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The CBIT + TMs trial: study protocol for a two-phase randomized controlled trial testing neuromodulation to augment behavior therapy for youth with chronic tics

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Trials 2023年第1期24卷 439-439页

作者： Conelea, Christine Greene, Deanna J. Alexander, Jennifer Houlihan, Kerry Hodapp, sarah Wellen, Brianna Francis, sunday Mueller, Bryon Hendrickson, Tim Tseng, Angela Chen, Mo Fiecas, Mark Lim, Kelvin Opitz, Alexander Jacob, suma Department of Psychiatry and Behavioral Sciences Masonic Institute for the Developing Brain University of Minnesota 2025 E. River Parkway Minneapolis 55414 MN United States Department of Cognitive Science University of California San Diego San Diego United States Department of Psychiatry and Behavioral Sciences University of Minnesota Minneapolis United States Masonic Institute for the Developing Brain University of Minnesota Informatics Institute Minneapolis United States Non-Invasive Neuromodulation Lab Brain Conditions MnDRIVE Initiative University of Minnesota Minneapolis United States Neuroscience Program Research Department Gillette Children’s Specialty Healthcare Saint Paul United States School of Public Health Division of Biostatistics University of Minnesota Minneapolis United States Department of Biomedical Engineering University of Minnesota Minneapolis United States

Background: Comprehensive Behavioral Intervention for Tics (CBIT) is a first-line treatment for tic disorders that aims to improve controllability over tics that an individual finds distressing or impairing. However, it is only effective for approximately half of patients. supplementary motor area (sMA)-directed neurocircuitry plays a strong role in motor inhibition, and activity in this region is thought to contribute to tic expression. Targeted modulation of sMA using transcranial magnetic stimulation (TMs) may increase CBIT efficacy by improving patients' ability to implement tic controllability behaviors. Methods: The CBIT + TMs trial is a two-phase, milestone-driven early-stage randomized controlled trial. The trial will test whether augmenting CBIT with inhibitory, non-invasive stimulation of sMA with TMs modifies activity in sMA-mediated circuits and enhances tic controllability in youth ages 12–21 years with chronic tics. Phase 1 will directly compare two rTMs augmentation strategies (1 Hz rTMs vs. cTBs) vs. sham in N = 60 participants. Quantifiable, a priori “Go/No Go Criteria” guide the decision to proceed to phase 2 and the selection of the optimal TMs regimen. Phase 2 will compare the optimal regimen vs. sham and test the link between neural target engagement and clinical outcomes in a new sample of N = 60 participants. Discussion: This clinical trial is one of few to date testing TMs augmentation of therapy in a pediatric sample. The results will provide insight into whether TMs is a potentially viable strategy for enhancing CBIT efficacy and reveal potential neural and behavioral mechanisms of change. Trial registration: *** NCT04578912 . Registered on October 8, 2020. © 2023, The Author(s).

关键词： Behavior therapy Neuromodulation Pediatric Tic Tourette Transcranial magnetic stimulation

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Variations among Electronic Health Record and Physiologic streaming Vital signs for Use in Predictive Algorithms in Pediatric severe sepsis

ACI Open

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ACI Open 2022年第2期06卷 e76-e84页

作者： Adam C. Dziorny Robert B. Lindell Julie C. Fitzgerald Christopher P. Bonafide Department of Pediatrics University of Rochester School of Medicine Rochester New York United States Department of Biomedical Engineering University of Rochester Rochester New York United States Department of Anesthesiology and Critical Care Medicine Children's Hospital of Philadelphia Philadelphia Pennsylvania United States Pediatric Sepsis Program Children's Hospital of Philadelphia Philadelphia Pennsylvania United States Department of Pediatrics Children's Hospital of Philadelphia Philadelphia Pennsylvania United States Department of Biomedical and Health Informatics Children's Hospital of Philadelphia Philadelphia Pennsylvania United States

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Differential regulation of JAK1 expression by ETs1 associated with predisposition to primary biliary cholangitis

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Journal of Genetics and Genomics 2023年第10期50卷 807-812页

