SC COBRE for Translational Research Improving Musculoskeletal Health (SC-TRIMH)

SC COBRE 用于改善肌肉骨骼健康的转化研究 (SC-TRIMH)

• 批准号: 10854267
• 负责人: Hai Yao
• 金额: $ 34.55万
• 依托单位: CLEMSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10854267
• 关键词: Acupuncture Therapy; Address; Affect; Area; Assessment tool; Award; Biological Markers; Biomechanics; Biomedical Research; Blood flow; Bone Diseases; Centers of Research Excellence; Cessation of life; Chronic; Circulation; Clinical; Collaborations; Complement; Computer Models; Data; Data Science; Data Scientist; Development; Devices; Diagnosis; Diagnostic Imaging; Doppler Ultrasound; Dose; Effectiveness; Engineering; Evaluation; Fiber Optics; Foundations; Functional disorder; General Population; Goals; Head; Health; Helping to End Addiction Long-term; Image; Injury; Intervention; Location; Machine Learning; Measurement; Measures; Medical; Mentors; Metals; Methods; Monitor; Muscle; Musculoskeletal; Myofascial Pain Syndromes; Myofascial Trigger Point; Needles; Opioid; Pain; Pain Disorder; Pain management; Patients; Physicians; Posture; Preparation; Prevalence; Prevention; Process; Property; Public Health; Research; Research Infrastructure; Research Personnel; Research Proposals; Robotics; Science; Sensitivity and Specificity; Solid; South Carolina; Sports Medicine; Stress; Symptoms; System; Technology; Technology Transfer; Testing; Therapeutic; Tissues; Training; Translational Research; Ultrasonography; United States National Institutes of Health; Universities; Validation; addiction; arthropathies; chronic musculoskeletal pain; combat; design; elastography; fabrication; feature extraction; improved; innovation; instrumentation; machine learning model; manufacture; microsensor; minimally invasive; model development; multi-scale modeling; multidisciplinary; multimodality; muscle stiffness; non-opioid analgesic; novel; novel therapeutics; opioid misuse; opioid use disorder; optical sensor; overdose risk; pre-clinical assessment; prevent; response; sensor; sensor technology; side effect; tool; ultrasound; virtual human; volunteer

