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A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy

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Nature Communications 2025年第1期16卷 1-18页

作者： Salovska, Barbora Li, Wenxue Bernhardt, Oliver M. Germain, Pierre-Luc Wang, Qinyue Gandhi, Tejas Reiter, Lukas Liu, Yansheng Department of Pharmacology Yale University School of Medicine New Haven CT United States Cancer Biology Institute Yale University School of Medicine West Haven CT United States Biognosys AG Zurich‐Schlieren Switzerland Institute for Neuroscience Department of Health Sciences and Technology ETH Zurich Zurich Switzerland Master Program of Public Health Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT United States Department of Biomedical Informatics & Data Science Yale University School of Medicine New Haven CT United States

Quantifying protein turnover is fundamental to understanding cellular processes and advancing drug discovery. Multiplex-DIA mass spectrometry (MS), combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC) reliably measures protein turnover and degradation kinetics. Previous multiplex-DIA-MS workflows have employed various strategies including leveraging the highest isotopic labeling channels to enhance the detection of isotopic signal pairs. Here we present a robust workflow that integrates a machine learning algorithm and channel-specific statistical filtering, enabling dynamic adaptation to channel ratio changes across multiplexed experiments and enhancing both coverage and accuracy of protein turnover profiling. We also introduce KdeggeR, a data analysis tool optimized for pSILAC-DIA experiments, which determines and visualizes peptide and protein degradation profiles. Our workflow is broadly applicable, as demonstrated on 2-channel and 3-channel DIA datasets and across two MS platforms. Applying this framework to an aneuploid cancer cell model before and after cisplatin resistance, we uncover strong proteome buffering of key protein complex subunits encoded by the aneuploid genome mediated by protein degradation. We identify resistance-associated turnover signatures, including mitochondrial metabolic adaptation via accelerated degradation of respiratory complexes I and IV. Our approach provides a powerful platform for high-throughput, quantitative analysis of proteome dynamics and stability in health and disease. © The Author(s) 2025.

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Author Correction: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

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Nature genetics 2023年第6期55卷 1080页

作者： Bernardo Rodriguez-Martin Eva G Alvarez Adrian Baez-Ortega Jorge Zamora Fran Supek Jonas Demeulemeester Martin Santamarina Young Seok Ju Javier Temes Daniel Garcia-Souto Harald Detering Yilong Li Jorge Rodriguez-Castro Ana Dueso-Barroso Alicia L Bruzos Stefan C Dentro Miguel G Blanco Gianmarco Contino Daniel Ardeljan Marta Tojo Nicola D Roberts Sonia Zumalave Paul A Edwards Joachim Weischenfeldt Montserrat Puiggròs Zechen Chong Ken Chen Eunjung Alice Lee Jeremiah A Wala Keiran M Raine Adam Butler Sebastian M Waszak Fabio C P Navarro Steven E Schumacher Jean Monlong Francesco Maura Niccolo Bolli Guillaume Bourque Mark Gerstein Peter J Park David C Wedge Rameen Beroukhim David Torrents Jan O Korbel Iñigo Martincorena Rebecca C Fitzgerald Peter Van Loo Haig H Kazazian Kathleen H Burns Peter J Campbell Jose M C Tubio Department of Zoology Genetics and Physical Anthropology Universidade de Santiago de Compostela Santiago de Compostela Spain. Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain. Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Transmissible Cancer Group Department of Veterinary Medicine University of Cambridge Cambridge UK. The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Genome Data Science Institute for Research in Biomedicine (IRB Barcelona) The Barcelona Institute of Science and Technology (BIST) Barcelona Spain. Institucio Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain. The Francis Crick Institute London UK. Department of Human Genetics University of Leuven Leuven Belgium. Genomes and Disease Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea. Cancer Ageing and Somatic Mutation Programme Wellcome Sanger Institute Cambridge UK. Department of Biochemistry Genetics and Immunology University of Vigo Vigo Spain. Galicia Sur Health Research Institute Vigo Spain. Faculty of Science and Technology University of Vic-Central University of Catalonia (UVic-UCC) Vic Spain. Barcelona Supercomputing Center (BSC) Barcelona Spain. Experimental Cancer Genetics Wellcome Sanger Institute Cambridge UK. Oxford Big Data Institute University of Oxford Oxford UK. DNA Repair and Genome Integrity Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Department of Biochemistry and Molecular Biology Universidade de Santiago de Compostela Santiago de Compostela Spain. Medical R

