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13 results on '"Coolen, Ton"'

1. Bringing rehabilitation home with an e-health platform to treat stroke patients: study protocol of a randomized clinical trial (RGS@home)

Author

Mura, Anna, Maier, Martina, Ballester, Belén Rubio, De la Torre Costa, Javier, López-Luque, Judit, Gelineau, Axelle, Mandigout, Stephane, Ghatan, Per Hamid, Fiorillo, Raffaele, Antenucci, Fabrizio, Coolen, Ton, Chivite, Iñigo, Callen, Antonio, Landais, Hugo, Gómez, Olga Irina, Melero, Cristina, Brandi, Santiago, Domenech, Marc, Daviet, Jean-Christophe, Zucca, Riccardo, and Verschure, Paul F. M. J.

Published
2022
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2. Entropies of tailored random graph ensembles: bipartite graphs, generalised degrees, and node neighbourhoods

Author

Roberts, Ekaterina and Coolen, Ton

Subjects
Condensed Matter - Disordered Systems and Neural Networks
Abstract

We calculate explicit formulae for the Shannon entropies of several families of tailored random graph ensembles for which no such formulae were as yet available, in leading orders in the system size. These include bipartite graph ensembles with imposed (and possibly distinct) degree distributions for the two node sets, graph ensembles constrained by specified node neighbourhood distributions, and graph ensembles constrained by specified generalised degree distributions., Comment: 15 pages, 1 figure

Published
2014

4. Update from the ReIMAGINE Prostate Cancer Screening Study NCT04063566: Inviting Men for Prostate Cancer Screening Using Magnetic Resonance Imaging

Author

Marsden, Teresa, McCartan, Neil, Hadley, Joanna, Tuck, Steve, Brown, Louise, Haire, Anna J., Moss, Charlotte L., Green, Saran, Van Hemelrijck, Mieke, Coolen, Ton, Santaolalla, Aida, Isaac, Elizabeth, Brembilla, Giorgio, Kopcke, Douglas, Giganti, Francesco, Sidhu, Harbir, Punwani, Shonit, Emberton, Mark, and Moore, Caroline M.

Published
2021
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5. ReIMAGINE Prostate Cancer Screening Study: protocol for a single-centre feasibility study inviting men for prostate cancer screening using MRI

Author

Marsden, Teresa, primary, Lomas, Derek J, additional, McCartan, Neil, additional, Hadley, Joanna, additional, Tuck, Steve, additional, Brown, Louise, additional, Haire, Anna, additional, Moss, Charlotte Louise, additional, Green, Saran, additional, Van Hemelrijck, Mieke, additional, Coolen, Ton, additional, Santaolalla, Aida, additional, Isaac, Elizabeth, additional, Brembilla, Giorgio, additional, Kopcke, Douglas, additional, Giganti, Francesco, additional, Sidhu, Harbir, additional, Punwani, Shonit, additional, Emberton, Mark, additional, and Moore, Caroline M, additional

Published
2021
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7. The ReIMAGINE prostate cancer risk study protocol: A prospective cohort study in men with a suspicion of prostate cancer who are referred onto an MRI-based diagnostic pathway with donation of tissue, blood and urine for biomarker analyses.

Author

Marsden, Teresa, McCartan, Neil, Brown, Louise, Rodriguez-Justo, Manuel, Syer, Tom, Brembilla, Giorgio, Van Hemelrijck, Mieke, Coolen, Ton, Attard, Gerhardt, Punwani, Shonit, Moore, Caroline M., Ahmed, Hashim U., and Emberton, Mark

Subjects
PROSTATE cancer patients, DISEASE risk factors, ORGAN donation, URINALYSIS, PROSTATE cancer
Abstract

Introduction: The ReIMAGINE Consortium was conceived to develop risk-stratification models that might incorporate the full range of novel prostate cancer (PCa) diagnostics (both commercial and academic). Methods: ReIMAGINE Risk is an ethics approved (19/LO/1128) multicentre, prospective, observational cohort study which will recruit 1000 treatment-naive men undergoing a multi-parametric MRI (mpMRI) due to an elevated PSA (≤20ng/ml) or abnormal prostate examination who subsequently had a suspicious mpMRI (score≥3, stage ≤T3bN0M0). Primary outcomes include the detection of ≥Gleason 7 PCa at baseline and time to clinical progression, metastasis and death. Baseline blood, urine, and biopsy cores for fresh prostate tissue samples (2 targeted and 1 non-targeted) will be biobanked for future analysis. High-resolution scanning of pathology whole-slide imaging and MRI-DICOM images will be collected. Consortium partners will be granted access to data and biobanks to develop and validate biomarkers using correlation to mpMRI, biopsy-based disease status and long-term clinical outcomes. Results: Recruitment began in September 2019(n = 533). A first site opened in September 2019 (n = 296), a second in November 2019 (n = 210) and a third in December 2020 (n = 27). Acceptance to the study has been 65% and a mean of 36.5ml(SD+/-10.0), 12.9ml(SD+/-3.7) and 2.8ml(SD+/-0.7) urine, plasma and serum donated for research, respectively. There are currently 4 academic and 15 commercial partners spanning imaging (~9 radiomics, artificial intelligence/machine learning), fluidic (~3 blood-based and ~2urine-based) and tissue-based (~1) biomarkers. Conclusion: The consortium will develop, or adjust, risk models for PCa, and provide a platform for evaluating the role of novel diagnostics in the era of pre-biopsy MRI and targeted biopsy. [ABSTRACT FROM AUTHOR]

Published
2022
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9. ESTIMATING FUGL-MEYER UPPER EXTREMITY ASSESSMENT SCORE FROM KINEMATIC MOVEMENT DATA OBTAINED DURING STROKE REHABILITATION IN VIRTUAL REALITY

Author

Ballester, Belén Rubio, Maier, Martina, Bucker, Joris, Antenucci, Fabrizio, and Coolen, Ton

Abstract

Background and Aims:Assessing impairment and forming predictions about recovery are big challenges in neurorehabilitation. In clinical studies, effectiveness is usually assessed with established clinical scales, like the Fugl-Meyer Upper Limb Assessment (FMA). Although the FMA shows great specificity and validity, it is time-consuming, subjective and vulnerable to ceiling effects. Virtual reality (VR)-based setups equipped with motion capture systems could overcome these limitations by automating assessment, requiring that FMA scores can be reliably predicted from kinematic data.Methods:To test this hypothesis, we use movement quality parameters that were extracted from kinematic data recorded during rehabilitation sessions performed with the Rehabilitation Gaming System (RGS), a VR rehabilitation tool, that uses the Microsoft Kinect for motion capture. The protocol considered here asks patients to intercept spheres on a computer screen using their upper limbs. In this preliminary analysis, we performed a multivariate regression using clinical data from 98 stroke patients who completed 191 rehabilitation sessions with RGS.Results:We show that the best multivariate regression model for predicting FMA (intercept: 0.57 prediction range: 18.9 u2013 55.9, standard deviation: 11.96, R2: 0.47) includes the predictors finger flexion (u03b2u00b5: 0.41) and distance covered (u03b2u00b5: 0.17). However, the extent to which patients react fast and appropriate to training related events, or reaction strength, is the strongest predictor of FMA scores in all models tested (u03b2u00b5: 0.19 to 0.52).Conclusions:These results highlight the predictive power of kinematic data collected during unsupervised motor training and provide insight into new factors underlying recovery.

Published
2017

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