Registration of Smart Needle Measurements with Functional Ultrasonography by Machine Learning for Quantitative Monitoring and Assessment of Myofascial Pain Summary This Team Science Development Project complements and extends the scope and objective of the on-going COBRE project entitled “South Carolina COBRE for Translational Research Improving Musculoskeletal Health (SC-TRIMH, P20GM121342). The goal of SC-TRIMH is to enhance and expand the Biomedical Research capacity at Clemson University and the State of South Carolina to promote outstanding multidisciplinary, collaborative, and translational research in bone and joint diseases. SC-TRIMH proposes to implement a novel scientific concept for translational research, i.e., Virtual Human Trials, through powerful computational modeling combined with quantitative functional validation and assessment to expedite the process from concept development to deliverable new therapeutics, interventions, and devices for musculoskeletal health. The specific aims of SC-TRIMH are to: 1) train and mentor an initial cadre of five targeted junior investigators; 2) develop and enhance key areas of research infrastructure through the development of novel cores; and 3) promote the long- term viability of SC-TRIMH through technology transfer and rigorous evaluation and improvement strategies. The scientific cores of SC-TRIMH include: 1) Multi-scale Computational Modeling Core; 2) Advanced Fabrication and Testing Core; and 3) Preclinical Assessment Core. These cores provide key technical support to the junior investigators to implement the new concept of Virtual Human Trials to advance musculoskeletal health and facilitate their competitiveness for national research awards. Chronic musculoskeletal pain is estimated to affect 10% - 20% of the general population and has been a major public health problem. Among patients presenting with chronic musculoskeletal pain, the prevalence of myofascial pain syndrome (MPS) can be as high as 85%. While MPS is believed to be related to the dysfunction of myofascial tissues, however, currently there are no quantitative biomarkers that can quantify myofascial tissue abnormalities in latent or active states of pain to differentiate them from the healthy state. Opioids are commonly used to treat myofascial pain disorders with limited effectiveness but detrimental consequences. To improve treatment of MPS and prevent opioid misuse, there is an urgent need to develop clinically effective quantitative MPS biomarkers for effective management of chronic myofascial pain. The goal of the proposed research is to develop an innovative machine learning (ML) generated biomarker for quantitative assessment and differentiation of abnormal myofascial tissues in latent and active myofascial pain stages from healthy tissues, to objectively monitor and assess the responses to myofascial pain treatment and management. The new quantitative biomarker is enabled by innovatively integrating, through ML models, the functional ultrasonography with novel microsensor-embedded smart needle measurements of the tissue. The hypothesis is that the sensitivity and specificity of the ultrasound imaging diagnostics can be significantly improved by adding accurate anchor point information (e.g., depth-resolved tissue stiffness and oxygenation via a smart needle) to functional ultrasonography such as elastography (muscle stiffness) and Doppler ultrasound (blood flow), through the developed ML models. The improved sensitivity and specificity will allow quantitative structural and functional measurements of the myofascial tissue for better diagnosis and management of MPS. The specific aims are: 1) develop smart needles for minimally invasive measurements of depth-resolved myofascial tissue stiffness and oxygenation and co-register the results with ultrasonography; and 2) develop quantitative metrics from multimodal functional ultrasonography and co-registered smart needle measurements of myofascial tissues. The Team Science Development project will be performed by an interdisciplinary team of engineers, data scientists and physicians with combined expertise in sensor, instrumentation, manufacturing, computing, machine learning, ultrasonography, sports medicine and pain management, and acupuncture. The collaborative team science approach allows us to apply the latest data science method and cutting-edge sensor technologies to addressing the fundamental challenges in pain management. The developed quantitative biomarker is expected to become a new tool for enhancing the diagnosis, assessment, and effective management of MPS via non-opioid therapies and non-pharmacologic treatments, and thus contributes to the NIH HEAL Initiative to 1) improve treatment and prevention of opioid misuse and opioid use disorder and 2) enhance pain management. The initial studies and teaming resulted from this Team Science Development project will lay a solid foundation for preparation and submission of research proposals.

通过机器学习将智能针测量结果与功能性超声检查配准， 肌筋膜疼痛的定量监测和评估 总结 这个团队科学发展项目补充和扩展了正在进行的 名为“南卡罗来纳州COBRE促进转化研究改善肌肉骨骼健康”的COBRE项目 （SC-TRIMH，P20GM121342）。SC-TRIMH的目标是加强和扩大生物医学研究 能力在克莱姆森大学和南卡罗来纳州，以促进杰出的多学科， 骨与关节疾病的合作和转化研究。SC-TRIMH建议实施一种新的 翻译研究的科学概念，即，虚拟人体试验，通过强大的计算建模 结合定量功能验证和评估， 开发用于肌肉骨骼健康的可交付的新疗法、干预措施和设备。具体 SC-TRIMH的目标是：1）培训和指导五名有针对性的初级调查员的初始骨干; 2）开发和 通过开发新的核心，加强关键领域的研究基础设施;以及3）促进长期的 通过技术转让和严格的评估和改进战略，确保SC-TRIMH的长期可行性。 SC-TRIMH的科学核心包括：1）多尺度计算建模核心; 2）先进制造 和测试核心;和3）临床前评估核心。这些核心为初级人员提供关键技术支持 研究人员实施虚拟人体试验的新概念，以促进肌肉骨骼健康， 促进其在国家研究奖方面的竞争力。 据估计，慢性肌肉骨骼疼痛影响10% - 20%的普通人群，并且一直是 重大公共卫生问题。在慢性肌肉骨骼疼痛患者中， 肌筋膜疼痛综合征（MPS）可高达85%。而MPS被认为与功能障碍有关， 然而，目前还没有可以量化肌筋膜组织的定量生物标志物 疼痛的潜伏或活动状态的异常，以将其与健康状态区分开来。阿片类药物通常 用于治疗肌筋膜疼痛疾病，效果有限，但后果不利。提高 治疗MPS和防止阿片类药物滥用，迫切需要制定临床有效的定量 MPS生物标志物可有效治疗慢性肌筋膜疼痛。 拟议研究的目标是开发一种创新的机器学习（ML）生成的生物标志物 用于定量评估和区分潜伏和活动肌筋膜中的异常肌筋膜组织， 从健康组织的疼痛阶段，客观地监测和评估对肌筋膜疼痛治疗的反应 和管理新的定量生物标志物是通过ML模型创新性地整合， 功能性超声检查与新型微传感器嵌入式智能针测量的组织。 假设是超声成像诊断的灵敏度和特异性可以显著提高。 通过添加精确的锚点信息（例如，深度分辨组织硬度和氧合， 智能针）到功能性超声检查，如弹性成像（肌肉硬度）和多普勒超声 （血液流动），通过开发的ML模型。提高的灵敏度和特异性将允许定量 肌筋膜组织的结构和功能测量，以便更好地诊断和治疗MPS。 具体目标是：1）开发用于深度分辨的微创测量的智能针 肌筋膜组织硬度和氧合，并将结果与超声检查相结合;以及2）开发 来自多模态功能超声检查和共配准智能针测量的定量指标 肌筋膜组织。 团队科学发展项目将由一个跨学科的工程师团队，数据 科学家和医生在传感器，仪器，制造，计算， 机器学习、超声波检查、运动医学和疼痛管理以及针灸。合作 团队科学方法使我们能够应用最新的数据科学方法和尖端的传感器技术 来解决疼痛管理中的基本挑战。开发的定量生物标志物是 有望成为加强MPS诊断、评估和有效管理的新工具 通过非阿片类药物治疗和非药物治疗，从而有助于NIH HEAL倡议， 1)改善阿片类药物滥用和阿片类药物使用障碍治疗和预防，以及2）增强疼痛管理。 团队科学发展项目的初步研究和团队合作将奠定坚实的基础 准备和提交研究计划。

