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184 results for “personalisation”
Repository of Thoracolumbar Osteo-Ligamentous Spine Patient-Personalised FE Meshes
<p><em><strong>Repository of Thoracolumbar Osteo-Ligamentous Spine Patient-Personalised FE Meshes:</strong></em></p> <ul> <li>42 FE input files (.inp extension, Abaqus software, Simulia) representing patient-personalised thoracolumbar spine hexahedral meshes, including point coordinates, mesh connectivity IDs and element sets. Each input file is almost 99MB (totally 3.86GB), and it includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments.</li> <li>An excel file: "<a href="../records/10994164/files/Descriptive_List%20(42_P-S_FE_Models).xlsx?download=1">Descriptive_List (42_P-S_FE_Models).xlsx</a>" reporting measured spinopelvic parameters for 42 patient-personalised FE hexahedral meshes. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort and patient-personalised FE meshes. Model ID in the excel file is correspondent to the model’s name.</li> <li>One png file: "<a href="../records/10994164/files/42_P-S.png?download=1">42_P-S.png</a>" representing the first 10 patient-personalised thoracolumbar spine hexahedral models out of 42 FE models.</li> </ul> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="../records/10994164/files/Descriptive_List%20(42_P-S_FE_Models).xlsx?download=1">Descriptive_List (42_P-S_FE_Models).xlsx</a>" is correspondent to the same model number in the 42 stereolithography (stl) files (.stl extension) representing the thoracolumbar spine triangulated meshes (DOI: <a href="https://doi.org/10.5281/zenodo.8108354">10.5281/zenodo.8108354</a>; <a href="../records/8108354/files/42%20Patient-Specific%20stl%20files.rar?download=1">42 Patient-Specific stl files.rar</a>).</p> <p>2-Models are reconstructed thanks to the Statistical Shape Modelling (SSM) and mesh morphing techniques, i.e., spine sagittal geometrical parameters could be measured by a clinical software like sterEOS or Surgimap, then, different shape modes of morphed-mesh SSM tool could be activated in order to obtain spine deformity. Then, pros and cons analyses between patient geometrical data and synthesized geometrical information could exploit FE patient-personalised thoracolumbar osteo-ligamentous spine models.</p> <p>3- FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: jerome.noailly@upf.edu</p>
Food4Me - personalised nutrition European 9 countries public acceptance survey - Dataset from WP2
<p>This data set consists of a large cross-country survey conducted for the EU funded project Food4Me in 2013. It contains determinants for consumer acceptance and intention to use of personalized nutrition advices based on different levels of specificity and personality of data –self-reported intake, blood analysis and DNA based analysis of optimal nutrition patterns.</p> <p>Data are provided in SPSS and interoperable .CSV format. Surveys and metainformation are added in interoperable .RTF format.</p> <p>The underlying survey is present in 8 languages English, German, Spanish, Norwegian, Dutch, Portuguese, Greek and Polish. Used scales are sourced to the originals in the description file. </p>
PSemQE: Disambiguating Short Queries Through Personalised Semantic Query Expansion
<p>This repository contains research data associated to the publication "PSemQE: Disambiguating Short Queries Through Personalised Semantic Query Expansion". Included are the experimental results, as well as a knowledge graph of Wikipedia articles and categories.</p> <p>For further details, please see the README.md</p>
Selected articles from the scoping review of biomarker discovery studies for the EU project on "Personalised Medicine Trials" (PERMIT)
<p>This dataset provides the data extracted for the scoping review of the literature on biomarker discovery studies for patient stratification using machine learning analysis of omics data, as part of the EU project on “Personalised Medicine Trials” (PERMIT). It covers the references for all articles selected as part of the scoping review, as well as information on the study type and methodology, the outcome measures, the validation type, and representative sentences extracted from each article on the main results and key findings of the corresponding biomarker study.</p>