作者： Peng Jiang Chan Wang Mingming Zhang Ye Tian Weifeng Zhao Junyi Xin Yexi Huang Zhibin Zhao Wenjuan sun Jie Long Ruqi Tang Fang Qiu Xingjuan shi Yi Zhao Li Zhu Na Dai Lei Liu Xudong Wu Jinshan Nie Bo Jiang Youlin shao Yueqiu Gao Jianjiang Yu Zhigang Hu Zhidong Zang Yuhua Gong Yaping Dai Lan Wang Ningling Ding Ping Xu sufang Chen Lu Wang Jing Xu Luyao Zhang Junyan Hong Ruonan Qian Hu Li Xuan Jiang Congwei Chen Wenyan Tian Jian Wu Yuzhang Jiang Chongxu Han Kui Zhang Hong Qiu Li Li Hong Fan Liming Chen Jianqiong Zhang Zhongsheng sun Xiao Han Zhenhua Dai Erguang Li M.Eric Gershwin Zhexiong Lian Xiong Ma Michael F.seldin Weichang Chen Meilin Wang Xiangdong Liu Key Laboratory of Developmental Genes and Human Diseases School of Life Science and TechnologySoutheast UniversityNanjingJiangsu 210096China Institute of Translational Medicine Yangzhou University Medical CollegeYangzhouJiangsu 225009China Department of Radiology&Oncology The Second Affiliated Hospital of Soochow UniversitySuzhouJiangsu 215004China The First Affiliated Hospital of Soochow University SuzhouJiangsu 215006China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjingJiangsu 210029China Author links open overlay panelYexi Huang Guangdong Provincial People's Hospital Guangdong Academy of Medical SciencesGuangzhouGuangdong 510080China Department of Gastroenterology and Hepatology Shanghai Institute of Digestive DiseasesShanghai Jiao Tong University School of Medicine Affiliated Renji HospitalShanghai 200001China Department of Laboratory Medicine The Fourth Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsu 210031China Department of Gastrointestinal Endoscopy Eastern Hepatobiliary Surgery HospitalShanghai 201800China The Fifth People's Hospital Soochow UniversitySuzhouJiangsu 215007China Department of Gastroenterology Jiangsu University Affiliated Kunshan HospitalKunshanJiangsu 215300China Department of Gastroenterology Yixing People's HospitalYixinJiangsu 214200China Department of Gastroenterology Yancheng First People's HospitalYanchengJiangsu 224005China Department of Gastroenterology Taicang First People's HospitalSoochow UniversityTaicangJiangsu 215400China Department of Hepatology Jingjiang Second People's HospitalJingjiangJiangsu 214500China Department of Hepatology The Third People's Hospital of ChangzhouChangzhouJiangsu 213001China Department of Liver Diseases Shuguang HospitalShanghai University of Traditional Chinese MedicineShanghai 201203China Department of Laboratory Medicine Jiangyin People's HospitalSoutheast UniversityJiangyinJiangsu 214400China Department of Labora

Primary biliary cholangitis(PBC)is an autoimmune liver disease characterized by the destruction of intrahepatic small bile ducts and progressive cholestasis,eventually leading to liver cirrhosis and hepatic failure wi... 详细信息

关键词： cholangitis biliary

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Exploration of Dark Chemical Genomics space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Research Square

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Research square 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, Di Zhang, shuo Mura, Cameron Bourne, Philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between sARs-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVID-19 anti-infective polypharmacology

arXiv

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arXiv 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between sARs-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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The prospects and limitations of liquid biopsy utilization for clinical practice in Taiwan

The journal of liquid biopsy

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The journal of liquid biopsy 2025年 7卷 100290页

作者： Huei-Ying Li Chun-Chuan Chang Yu-Hsuan Yang Chi-Yuan Yao Jason Chia-Hsun Hsieh shao-Hsuan Chang Medical Microbiota Center of the First Core Laboratory National Taiwan University Taipei Taiwan. Professional Master's Program of Biotechnology Management National Taiwan University Taipei Taiwan. Graduate Institute of Medical Genomics and Proteomics National Taiwan University Taipei Taiwan. Department of Laboratory Medicine National Taiwan University Hospital Taipei Taiwan. Division of Oncology Department of Internal Medicine Linkou Chang Gung Memorial Hospital Taoyuan Taiwan. College of Medicine Chang Gung University Taoyuan Taiwan. Department of Biomedical Engineering Chang Gung University Taoyuan Taiwan.