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Using CART for advanced prediction of asthma attacks based on telemonitoring data

Using CART for advanced prediction of asthma attacks based o...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Joseph Finkelstein In Cheol Jeong Department of Biomedical Informatics Columbia University New York USA Chronic Disease Informatics Program Johns Hopkins University Baltimore USA

ISBN: (纸本)9781509014972

Advanced prediction of asthma exacerbations may significantly improve patient quality of life and reduce costs of urgent care delivery. Majority of current algorithms predict who is likely to experience asthma exacerbation rather than when it is about to occur. We used data from asthma home-based telemonitoring for advanced prediction of asthma exacerbation. The goal of this project was to develop an algorithm that predicts asthma exacerbation one day in advance based on previous 7-day window. CART was used for predictive modeling. Resulting algorithm had specificity 0.971, sensitivity of 0.647, and accuracy of 0.809. We concluded that machine learning has great potential for advanced prediction of chronic disease exacerbations based on home telemonitoring data.

关键词： Medical treatment diseases Prediction algorithms Predictive models Pediatrics Monitoring

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Polygenic risk modeling for prediction of epithelial ovarian cancer risk (vol 30, pg 349, 2021)

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EUROPEAN JOURNAL OF HUMAN GENETICS 2022年第5期30卷 630-631页

作者： Dareng, Eileen O. Tyrer, Jonathan P. Barnes, Daniel R. Jones, Michelle R. Yang, Xin Aben, Katja K. H. Adank, Muriel A. Agata, Simona Andrulis, Irene L. Anton-Culver, Hoda Antonenkova, Natalia N. Aravantinos, Gerasimos Arun, Banu K. Augustinsson, Annelie Balmana, Judith Bandera, Elisa V. Barkardottir, Rosa B. Barrowdale, Daniel Beckmann, Matthias W. Beeghly-Fadiel, Alicia Benitez, Javier Bermisheva, Marina Bernardini, Marcus Q. Bjorge, Line Black, Amanda Bogdanova, Natalia V. Bonanni, Bernardo Borg, Ake Brenton, James D. Budzilowska, Agnieszka Butzow, Ralf Buys, Saundra S. Cai, Hui Caligo, Maria A. Campbell, Ian Cannioto, Rikki Cassingham, Hayley Chang-Claude, Jenny Chanock, Stephen J. Chen, Kexin Chiew, Yoke-Eng Chung, Wendy K. Claes, Kathleen B. M. Colonna, Sarah Cook, Linda S. Couch, Fergus J. Daly, Mary B. Dao, Fanny Davies, Eleanor de la Hoya, Miguel de Putter, Robin Dennis, Joe DePersia, Allison Devilee, Peter Diez, Orland Ding, Yuan Chun Doherty, Jennifer A. Domchek, Susan M. Dork, Thilo du Bois, Andreas Durst, Matthias Eccles, Diana M. Eliassen, Heather A. Engel, Christoph Evans, Gareth D. Fasching, Peter A. Flanagan, James M. Fortner, Renee T. Machackova, Eva Friedman, Eitan Ganz, Patricia A. Garber, Judy Gensini, Francesca Giles, Graham G. Glendon, Gord Godwin, Andrew K. Goodman, Marc T. Greene, Mark H. Gronwald, Jacek Hahnen, Eric Haiman, Christopher A. Hakansson, Niclas Hamann, Ute Hansen, Thomas V. O. Harris, Holly R. Hartman, Mikael Heitz, Florian Hildebrandt, Michelle A. T. Hogdall, Estrid Hogdall, Claus K. Hopper, John L. Huang, Ruea-Yea Huff, Chad Hulick, Peter J. Huntsman, David G. Imyanitov, Evgeny N. Isaacs, Claudine Jakubowska, Anna James, Paul A. Janavicius, Ramunas Jensen, Allan Johannsson, Oskar Th. John, Esther M. Jones, Michael E. Kang, Daehee Karlan, Beth Y. Karnezis, Anthony Kelemen, Linda E. Khusnutdinova, Elza Kiemeney, Lambertus A. Kim, Byoung-Gie Kjaer, Susanne K. Komenaka, Ian Kupryjanczyk, Jolanta Kurian, Allison W. Kwong, Ava Lambrechts, Diether Larson, Melissa C. Lazaro, Conxi L University of Cambridge Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care Cambridge UK University of Cambridge Centre for Cancer Genetic Epidemiology Department of Oncology Cambridge UK Center for Bioinformatics and Functional Genomics Cedars-Sinai Medical Center Los Angeles CA USA Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen The Netherlands Netherlands Comprehensive Cancer Organisation Utrecht The Netherlands The Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital Family Cancer Clinic Amsterdam The Netherlands Veneto Institute of Oncology IOV—IRCCS Immunology and Molecular Oncology Unit Padua Italy Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Fred A. Litwin Center for Cancer Genetics Toronto ON Canada University of Toronto Department of Molecular Genetics Toronto ON Canada University of California Irvine Department of Epidemiology Genetic Epidemiology Research Institute Irvine CA USA N.N. Alexandrov Research Institute of Oncology and Medical Radiology Minsk Belarus ‘Agii Anargiri’ Cancer Hospital Athens Greece University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology Houston TX USA Lund University Department of Cancer Epidemiology Clinical Sciences Lund Sweden Vall d’Hebron Institute of Oncology Hereditary cancer Genetics Group Barcelona Spain University Hospital of Vall d’Hebron Department of Medical Oncology Barcelona Spain Rutgers Cancer Institute of New Jersey Cancer Prevention and Control Program New Brunswick NJ USA Landspitali University Hospital Department of Pathology Reykjavik Iceland University of Iceland BMC (Biomedical Centre) Faculty of Medicine Reykjavik Iceland University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg Department of Gynecology and Obstetrics Comprehensive Cancer Center ER-EMN Erlangen Germany Vanderbilt University School of Medicine Division of Epidemiology Department

