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1,659 results for “Patient Data”
Data from: Increase in extraction of I-123 iomazenil in patients with chronic cerebral ischemia
Background: Cerebral extraction of diffusively distributed substances like oxygen has been suggested to change according to the cerebral blood flow (CBF) and status of the microvasculature. The relationships between the cerebral extraction of diffusively distributed lipophilic tracers and the severity of cerebral ischemia has not yet been clarified. In the present study, we attempted to elucidate the association between the extraction fraction of the lipophilic tracer I-123 iomazenil (IMZ) (IMZ-EF) and the oxygen extraction fraction (OEF) derived from O-15 PET in patients with chronic steno-occlusive disease of internal carotid artery (ICA) or middle cerebral artery (MCA). Methods: Seven patients with unilateral chronic severe stenosis or occlusion of the middle cerebral/internal cerebral artery were prospectively recruited for this study. All the patients underwent both O-15 PET and quantitative I-123 IMZ SPECT. Parametric images derived from the PET and SPECT scans were anatomically normalized and evaluated by automated image analysis based on the volume-of-interest template. Results: The asymmetry index (AI) of IMZ-EF was shown to significantly correlated with the AI of OEF (r = 0.562, P < 0.001) in the internal carotid artery perfusion area. Strong and significant correlation between the AI of the influx rate constant K1 of IMZ and the AI of the cerebral metabolic rate of oxygen (r = 0.552, P = 0.001) was clarified. Conclusions: Our results suggested that the transportation efficiency of I-123 IMZ into the brain tissue was an indicator for evaluating severity of cerebral ischemia in patients with chronic steno-occlusive disease of ICA or MCA. Cerebral metabolic state can possibly be estimated by I-123 IMZ SPECT without cyclotron.
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Simulated Patient Data
<p>Generated with https://github.com/greenelab/DAPS/</p>
Data used to generate Fig2A and Patient F column of Table1
<p>The mat file consists of the data from every training and feedback session performed by patient F. This data was used to generate Figure 2A and Patient F column of Table 1.</p>
Data used to generate Fig2C and Patient B column of Table1
<p>The mat file consists of the data from every training and feedback session performed by patient G. This data was used to generate Figure 2C and Patient B column of Table 1.</p>
Data used to generate Fig2B and Patient G column of Table1
<p>The mat file consists of the data from every training and feedback session performed by patient G. This data was used to generate Figure 2B and Patient G column of Table 1.</p>
Data used to generate Fig1D and Patient W column of Table1
<p>The mat file consists of the data from every training and feedback session performed by patient W. This data was used to generate Fig1D and Patient W column of Table 1.</p>
Data used to generate Fig1A and Patient F column of Table1
<p>The mat file consists of the data from every training and feedback session performed by patient F. This data was used to generate Fig1A and Patient F column of Table 1.</p>
Data used to generate Fig1C and Patient B column of Table1
<p>The mat file consists of the data from every training and feedback session performed by patient B. This data was used to generate Fig1C and Patient B column of Table 1.</p>
Data used to generate Fig1B and Patient G column of Table1
<p>The mat file consists of the data from every training and feedback session performed by patient G. This data was used to generate Fig1B and Patient G column of Table 1.</p>
Real-World Gait Speed Estimation: Laboratory and Real-Life Patient Data from TOLIFE Wearables