Objective:Liquid biopsy is a promising, non-invasive diagnostic tool for cancer, offering rapid and cost-effective genomic analysis. It provides a less invasive alternative to traditional tissue biopsies, with potential benefits in monitoring disease progression and detecting minimal residual disease (MRD). However, its clinical integration faces challenges, including utility assessment and workflow adaptation. This study evaluates the value of liquid biopsy in Taiwan from a clinical physician's perspective. Methods:A survey was conducted with 16 physicians specializing in thoracic medicine and hematologic oncology. Participants responded to a 5-point Likert scale to evaluate the timing of liquid biopsy adoption, willingness to incorporate it into clinical practice, and agreement on its role in managing specific clinical conditions. Results:Forty percent of physicians preferred liquid biopsy when tissue samples were unavailable. The inclusion of liquid biopsy under National Health Insurance (NHI) was a key factor in its adoption. Hematologic oncologists showed a stronger preference for liquid biopsy, particularly for MRD testing, compared to their counterparts in thoracic medicine (hematologic oncology vs. thoracic medicine: 4.2 ± 0.83 vs. 3.1 ± 0.60; p value = 0.01). Younger physicians valued turnaround time, while senior physicians prioritized test brand, with a focus on report speed. Conclusion:Physicians are generally less inclined to replace tissue biopsies with liquid biopsy, but hematologic oncologists show more flexibility. Test brand plays a role in physician decision-making, and the inclusion of liquid biopsy under NHI coverage is vital for its broader adoption in Taiwan.

关键词： Cancer diagnosis Clinical adoption Liquid biopsy Minimal residual disease (MRD) National health insurance

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The cortical N1 response to balance perturbation is associated with balance and cognitive function in different ways between older adults with and without Parkinson's disease

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Cerebral cortex communications 2022年第3期3卷 tgac030页

作者： Aiden M Payne J Lucas McKay Lena H Ting Wallace H. Coulter Department of Biomedical Engineering Emory University and Georgia Tech Atlanta GA 30332 USA. Department of Biomedical Informatics Emory University School of Medicine Atlanta GA 30322 USA. Jean & Paul Amos Parkinson's Disease & Movement Disorders Program Department of Neurology Emory University School of Medicine Atlanta GA 30322 USA. Division of Physical Therapy Department of Rehabilitation Medicine Emory University Atlanta GA 30322 USA.

Mechanisms underlying associations between balance and cognitive impairments in older adults with and without Parkinson's disease are poorly understood. Balance disturbances evoke a cortical N1 response that is associated with both balance and cognitive abilities in unimpaired populations. We hypothesized that the N1 response reflects neural mechanisms that are shared between balance and cognitive function, and would therefore be associated with both balance and cognitive impairments in Parkinson's disease. Although N1 responses did not differ at the group level, they showed different associations with balance and cognitive function in the Parkinson's disease vs. control groups. In the control group, higher N1 amplitudes were correlated with lower cognitive set shifting ability and lower balance confidence. However, in Parkinson's disease, narrower N1 widths (i.e., shorter durations) were associated with greater parkinsonian motor symptom severity, lower balance ability and confidence, lower mobility, and lower overall cognitive function. Despite different relationships across populations, the present results suggest the N1 response reflects neural processes related to both balance and cognitive function. A better understanding of neural mechanisms linking balance and cognitive function could provide insight into associations between balance and cognitive decline in aging populations.

关键词： EEG aging cognitive-motor interactions posture set shifting

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The Brain Tumor segmentation in Pediatrics (BraTs-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-AsNR-MICCAI BraTs-PEDs)

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron s. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, sanjay P. Reitman, Zachary Rudie, Jeffrey D. sanchez-Montano, Mariana shaikh, Ibraheem salman sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-AsNR-MICCAI BraTs-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-AsNR-MICCAI BraTs-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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Deep learning based signal enhancement of low-resolution accelerometer for fall detection systems

arXiv

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arXiv 2020年

作者： Liu, Kai-Chun Hung, Kuo-Hsuan Hsieh, Chia-Yeh Huang, Hsiang-Yun Chan, Chia-Tai Tsao, Yu Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan32023 Taiwan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei11221 Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City24205 Taiwan

In the last two decades, fall detection (FD) systems have been developed as a popular assistive technology. To support long-term FD services, various power-saving strategies have been implemented. Among them, a reduced sampling rate is a common approach for an energy-efficient system in the real world. However, the performance of FD systems is diminished flowing to low-resolution (LR) accelerometer signals. To improve the detection accuracy with LR accelerometer signals, several technical challenges must be considered, including mismatch of effective features and the degradation effects. In this work, a deeplearning-based accelerometer signal enhancement (AsE) model is proposed as a front-end processor to help typical LR-FD systems achieve better detection performance. The proposed AsE model based on a deep denoising convolutional autoencoder architecture reconstructs high-resolution (HR) signals from the LR signals by learning the relationship between the LR and HR signals. The results show that the FD system using support vector machine and the proposed AsE model at an extremely low sampling rate (sampling rate Copyright © 2020, The Authors. All rights reserved.