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

关键词： Clinical genetics Genetic markers Risk factors Biomedicine general Human Genetics Bioinformatics Gene Expression Cytogenetics

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Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction (vol 53, pg 65, 2021)

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NATURE GENETICS 2021年第3期53卷 413-413页

作者： Conti, David V. Darst, Burcu F. Moss, Lilit C. Saunders, Edward J. Sheng, Xin Chou, Alisha Schumacher, Fredrick R. Al Olama, Ali Amin Benlloch, Sara Dadaev, Tokhir Brook, Mark N. Sahimi, Ali Hoffmann, Thomas J. Takahashi, Atushi Matsuda, Koichi Momozawa, Yukihide Fujita, Masashi Muir, Kenneth Lophatananon, Artitaya Wan, Peggy Le Marchand, Loic Wilkens, Lynne R. Stevens, Victoria L. Gapstur, Susan M. Carter, Brian D. Schleutker, Johanna Tammela, Teuvo L. J. Sipeky, Csilla Auvinen, Anssi Giles, Graham G. Southey, Melissa C. MacInnis, Robert J. Cybulski, Cezary Wokolorczyk, Dominika Lubinski, Jan Neal, David E. Donovan, Jenny L. Hamdy, Freddie C. Martin, Richard M. Nordestgaard, Borge G. Nielsen, Sune F. Weischer, Maren Bojesen, Stig E. Roder, Martin Andreas Iversen, Peter Batra, Jyotsna Chambers, Suzanne Moya, Leire Horvath, Lisa Clements, Judith A. Tilley, Wayne Risbridger, Gail P. Gronberg, Henrik Aly, Markus Szulkin, Robert Eklund, Martin Nordstrom, Tobias Pashayan, Nora Dunning, Alison M. Ghoussaini, Maya Travis, Ruth C. Key, Tim J. Riboli, Elio Park, Jong Y. Sellers, Thomas A. Lin, Hui-Yi Albanes, Demetrius Weinstein, Stephanie J. Mucci, Lorelei A. Giovannucci, Edward Lindstrom, Sara Kraft, Peter Hunter, David J. Penney, Kathryn L. Turman, Constance Tangen, Catherine M. Goodman, Phyllis J. Thompson, Ian M., Jr. Hamilton, Robert J. Fleshner, Neil E. Finelli, Antonio Parent, Marie-Elise Stanford, Janet L. Ostrander, Elaine A. Geybels, Milan S. Koutros, Stella Freeman, Laura E. Beane Stampfer, Meir Wolk, Alicja Hakansson, Niclas Andriole, Gerald L. Hoover, Robert N. Machiela, Mitchell J. Sorensen, Karina Dalsgaard Borre, Michael Blot, William J. Zheng, Wei Yeboah, Edward D. Mensah, James E. Lu, Yong-Jie Zhang, Hong-Wei Feng, Ninghan Mao, Xueying Wu, Yudong Zhao, Shan-Chao Sun, Zan Thibodeau, Stephen N. McDonnell, Shannon K. Schaid, Daniel J. West, Catharine M. L. Burnet, Neil Barnett, Gill Maier, Christiane Schnoeller, Thomas Luedeke, Manuel Kibel, Adam S. Drake, Bettina F. Cussenot, Olivier Cancel-Tassin, Gerald Center for Genetic Epidemiology Department of Preventive Medicine Keck School of Medicine University of Southern California/Norris Comprehensive Cancer Center Los Angeles CA USA The Institute of Cancer Research London UK Department of Population and Quantitative Health Sciences Case Western Reserve University Cleveland OH USA Seidman Cancer Center University Hospitals Cleveland OH USA Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Strangeways Research Laboratory Cambridge UK Stroke Research Group Department of Clinical Neurosciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics University of California San Francisco CA USA Institute for Human Genetics University of California San Francisco CA USA Laboratory for Statistical Analysis RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Genomic Medicine National Cerebral and Cardiovascular Center Research Institute Suita Japan Laboratory of Clinical Genome Sequencing Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan Biobank Tokyo Japan Laboratory for Genotyping Development RIKEN Center of Integrative Medical Sciences Yokohama Japan Laboratory for Cancer Genomics RIKEN Center of Integrative Medical Sciences Yokohama Japan Division of Population Health Health Services Research and Primary Care School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK Warwick Medical School University of Warwick Coventry UK Epidemiology Program University of Hawaii Cancer Center Honolulu HI USA Behavioral and Epidemiology Research Group Research Program American Cancer Society Atlanta GA USA Institute of Biomedicine University of Turku Turku Finland Department of Medical Genetics Genomics Laboratory Division Turku University Hospital Turku Finland Department of Urolog