<div><strong>TOLIFE Wearable Dataset Collection</strong></div> <p>This data collection was carried out in the context of the TOLIFE Horizon Europe project, which aims to develop and validate artificial intelligence (AI) solutions for processing patient data collected through non-intrusive wearable sensors. The ultimate goal is to enable personalized treatment, monitor health outcomes, and improve the quality of life of patients with Chronic Obstructive Pulmonary Disease (COPD).</p> <p>The dataset is composed of four distinct subsets, each acquired under different conditions and with specific goals:</p> <div><strong>1. </strong>Gait Speed Dataset</div> <div><strong>2.</strong> Activity Recognition Dataset</div> <div><strong>3. </strong>Validation Dataset</div> <div><strong>4. </strong>Daily Life Dataset</div> <div> </div> <p><strong>1. Gait Speed Dataset:</strong></p> <div><strong>Objective:</strong> To collect reference gait speed data under controlled conditions using wearable sensors, with a focus on validating gait speed estimation algorithms.</div> <div> </div> <div><strong>Materials:</strong></div> <div>Smartphone: Samsung Galaxy A14</div> <div>Smartwatch: Samsung Galaxy Watch 5</div> <div>Smart shoes (custom prototype)</div> <p><strong>Acquisition Protocol:</strong></p> <div>Participants: 25 healthy individuals (10 M, 15 F), age: 28.9 ± 4.8 years</div> <div>Test: Six Minute Walking Test (6MWT) along a 10-meter path</div> <div>Paces: Slow, Medium, Fast (self-selected)</div> <div> </div> <div>Device Placement:</div> <div>- Watch: left </div> <div>- Phone: left front pocket (screen facing thigh, Y-axis upward)</div> <div>- Reference System: Xsens Awinda (17 wireless IMUs) with MVN Analyze software</div> <div>- Reference gait speed = horizontal velocity of Center of Mass (CoM)</div> <p><strong>Folder Content:</strong></p> <div>Wearable folder: individual .csv files for each sensor (phone, watch, shoes)</div> <div>Reference folder: CoM speed from Xsens + walked distances in smwd.csv</div> <div> </div> <p><strong>2. Activity Recognition Dataset:</strong></p> <div><strong>Objective:</strong></div> <div>To build a dataset for training AI models to recognize different daily activities (e.g., resting, walking, stair climbing) using wearable sensor data.</div> <p><strong>Materials:</strong></p> <div>Same TOLIFE wearable platform as above (phone, watch, smart shoes).</div> <p><strong>Acquisition Protocol:</strong></p> <div>Participants: 15 healthy individuals (7 F, 8 M), age: 27.3 ± 3 years</div> <div>Activities:</div> <div>- Resting: lying, sitting, standing (max 5 sec walking allowed)</div> <div>- Walking: 3 sessions (2 x 180 m + 1 x 450 m with directional changes)</div> <div>- Stair climbing: continuous ascent and descent for ~2 minutes</div> <div>Smartwatch placement: user's preferred wrist</div> <p><strong>Folder Content:</strong></p> <div>Data folder: wearable sensor data for each recording session</div> <div>Timestamps folder: .csv files with annotated start/stop times and activity labels</div> <div> </div> <p><strong>3. Validation Dataset:</strong></p> <div><strong>Objective:</strong></div> <div>To test the robustness of trained activity and gait estimation models in less controlled, real-world environments.</div> <p><strong>Materials: </strong>Same TOLIFE wearable platform.</p> <p><strong>Acquisition protocol:<br></strong>Participants: 10 healthy adults (5 M, 5 F), age: 43.8 ± 12.1 years<br>Protocol: free walking outdoors, with mixed activities:<br>- Resting, level walking, stair climbing<br>- 4 walking paths of known length (100–280)<br>- Device placement: user’s preferred side (watch), front pants pocket (phone)</p> <p><strong>Folder Content:<br></strong>Data folder: continuous sensor data recordings<br>Timestamps folder: annotated transitions between activities</p> <p> </p> <div><strong>4. Daily Life Dataset:</strong></div> <div> </div> <div><strong>Objective: </strong>To capture real-world data from COPD patients in daily life conditions for long-term monitoring of mobility and gait speed.</div> <p><strong>Materials: </strong>TOLIFE wearable platform (smartphone, smartwatch, smart shoes)</p> <p><strong>Acquisition Protocol:<br></strong>Participants: 38 COPD patients (23 M, 15 F), age: 63.3 ± 5.8 years<br>Duration: continuous data collection over two weeks<br>Initial Reference Visit (RV):<br>- 2 clinical 6MWTs performed by a pulmonologist, average distance used as a reference value<br>- Instructions: participants used the devices freely (indoors/outdoors) in comfortable conditions</p> <p><strong>Folder Content:<br></strong>Data folder: two weeks of wearable sensor recordings per patient<br>Clinical Six_Minute_Walking_Distance.csv: distances from RV</p> <p> </p> <p>Citation:</p> <p>When using any portion of this dataset collection, please cite:</p> <p>Zanoletti, M.; Bufano, P.; Bossi, F.; Di Rienzo, F.; Marinai, C.; Rho, G.; Vallati, C.; Carbonaro, N.; Greco, A.; Laurino, M.; et al. Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings. Sensors 2024, 24, 3205. https://doi.org/10.3390/s24103205</p>
