Net exhibited performance improvements of 11.37% and 3.2% in Dice similarity coefficient, and 3.16% and 5.54% in mean absolute error, in terms of heart and lung-impedance image separation, respectively.</span></p>
Occupational exposure to silica and risk of heart disease: a systematic review with meta-analysis
<p><b><span>Objective</span></b> To search for evidence of the relationship between occupational silica exposure and heart disease.</p> <p><b><span>Design</span></b> A systematic review and meta-analysis.</p> <p><b><span>Background</span></b> Growing evidences suggest a connection between occupational silica exposure and heart disease; however, the link between them is less clear.</p> <p><b><span>Data sources </span></b>PubMed, ScienceDirect, Springer and EMBASE were searched for articles published between 1 January 1995 and 20 June 2019. Articles that investigated the effects of occupational silica exposure on heart disease risk were considered.</p> <p><b><span>Study selection </span></b><span>We included c</span><span>ohort stud</span><span>ies</span>, including prospective, retrospective and retro-prospective studies.</p> <p><b><span>Data extraction and synthesis</span></b> We extracted data by using a piloted data collection form and conducted random-effects meta-analysis and exposure-response analyses. The meta-relative risk (meta-RR), a measure of the average ratio of heart disease rates for those with and without silica exposure, was used as an inverse variance-weighted average of relative risks from the individual studies. The Newcastle-Ottawa Quality assessment Scale about cohort studies was used for study quality assessment.</p> <p><b><span>Outcome measure</span></b> We calculated heart disease risks of pulmonary heart disease, ischaemic heart disease and other heart diseases.</p> <p><span><b><span>Results</span></b> Twenty cohort articles were included. Results suggest a significant increase of overall heart disease risk (meta-RR = 1.08, 95% CI = 1.03, 1.13). Stronger evidences of association with pulmonary heart disease were found through both categories of heart disease risk estimate (meta-RR = 1.24, 95% CI = 1.08, 1.43) and exposure-response analyses (meta-RR = 1.39, 95% CI = 1.19, 1.62). Moreover, our subgroup analyses revealed that the statistical heterogeneity among studies could be attributed mainly to the diversities of reference group, occupation and study quality score.</span></p> <p><b><span>Conclusions</span></b> Silica-exposed workers have increased risk of overall heart disease, especially pulmonary heart disease. While further research is needed to better clarify the relationship between occupational silica exposure and ischaemic heart disease.</p>
Hypochondriac Heart (2021) by Katie Kirk
This is a glazed ceramic piece at the Brand Library in Glendale, CA. It's part of the Generations exhibit. https://online.fliphtml5.com/tzjnt/gofg/#p=30 Captured with Trnio Plus using photo mode. Edited in MeshMixer. Source: Objaverse 1.0 / Sketchfab
Dataset for article: Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88
<p>Dataset for article:</p> <p>Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88</p>
Breathing frequency bias in fractal analysis of heart rate variability (datasets)
<p>data form the article:</p> <p>Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Bi- ological Psychology, 82(1), pp. 82-88 </p>
VELOCITY VECTOR FIELDS, MEASURED AT THE OUTFLOW FROM TWO HINGE MODELS OF A BILEAFLET MECHANICAL HEART VALVE, USING 2-COMPONENT PARTICLE IMAGE VELOCIMETRY TECHNIQUE
<p>VELOCITY VECTOR FIELDS, MEASURED AT THE OUTFLOW FROM TWO HINGE MODELS OF A BILEAFLET MECHANICAL HEART VALVE, USING 2-COMPONENT PARTICLE IMAGE VELOCIMETRY TECHNIQUE</p>
Dataset for a Mouse and Rat heart trancriptomic and co-expression network analysis
<ul> <li>mouse_heart_data and rat_heart_expression contain GEO expression matrix for mouse and rat experiments.</li> <li>gse_gsm_mouse.txt and gse_gsm_rat.txt contain experiment IDs and series IDs from GEO.</li> <li>heart_quantNormData_mouse.tsv and heart_quantNormData_rat.tsv contain normalised expression matrices.</li> <li>heart_quantNormData_mouse_1sd.tsv and heart_quantNormData_rat_1sd.tsv contain the normalised expression matrices restricted to genes with a standard deviation higher than 1.</li> <li>fileForSCHypeThreshold0.5_heart.txt and fileForSCHypeThreshold0.75_heart.txt are the input for SCHype. schype_output_0.5th_heart.nodes.txt, schype_output_0.5th_heart.edges.txt, schype_output_0.75th_heart.nodes.txt and schype_output_0.75th_heart.edges.txt are the outputs.</li> <li>geneLists.zip contains gene lists used for ontology analysis (ENSEMBL gene id)</li> </ul>
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.