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

关键词： Epidemiology Genome-wide association studies Personalized medicine Prostate cancer

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Using Heart Rate Variability for Automated Identification of Exercise Exertion Levels

Using Heart Rate Variability for Automated Identification of...

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International Conference on Information Technology and Communication in Health, ITCH 2015

作者： Finkelstein, Joseph Jeong, In Cheol Chronic Disease Informatics Program Division of Geriatric Medicine and Gerontology Johns Hopkins University 5200 Eastern Avenue BaltimoreMD21224 United States

ISBN: (纸本)9781614994879

Heart rate monitoring is being used to estimate activity of autonomous nervous system by analysing heart rate variability (HRV). HRV has been recently shown to be effective means to monitor efficacy of exercise in patients with cardiovascular conditions and older adults. Whether HRV can be used to identify exercise exertion levels is unknown. There are multiple approaches to analyse HRV however it is not clear which approach is optimal in assessing cycling exercise. Previous studies demonstrated potential of analysis of short-term sequences of beat-by-beat heart rate data in a time domain for continuous monitoring of levels of physiological stress. The goal of this study was to assess the potential value of short-term HRV analysis during cycling exercise for automated identification of exercise exertion level. HRV indices were compared during rest, height of exercise exertion, and exercise recovery. Comparative analysis of HRV during cycling exercise demonstrated responsiveness of time-domain indices to different phases of an exercise program. Using discriminant analysis, canonical discriminant functions were built which correctly identified 100% of 'highest level of exertion' and 80.0% of 'rest' episodes. HRV demonstrated high potential in monitoring autonomic balance and exercise exertion during cycling exercise program. © 2015 The authors and IOS Press. All rights reserved.

关键词： Discriminant analysis

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Comparative Utility of Time and Frequency HRV Domains for Automated Classification of Exercise Exertion Levels

Comparative Utility of Time and Frequency HRV Domains for Au...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： In Cheol Jeong Joseph Finkelstein Chronic Disease Informatics Program Johns Hopkins University

ISBN: (纸本)9781467368001

Exercise exertion results in activation of sympathetic nervous system. Heart rate variability (HRV) has been used to analyze activity of sympathetic nervous system (ANS). However, approaches to use HRV for exercise exertion analysis were not explored systematically. The main goal of this study was to develop classification algorithms to determine level of exercise exertion in real time and to compare potential of HRV time domain parameters versus HRV frequency domain parameters versus combined time and frequency parameter set. Discriminant analysis was used to identify optimal parameter sets and to develop algorithms for classification of exercise exertion levels. Time-domain HRV parameters demonstrated higher classification accuracy (95.6%) as compared to frequency-domain parameters (82.2%). Combing HRV parameters from time and frequency domains improves classification accuracy (97.8%). Our results suggested that HRV analysis can be used to automatically classify exercise exertion levels. Future studies should focus on more granular approach in identifying different stages of exercise process. Evaluation of classification algorithms should be based on larger sample of diverse representatives of different age, sex and health condition groups.