Underlying data for The Impact of ABCB1 (rs1045642 and rs117068084) Gene Polymorphisms on Response to Imatinib Treatment in A Sample of Iraqi Chronic Myeloid Leukemia-Chronic Phase Patients
<p>Underlying data for The Impact of ABCB1 (rs1045642 and rs117068084) Gene Polymorphisms on Response to Imatinib Treatment in A Sample of Iraqi Chronic Myeloid Leukemia-Chronic Phase Patients </p>
Data Source file for manuscript: Within-host genetic diversity of extended-spectrum beta-lactamase-producing Enterobacterales in long-term colonized patients
<p>ABSTRACT</p><p>Infections caused by extended-spectrum beta-lactamase (ESBL)-producing Enterobacterales (ESBL-PE) are associated with excess morbidity and mortality. Despite recognition of this immediate impact on human health, essential aspects of their molecular epidemiology remain under-investigated. This includes knowledge on the potential of a particular strain to persist in a host, mutational events during colonization, and the genetic diversity in individual patients over time. To investigate long-term genetic diversity of colonizing and infecting ESBL-producing <i>Klebsiella pneumoniae </i>species complex and ESBL-<i>Escherichia coli</i> in individual patients over time, we conducted performed a ten-year longitudinal retrospective study and extracted clinical and microbiological data from electronic health records. In this investigation, 76 ESBL-<i>K. pneumoniae</i> species complex and 284 ESBL-<i>E. coli</i> isolates were recovered from 19 and 61 patients. Strain persistence was detected in all patients colonized with ESBL-<i>K. pneumoniae </i>species complex, and 83.6% of patients colonized with ES BL-<i>E. coli</i>. Antimicrobial resistance genes, plasmid replicons, and whole ESBL-plasmids were shared between isolates regardless of chromosomal relatedness. Our study suggests that patients colonized with ESBL-producers may act as durable reservoirs for ongoing transmission of ESBLs, and that they are at a prolonged risk of recurrent infection with colonizing strains.</p><p>DATA SOURCE FILE</p><p>In this Data Source file, you will find access to the raw data, metadata and results obtained during the study: "Within-host genetic diversity of extended-spectrum beta-lactamase-producing Enterobacterales in long-term colonized patients". Data is organized following the structure of Figures/Tables of the manuscript and Supplementary Material. Additional figures not included in the main manuscript nor in the Supplementary Material are also available.</p><p>All sequencing and sample data from this study can be accessed at the NCBI database under the BioProject number PRJNA910977: <a href="http://dataview.ncbi.nlm.nih.gov/object/PRJNA910977">http://dataview.ncbi.nlm.nih.gov/object/PRJNA910977. </a>No new software was developed during this study. Standard bioinformatics software was used and the commands used can be accessed at the Supplementary Information file.</p>
Data of patients with acute respiratory failure in the MIMIC IV database
<p>MIMIC IV数据库中急性呼吸衰竭患者的数据。</p>
Clinical data of patients and values of antibodies in the IgM, IgA and IgG classess against Mycoplasma pneumoniae
<p><span>Data on <em>Mycoplasma pneumoniae</em> infections during last years are very limited. In this manuscript, we presented the assessment of the seroprevalence of antibodies against <em>Mycoplasma pneumoniae</em> in the numerous group of patients, as well as clinical aspects of the differential diagnostics of respiratory tract infections.</span></p>
Updated Data corresponding to a study on 'Rapid Patient-Specific FEM Meshes from 3D iPhone Scans'
Open the record for dataset details and reuse information.