关键词： Wearable sensors

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Real world fracture prediction of fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OsTA) and one-minute osteoporosis risk test: An 11-year longitudinal study

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Bone Reports 2024年 20卷 101742页

作者： sheng, Yueh-Hsuan Wu, Tai-Yin Liaw, Chen-Kun Hsiao, sheng-Huang Kuo, Kuan-Liang Tsai, Ching-Yao Department of Family Medicine Taipei City Hospital Renai Branch No. 10 Sec. 4 Renai Rd. Daan Dist. Taipei City Taiwan Department of Family Medicine Taipei City Hospital Zhongxing Branch No. 145 Zhengzhou Rd. Datong Dist. Taipei City Taiwan Institute of Epidemiology and Preventive Medicine National Taiwan University No. 17 Xuzhou Rd. Zhongzheng Dist. Taipei City Taiwan Department of Health and Welfare University of Taipei No. 101 Sec. 2 Zhongcheng Rd. Shilin Dist Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan Department of Orthopedics Shuang Ho Hospital Taipei Medical University No. 291 Zhongzheng Rd. Zhonghe Dist. New Taipei City Taiwan Research Center of Biomedical Device College of Biomedical Engineering Graduate Institute of Biomedical Optomechatronics Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan TMU Biodesign Center Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei City Taiwan Department of Neurosurgery Taipei City Hospital Renai Branch No. 10 Sec. 4 Ren'ai Rd. Da'an Dist. Taipei City Taiwan Department of Psychology National Chengchi University No. 64 Sec. 2 Zhinan Rd. Wenshan Dist. Taipei City Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City Taiwan The Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen University No. 510 Zhongzheng Rd. Xinzhuang Dist. New Taipei City Taiwan Department of Ophthalmology Taipei City Hospital No. 145 Zhengzhou Rd. Datong Dist. Taipei City Taiwan Institute of Public Health National Yang Ming Chiao Tung University No. 155 Sec. 2 Linong St. Beitou Dist. Taipei City Taiwan Department of Business Administration Fu Jen Catholic University No. 510 Zhongzheng Rd. Xinzhuang Dist. New Taipei City Taiwan

Introduction: Fractures affect people's quality of life especially in the elders. One of the most important risk factors is osteoporosis. There are many screening tools to predict osteoporosis and fractures. We aimed to compare the predictive validity of three commonly used screening tools: fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OsTA) and one-minute osteoporosis risk test. Among them, OsTA and one-minute osteoporosis risk test were originally developed to predict osteoporosis risks and FRAX was to predict fracture risks. Methods: This is an 11-year longitudinal study. We enrolled 708 senior people from health examinees in Taiwan in 2010. A standardized questionnaire and blood tests were provided. Annual telephone interview was conducted to assess the real fracture status. We calculated risk scores of FRAX, OsTA, and one-minute osteoporosis risk test and compared with real-world fracture records. Results: The mean age of the participants were 74.9 (sD 6.4). There were 356 (50.3 %) men. From 2010 to 2020, a total of 105 (14.8 %) persons suffered from fractures. Compared to people without fractures, people with fractures had higher FRAX major osteoporotic fracture risk scores (14.0 % ± 7.6 % vs.11.3 % ± 5.7 %), higher hip fracture risk scores, and higher OsTA risk (5.9 % ± 1.4 % vs. 5.3 % ± 1.3 %). Cox regression analysis showed that hazard ratios for fracture of high FRAX risk was 1.53 (95 % confidence interval (CI) 1.05–2.21), and for high OsTA risk was 1.37 (95 % CI 1.04–1.82). Conclusions: Only OsTA and FRAX scores were satisfactory in predicting 10-year fractures. © 2024

关键词： Fracture risk assessment tool (FRAX) Fractures One-minute osteoporosis risk test Osteoporosis self-assessment tool for Asians (OsTA)

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