关键词： Face Heart rate variability Hafnium Correlation Time-frequency analysis

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Introducing contactless assessment of heart rate variability using high speed video camera

Introducing contactless assessment of heart rate variability...

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IEEE Princeton Section Sarnoff Symposium

作者： In Cheol Jeong Joseph Finkelstein Chronic Disease Informatics Program Johns Hopkins University Baltimore MD US Chronic Disease Informatics Program Johns Hopkins University Baltimore MD USA

Heart rate variability (HRV) was shown instrumental in assessing the balance of Autonomous Nervous System (ANS). Recent studies demonstrated potential in measuring heart rate using a web cam however existing contactless approaches have limited resolution to satisfy requirements of HRV analysis. In the present article, we propose an approach to measure HRV using contactless methodology based in high-speed camera which addresses this limitation. In this study we estimated precision of contactless estimation of SDNN which is one of the most frequently used HRV parameters. The main goals of this study were (1) to compare between short-term ECG-based standard deviation of RR intervals (SDNN) and short-term image-based standard deviation of RR intervals (iSDNN) (2) to estimate feasibility of using iSDNN as a proxy of SDNN. Correlation analysis in healthy volunteers demonstrated the potential of using iSDDN to estimate SDNN. The results showed a meaningful correspondence between SDNN and iSDNN with correlation coefficients ranging from 0.6 to 1.0. A contactless methodology to monitor ANS balance can be implemented using high speed camera. Based on the initial evaluation the proposed approach has potential for continuous contactless monitoring of stress reactions in a variety of settings. Further assessment of the proposed approach is warranted in larger sample of diverse group of participants.

关键词： Heart rate variability Monitoring Standards Biomedical monitoring Correlation Cameras Electrocardiography

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Introducing Telerehabilitation in Patients with Multiple Sclerosis with Significant Mobility Disability: Pilot Feasibility Study

Introducing Telerehabilitation in Patients with Multiple Scl...

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IEEE International Conference on Healthcare informatics (ICHI)

作者： In Cheol Jeong Joseph Finkelstein Chronic Disease Informatics Program Johns Hopkins University Baltimore USA

Recent studies demonstrated that exercise interventions in significantly disabled patients with multiple sclerosis (PwMS) resulted in endurance and strength improvement and reduction of fatigue and spasticity. However engagement in long-term exercise training is limited in these patients due to multiple barriers including mobility, access to an exercise facility, and cost. It is unclear if PwMS with significant mobility disability are willing and able to engage in home-based telerehabilitation. The goal of this study was to assess level of acceptance and ability of these patients to carry out exercises using the telerehabilitation system without supervision. Ten PwMS with significant mobility disability were instructed how to use the telerehabilitation system and were then asked to use it independently according to predefined set of tasks. Feasibility was assessed by ability of study participants to successfully follow exercise tasks provided by a touch screen tablet. Telerehabilitation acceptance was measured by an attitudinal survey. Participant characteristics were: age=53±11, PDDS=5.3±2.4, years with MS=16±9. Average time required to train the patients to use the telerehabilitation system at home for flexibility and resistance exercises was 11±3 minutes and for active arm/leg biking: 3±1 minutes. All patients were able to successfully complete flexibility and resistance exercises and navigate the interface for arm biking exercise. Heart rate readings were affected by noise in some subjects. Active leg cycling was completed by 50% of patients, another half was unable to use it due to severe lower limb mobility impairment. Passive leg cycling supported by a splinting device was successfully operated by 100%. Overall, high acceptance and usability of the system in PwMS with severe mobility disability was demonstrated. This approach has a potential in supporting a comprehensive exercise delivery model in PwMS with significant mobility disability which will support i

关键词： Pulse width modulation Multiple sclerosis Portals Computers Fatigue Training

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Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk

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European journal of human genetics : EJHG 2022年第5期30卷 630-631页