项目成果

Mechanical and Viscoelastic Properties of Wrinkled Graphene Reinforced Polymer Nanocomposites - Effect of Interlayer Sliding within Graphene Sheets.

• DOI: 10.1016/j.carbon.2021.02.071
• 发表时间: 2021-06-15
• 期刊: Carbon
• 影响因子: 10.9
• 作者: Wang Y;Meng Z
• 通讯作者: Meng Z

Multidimensional Mechanics of Three-Dimensional Printed and Micro-Architectured Scaffolds.

• DOI: 10.1115/1.4051182
• 发表时间: 2021-06
• 期刊: Journal of applied mechanics
• 作者: Pooya Niksiar;Zhaoxu Meng;M. Porter

SARS-CoV-2 variants of concern Alpha and Delta show increased viral load in saliva.

值得关注的 SARS-CoV-2 变体 Alpha 和 Delta 显示唾液中病毒载量增加。

• DOI: 10.1101/2022.02.10.22270797
• 发表时间: 2022
• 期刊: medRxiv : the preprint server for health sciences
• 作者: King,KylieL;Wilson,Stevin;Napolitano,JustinM;Sell,KeeganJ;Rennert,Lior;Parkinson,ChristopherL;Dean,Delphine
• 通讯作者: Dean,Delphine

A Minimal Information Model for Potential Drug-Drug Interactions.

• DOI: 10.3389/fphar.2020.608068
• 发表时间: 2020
• 期刊: Frontiers in pharmacology
• 影响因子: 5.6
• 作者: Hochheiser H;Jing X;Garcia EA;Ayvaz S;Sahay R;Dumontier M;Banda JM;Beyan O;Brochhausen M;Draper E;Habiel S;Hassanzadeh O;Herrero-Zazo M;Hocum B;Horn J;LeBaron B;Malone DC;Nytrø Ø;Reese T;Romagnoli K;Schneider J;Zhang LY;Boyce RD
• 通讯作者: Boyce RD

Information integrated glass module fabricated by integrated additive and subtractive manufacturing.

• DOI: 10.1364/ol.389203
• 发表时间: 2020-03
• 期刊: Optics letters
• 影响因子: 3.6
• 作者: Qi Zhang;Jincheng Lei;Yizheng Chen;Jianan Tang;Yongji Wu;Liwei Hua;Hai Xiao