Selected articles from the scoping review on the study designs for clinical trials applied to personalised medicine
<p>The dataset provides the data extracted for the scoping review of the literature on the study designs for clinical trials applied to personalised medicine, as part of the EU project on “Personalised Medicine Trials” (PERMIT).</p> <p>The dataset reports the references for all articles selected as part of the scoping review, as well as information on the general study characteristics and definition, methodology, statistical considerations, and examples of each study design referred to in each included paper.</p>
Evaluating Translational Methods for Personalised Medicine
<p>This dataset provides the data extracted for the scoping review of the literature on preclinical models for personalised medicine, as part of the EU project on “Personalised Medicine Trials” (PERMIT). It covers the references for all articles selected as part of the scoping review of the two case models chosen, oncology and brain disorders.</p>
VISION Invited lecture - Objective: Personalised Prevention of Pancreatic Cancer
<p>Recording of the invited lecture that took place online on 16 December 2021 - <strong>Núria Malats, MD, MPH, PhD - Objective: Personalised Prevention of Pancreatic Cancer.</strong></p> <p>Identifying the population at high risk of developing non-hereditary pancreatic cancer in order to offer them to enter screening programs and diagnose cancer in early stages, is a must. To this end, we integrate information on the risk factors for this tumor with biomarkers determined at the omics level, including genomics and microbiome. All this information comes from a large study carried out in 28 centres from six European countries. For the integration of this big molecular data with epidemiological and clinical data, we use artificial intelligence tools. In the lecture, I will go through the most recent achievements of these collective efforts.</p>
3D Printing of Personalised Carvedilol Tablets Using Selective Laser Sintering - Underlying Data
<p><strong>Underlying μCT Data for "<em>3D Printing of Personalised Carvedilol Tablets Using Selective Laser Sintering</em>"</strong></p> <p>by <em>Atabak Ghanizadeh Tabriz, Quentin Gonot-Munck, Arnaud Baudoux, Vivek Garg, Richard Farnish, Orestis L. Katsamenis, Ho-Wah Hui, Nathan Boersen, Sandra Roberts, John Jones, and Dennis Douroumis</em></p> <p><em>published in MDPI pharmaceutics<br> In section: Physical Pharmacy and Formulation, Recent Non-oral Dosage Form Development: Focus on 3D-Printed Formulations</em></p> <p><em>The micro- and macro-porosities of representative 3D-printed tablets at 25%, 40%, and 55% laser intensities was measured. SLS-printed components were also characterised by means of X-ray microfocus computed tomography (μCT). Imaging was performed at the University of Southampton’s μ-VIS X-ray Imaging Centre (www.muvis.org) using a customised μCT scanner optimised for 3D X-ray histology (www.xrayhistology.org). The system, which is based on Nikon’s XTH225ST system (Nikon Metrology UK Ltd.)</em></p> <p> </p> <p><strong>Data index</strong></p> <ul> <li>20230206_XRH_3299_OLK_PHAR08603-DOSF_40.zip <ul> <li>Dataset (including ORS Dragonfly analysis file) of object printed at 40% laser power<br> 10 µm voxel size isotropic</li> </ul> </li> <li>20230206_XRH_3299_OLK_PHAR08616-DOSF.zip <ul> <li>Dataset (including ORS Dragonfly analysis file) of object printed at 55% laser power<br> 10 µm voxel size isotropic</li> </ul> </li> <li>20230206_XRH_3299_OLK_PHAR08616-DOSF_25.zip <ul> <li>Dataset (including ORS Dragonfly analysis file) of object printed at 25% laser power<br> 10 µm voxel size isotropic</li> </ul> </li> <li>SLS-3DP_OLK-CorrectRes.xlsx <ul> <li>Analysis results & graphs</li> </ul> </li> </ul> <p><em>X-ray CT analysis conducted using Dragonfly software (v. 2022.1.0.1231; Object Research Systems (ORS) Inc, Montreal, Canada, 2020; software available at http://www.theobjects.com/dragonfly</em></p>
[Supplementary Material] Personalised Route Training for People with Cognitive Impairments through Co-Creation