作者： Eileen O Dareng Jonathan P Tyrer Daniel R Barnes Michelle R Jones Xin Yang Katja K H Aben Muriel A Adank Simona Agata Irene L Andrulis Hoda Anton-Culver Natalia N Antonenkova Gerasimos Aravantinos Banu K Arun Annelie Augustinsson Judith Balmaña Elisa V Bandera Rosa B Barkardottir Daniel Barrowdale Matthias W Beckmann Alicia Beeghly-Fadiel Javier Benitez Marina Bermisheva Marcus Q Bernardini Line Bjorge Amanda Black Natalia V Bogdanova Bernardo Bonanni Ake Borg James D Brenton Agnieszka Budzilowska Ralf Butzow Saundra S Buys Hui Cai Maria A Caligo Ian Campbell Rikki Cannioto Hayley Cassingham Jenny Chang-Claude Stephen J Chanock Kexin Chen Yoke-Eng Chiew Wendy K Chung Kathleen B M Claes Sarah Colonna Linda S Cook Fergus J Couch Mary B Daly Fanny Dao Eleanor Davies Miguel de la Hoya Robin de Putter Joe Dennis Allison DePersia Peter Devilee Orland Diez Yuan Chun Ding Jennifer A Doherty Susan M Domchek Thilo Dörk Andreas du Bois Matthias Dürst Diana M Eccles Heather A Eliassen Christoph Engel Gareth D Evans Peter A Fasching James M Flanagan Renée T Fortner Eva Machackova Eitan Friedman Patricia A Ganz Judy Garber Francesca Gensini Graham G Giles Gord Glendon Andrew K Godwin Marc T Goodman Mark H Greene Jacek Gronwald Eric Hahnen Christopher A Haiman Niclas Håkansson Ute Hamann Thomas V O Hansen Holly R Harris Mikael Hartman Florian Heitz Michelle A T Hildebrandt Estrid Høgdall Claus K Høgdall John L Hopper Ruea-Yea Huang Chad Huff Peter J Hulick David G Huntsman Evgeny N Imyanitov Claudine Isaacs Anna Jakubowska Paul A James Ramunas Janavicius Allan Jensen Oskar Th Johannsson Esther M John Michael E Jones Daehee Kang Beth Y Karlan Anthony Karnezis Linda E Kelemen Elza Khusnutdinova Lambertus A Kiemeney Byoung-Gie Kim Susanne K Kjaer Ian Komenaka Jolanta Kupryjanczyk Allison W Kurian Ava Kwong Diether Lambrechts Melissa C Larson Conxi Lazaro Nhu D Le Goska Leslie Jenny Lester Fabienne Lesueur Douglas A Levine Lian Li Jingmei Li Jennifer T Loud Karen H Lu Jan Lubiński Phuong L Mai Siranoush Manoukian Jeffrey R Marks Ra University of Cambridge Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care Cambridge UK. University of Cambridge Centre for Cancer Genetic Epidemiology Department of Oncology Cambridge UK. Center for Bioinformatics and Functional Genomics Cedars-Sinai Medical Center Los Angeles CA USA. Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen The Netherlands. Netherlands Comprehensive Cancer Organisation Utrecht The Netherlands. The Netherlands Cancer Institute-Antoni van Leeuwenhoek hospital Family Cancer Clinic Amsterdam The Netherlands. Veneto Institute of Oncology IOV-IRCCS Immunology and Molecular Oncology Unit Padua Italy. Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Fred A. Litwin Center for Cancer Genetics Toronto ON Canada. University of Toronto Department of Molecular Genetics Toronto ON Canada. University of California Irvine Department of Epidemiology Genetic Epidemiology Research Institute Irvine CA USA. N.N. Alexandrov Research Institute of Oncology and Medical Radiology Minsk Belarus. 'Agii Anargiri' Cancer Hospital Athens Greece. University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology Houston TX USA. Lund University Department of Cancer Epidemiology Clinical Sciences Lund Sweden. Vall d'Hebron Institute of Oncology Hereditary cancer Genetics Group Barcelona Spain. University Hospital of Vall d'Hebron Department of Medical Oncology Barcelona Spain. Rutgers Cancer Institute of New Jersey Cancer Prevention and Control Program New Brunswick NJ USA. Landspitali University Hospital Department of Pathology Reykjavik Iceland. University of Iceland BMC (Biomedical Centre) Faculty of Medicine Reykjavik Iceland. University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg Department of Gynecology and Obstetrics Comprehensive Cancer Center ER-EMN Erlangen Germany. Vanderbilt University School of Medicine Division of Epide

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