Dynamic Tracer Kinetic Model to Detect Preclinical Diabetic Retinopathy (DR)

用于检测临床前糖尿病视网膜病变 (DR) 的动态示踪动力学模型

基本信息

项目摘要

Project Summary Diabetic retinopathy (DR) is one of the most common complications associated with diabetes. Detection of clinical DR signs can take several years from the onset of diabetes; hence, the long preclinical phase should provide a window to apply interventions that can slow or prevent progression to clinical endpoint (mild to severe visual impairment). In fact, early detection and treatment of DR can prevent more than 90% of vision loss. However, the current unmet clinical challenge is finding an appropriate tool or technology to detect preclinical signs (biomarkers) of DR. Since the retinal vessels are early and prevalent targets of diabetic damage, sensitive identifiers of structural and functional blood vessel changes hold great potential as biomarkers. Recent advances in retinal imaging technology have allowed a better visualization of vessel characteristics. Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO) and OCT angiography (OCTA) studies recently suggested that there may be a transitional vascular remodeling during the preclinical phase in diabetic patients. Though the main benefit of these technologies is the non-invasive nature of data acquisition, there are limitations (e.g., long scan times, limited field-of-view, motion artifacts and need for an expert operator) that prevent these technologies to be effective preclinical detection tools to be used in a clinical setting. Therefore, there is a great need for enhanced detection sensitivity and quantitative means to analyze the early preclinical vasculature changes that can be readily translated into clinical practice. To address this critical unmet clinical need, we have developed a novel dynamic tracer kinetic model to measure quantitatively vascular permeability and blood flow changes based on fluorescein video-angiography (FVA). The approach is immediately translatable to FVA data collected in patients as demonstrated by our preliminary data. In this proposal, we will demonstrate that our dynamic tracer kinetic model can detect preclinical DR with a higher sensitivity and specificity than other retinal imaging modalities such as OCTA and AOSLO. Specific Aim 1 will optimize/validate the retinal vascular permeability and blood flow measurements against gold standard techniques of permeability (Evans-blue) and blood flow (microspheres). Specific Aim 2 will demonstrate that longitudinal preclinical changes in the retinal vascular permeability and blood flow detected by our model will occur before clinical retinopathy in diabetic rodent model. Specific Aim 3 will characterize longitudinal changes in retinal vascular permeability and blood flow in both normal subjects and diabetic patients. Specific Aim 4 will demonstrate higher sensitivity of preclinical DR detection by the dynamic tracer kinetic model over optical coherence tomography angiography (OCTA) and adaptive optics scanning laser ophthalmoscopy (AOSLO) in diabetic patients without clinical retinopathy (DMnoDR).
项目摘要 糖尿病视网膜病变(DR)是糖尿病最常见的并发症之一。检测 糖尿病的临床表现可能需要数年的时间;因此,较长的临床前阶段应该 提供一个应用干预措施的窗口,以减缓或防止进展至临床终点(轻微至 严重视力障碍)。事实上,早期发现和治疗DR可以阻止90%以上的视力 损失。然而,目前尚未满足的临床挑战是找到合适的工具或技术来检测 糖尿病的临床前体征(生物标记物),因为视网膜血管是糖尿病的早期和流行的目标 损伤,结构和功能血管变化的敏感识别符具有巨大的潜力 生物标志物。视网膜成像技术的最新进展使血管有了更好的可视化 特点。自适应光学扫描激光眼底镜(AOSLO)和OCT血管造影(OCTA) 最近的研究表明,在临床前阶段可能存在过渡性血管重塑。 糖尿病患者。尽管这些技术的主要优点是数据采集的非侵入性, 存在一些限制(例如,较长的扫描时间、有限的视野、运动伪影和需要专家 操作员)阻止这些技术成为临床上使用的有效临床前检测工具 布景。因此,迫切需要提高检测灵敏度和定量分析手段 早期临床前血管改变,可以很容易地转化为临床实践。要解决这个问题 ,我们开发了一种新的动态示踪动力学模型来定量测量 血管通透性和血流量的变化基于荧光素视频血管造影(FVA)。方法是 如我们的初步数据所示,可立即转换为患者收集的FVA数据。在这 建议,我们将证明我们的动态示踪剂动力学模型可以检测到临床前DR具有更高的 与OCTA和AOSLO等其他视网膜成像方法相比,具有更高的敏感性和特异性。具体目标1将 对照金标准优化/验证视网膜血管通透性和血流量测量 渗透性(伊文思蓝)和血液流动(微球)技术。具体目标2将证明 我们的模型检测到的视网膜血管通透性和血流的临床前纵向变化将 糖尿病啮齿动物模型中视网膜病变发生在临床之前。具体目标3将描述纵向变化的特征 正常受试者和糖尿病患者的视网膜血管通透性和血流量。具体目标4将 通过动态示踪剂动力学模型比光学技术显示更高的临床前DR检测灵敏度 相干断层血管造影术(OCTA)和自适应光学扫描激光眼底镜(AOSLO) 糖尿病无临床视网膜病变患者(DMnoDR)。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

JENNIFER J Kang-Mieler其他文献

JENNIFER J Kang-Mieler的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('JENNIFER J Kang-Mieler', 18)}}的其他基金