<p>Supplementary material for the paper ' Paving the Way: Personalised Route Training for People with Cognitive Impairments through Co-Creation' submitted to MUC 2024.</p>
Dataset for Formulation of minitablets with personalised dissolution profile by fluid-bed granulation of drug nanosuspensions
<p>Source data for all graphs in the manuscript titled "Formulation of minitablets with personalised dissolution profile by fluid-bed granulation of drug nanosuspensions" by Mutylo et al., submitted to International Journal of Pharmaceutics. The data sets contains values for particle size distributions, tablet uniformity, drug loading, and dissolution curves.</p>
Dataset for "Personalised Learning Environments Based on Knowledge Graphs and the Zone of Proximal Development"
<p>The dataset accompanying our paper "Personalised Learning Environments Based on Knowledge Graphs and<br> the Zone of Proximal Development" published in proceedings of CSEDU 2022.</p> <p>In the dataset you will find the raw csv results from both the explorative survey and the evaluation survey.<br> <br> The surveys were made in Google Forms and the results have also been exported as pdf files that are included as well.<br> <br> Finally, screenshots of the application, grouped by module can be found inside the screenshots.zip archive.</p>
Development of human pancreatic cancer avatars as a model for dynamic immune landscape profiling and personalised therapy
<div> <div> <div> <p>Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, a disease with dismal overall survival. Advances in treatment are hindered by a lack of preclinical models. Here we show how a personalised organotypic 'avatar' created from resected tissue, allows spatial and temporal reporting on a complete in situ tumour microenvironment, and mirrors clinical responses. Our perfusion culture method extends tumour slice viability, maintaining stable tumour content, metabolism, stromal composition, and immune cell populations for 12 days. Using multiplexed immunofluorescence and spatial transcriptomics, we identify immune neighbourhoods and potential for immunotherapy. We employed avatars to assess the impact of a pre-clinically validated metabolic therapy and show recovery of stromal and immune phenotypes and tumour re-differentiation. To determine clinical relevance, we monitored avatar response to gemcitabine treatment and identified a patient avatar-predicable response from clinical follow-up. Thus, avatars provide valuable information for the syngeneic testing of novel therapeutics and a truly personalised therapeutic assessment platform for patients.</p> </div> </div> </div>
Empowering Healthy Lifestyle Personalised Intervention to Prevent and Control Obesity: The HealthyW8 Project
ClinicalTrials.gov study NCT07011368. IPD Sharing: NO. Countries: 8. Publications: 1.
Adaptive CT Acquisition for Personalised Thoracic Imaging
ClinicalTrials.gov study NCT04070586. IPD Sharing: YES. Countries: 1. Publications: 1.
Piloting a Web-based Personalised Nutrition App (eNutri) with UK University Students
ClinicalTrials.gov study NCT05544461. IPD Sharing: YES. Countries: 1. Publications: 1.
Personalised Diet, Exercise and Emotional Support for Lung Cancer Patients Having Chemotherapy, Radiotherapy or Immunotherapy Treatment
ClinicalTrials.gov study NCT05318807. IPD Sharing: NO. Countries: 1. Publications: 1.
Personalised Risk Assessment in Febrile Illness to Optimise Real-life Management Across the European Union (PERFORM)
ClinicalTrials.gov study NCT03502993. IPD Sharing: NO. Countries: 1. Publications: 2.
Effectiveness of an mHealth Intervention, Based on Rehabilitation and a Personalised Nutrition Plan, in the Recovery and Improvement of Dysphagia in Patients Diagnosed With Stroke
ClinicalTrials.gov study NCT07267468. IPD Sharing: YES. Countries: 1. Publications: 23.
Development of human pancreatic cancer avatars as a model for dynamic immune landscape profiling and personalised therapy
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Brief personalised rhythmic auditory stimulation facilitates cognitive and neural processing in ageing
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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.