Gait data of idiopathic toe walking patients with pre & post sessions in surgical or conservative treatment group & healthy participants including ankle kinematics & kinetics
<p>This dataset contains gait data of 29 Idiopathic Toe Walking (ITW) patients and 21 Healthy participants from the Kinesiology Laboratory database. For the ITW population, the gait data of two clinical gait analyses were included, one before and one after surgical or conservative treatment. The dataset includes the trajectory of lower body markers, ankle kinematics and moments as well as calculated gait parameters. The markers' trajectory, ankle kinematics and moments are stored in c3d files, a biomechanics standard file format. The ankle kinematics and moments are also stored in the following csv or xlsx files: <em>ControlGroup_DataPerCycle.csv</em>, <em>ITW_DataPerCycle.xlsx</em> & <em>ITW_Matched_DataPerCycle.xlsx</em>; one row corresponding to the data of one gait cycle. The gait parameters were computed from the kinematics & kinetics data and were stored in the following xlsx files: <em>ITW_WithWithoutSurg.xlsx</em> & <em>ITW_WithWithoutSurg_matched.xlsx</em>; one row corresponding to the data of one clinical gait analysis.</p>
Data and scripts supporting "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors"
<p>This submission contains the processed sequencing data and analysis scripts, as well as the raw and processed LC-MS metabolomics files accompanying our manuscript "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors" (Cell Reports Medicine, 2024)</p>
Data from: Self-myofascial release decreased pain intensity and improved conditioned pain modulation response in patients with myofascial pain syndrome: a single-blind RCT study
<p>Objective: The present study investigated the effects of self-myofascial release (SMFR) on pain perception, myofascial trigger point (MTrP) activity, and conditioned pain modulation (CPM) response in patients with myofascial pain syndrome (MPS).</p> <p>Methods: A total of 32 MPS patients with a visual analog scale (VAS) score greater than 30/100 mm were enrolled and randomly assigned to 3 groups of 4 weeks of intervention – A) SMFR with a 60 s period (n=11), B) SMFR with a 30 s period (n=10) and C) conventional myofascial release (MFR) performed by the physiotherapist (n=11). Pressure pain threshold (PPT), VAS, resting-state electromyography amplitude (rAEMG) and conditioned pain modulation response (CPM-R) were assessed before, 1 week, 2 weeks and 4 weeks during the intervention.</p> <p>Results: Compared with baseline, VAS and rAEMG levels in were significantly decreased, while the PPT and CPM-R values were significantly increased in all groups after 4 weeks' intervention without significant difference between groups.</p> <p>Conclusion: Both SMFR and MFR techniques with a single pressing duration of 60s and 30s had significant therapeutic effects on chronic myofascial pain, with the decrease of VAS and rAEMG, the increase of PPT and CPM-R values, indicating that SMFR could activate and restore the function of descending pain modulation.</p>
Data set from: Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial.
<p>Data set from the paper "Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial" doi: <a href="https://dx.doi.org/10.3390%2Fmedicina57070713">10.3390/medicina57070713</a></p>
Supplementary data for "Analysis of Non-21α-hydroxylase-deficiency Primary Adrenal Insufficiency in Childhood: Data from 113 Chinese Patients"
<p>Supplementary data for "Analysis of Non-21α-hydroxylase-deficiency Primary Adrenal Insufficiency in Childhood: Data from 113 Chinese Patients"</p> <p><strong>Supplementary Table 1 </strong>Causes of Primary Adrenal Insufficiency in Children</p> <p><strong>Supplementary Table 2</strong> Mutations Detected in Subjects with non-21-OHD CAH Inherited Causes of Childhood-Onset Primary Adrenal Insufficiency</p> <p><strong>Supplementary Table 3 </strong>Clinical findings in Subjects with Non-21-OHD Inherited Causes of Childhood-Onset Primary Adrenal Insufficiency</p> <p><strong>Supplementary Table 4 </strong>Population Frequencies of Common Variants in <em>STAR</em> or <em>MC2R</em></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.