Dynamic Tracer Kinetic Model to Detect Preclinical Diabetic Retinopathy (DR)
用于检测临床前糖尿病视网膜病变 (DR) 的动态示踪动力学模型
  • 批准号:
    10708172
  • 财政年份:
    2021
  • 资助金额:
    $ 53.08万
  • 项目类别:
Dynamic Tracer Kinetic Model to Detect Preclinical Diabetic Retinopathy (DR)
用于检测临床前糖尿病视网膜病变 (DR) 的动态示踪动力学模型
  • 批准号:
    10612529
  • 财政年份:
    2021
  • 资助金额:
    $ 53.08万
  • 项目类别:
Sustained Ocular Drug Delivery System for Anti-VEGF Agents
抗 VEGF 药物持续眼部给药系统
  • 批准号:
    10363699
  • 财政年份:
    2019
  • 资助金额:
    $ 53.08万
  • 项目类别:
Sustained Ocular Drug Delivery System for Anti-VEGF Agents
抗 VEGF 药物持续眼部给药系统
  • 批准号:
    10608062
  • 财政年份:
    2019
  • 资助金额:
    $ 53.08万
  • 项目类别:
Sustained Ocular Drug Delivery System for Anti-VEGF Agents
抗 VEGF 药物持续眼部给药系统
  • 批准号:
    10307325
  • 财政年份:
    2019
  • 资助金额:
    $ 53.08万
  • 项目类别:
Sustained Ocular Drug Delivery System for Anti-VEGF Agents
抗 VEGF 药物持续眼部给药系统
  • 批准号:
    9918421
  • 财政年份:
    2019
  • 资助金额:
    $ 53.08万
  • 项目类别:
Sustained Ocular Drug Delivery System for Anti-VEGF Agents
抗 VEGF 药物持续眼部给药系统
  • 批准号:
    10645936
  • 财政年份:
    2019
  • 资助金额:
    $ 53.08万
  • 项目类别:
Efficacy of the Microsphere-Thermo-Responsive Hydrogel Ocular Drug Delivery System
微球热响应水凝胶眼部给药系统的功效
  • 批准号:
    9099053
  • 财政年份:
    2016
  • 资助金额:
    $ 53.08万
  • 项目类别:
BME design challenge of improving surgical safety
提高手术安全性的 BME 设计挑战
  • 批准号:
    9058041
  • 财政年份:
    2012
  • 资助金额:
    $ 53.08万
  • 项目类别:
Biocompatibility of thermo-responsive hydrogel ocular drug delivery system
热响应水凝胶眼部给药系统的生物相容性
  • 批准号:
    7940224
  • 财政年份:
    2010
  • 资助金额:
    $ 53.08万
  • 项目类别:

相似海外基金

ImproviNg rEnal outcomes following coronary angiograPhy and/or percuTaneoUs coroNary intErventions: a pragmatic, adaptive, patient-oriented randomized controlled trial
改善冠状动脉造影和/或经皮冠状动脉介入治疗后的肾脏结局:一项务实、适应性、以患者为导向的随机对照试验
  • 批准号:
    478732
  • 财政年份:
    2023
  • 资助金额:
    $ 53.08万
  • 项目类别:
    Operating Grants
SBIR Phase II: Novel size-changing, gadolinium-free contrast agent for magnetic resonance angiography
SBIR II 期:用于磁共振血管造影的新型尺寸变化、无钆造影剂
  • 批准号:
    2322379
  • 财政年份:
    2023
  • 资助金额:
    $ 53.08万
  • 项目类别:
    Cooperative Agreement
Neonatal Optical Coherence Tomography Angiography to Assess the Effects of Postnatal Exposures on Retinal Development and Predict Neurodevelopmental Outcomes
新生儿光学相干断层扫描血管造影评估产后暴露对视网膜发育的影响并预测神经发育结果
  • 批准号:
    10588086
  • 财政年份:
    2023
  • 资助金额:
    $ 53.08万
  • 项目类别:
Motion-Resistant Background Subtraction Angiography with Deep Learning: Real-Time, Edge Hardware Implementation and Product Development
具有深度学习的抗运动背景减影血管造影:实时、边缘硬件实施和产品开发
  • 批准号:
    10602275
  • 财政年份:
    2023
  • 资助金额:
    $ 53.08万
  • 项目类别:
Highly Accelerated Magnetic Resonance Angiography using Deep Learning
使用深度学习的高加速磁共振血管造影
  • 批准号:
    2886357
  • 财政年份:
    2023
  • 资助金额:
    $ 53.08万
  • 项目类别:
    Studentship
Development of a method to simultaneously obtain cerebral blood flow information and progression of cerebral white matter lesions using head MR angiography.
开发一种使用头部磁共振血管造影同时获取脑血流信息和脑白质病变进展的方法。
  • 批准号:
    23K14839
  • 财政年份:
    2023
  • 资助金额:
    $ 53.08万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Development of a new diagnostic method for coronary artery disease using automated image analysis with postmortem coronary angiography CT
使用死后冠状动脉造影 CT 自动图像分析开发冠状动脉疾病的新诊断方法
  • 批准号:
    23K19795
  • 财政年份:
    2023
  • 资助金额:
    $ 53.08万
  • 项目类别:
    Grant-in-Aid for Research Activity Start-up
Novel ultrahigh speed swept source OCT angiography methods in diabetic retinopathy
糖尿病视网膜病变的新型超高速扫源 OCT 血管造影方法
  • 批准号:
    10656644
  • 财政年份:
    2023
  • 资助金额:
    $ 53.08万
  • 项目类别:
Automated Machine Learning-Based Brain Artery Segmentation, Anatomical Prior Labeling, and Feature Extraction on MR Angiography
基于自动机器学习的脑动脉分割、解剖先验标记和 MR 血管造影特征提取
  • 批准号:
    10759721
  • 财政年份:
    2023
  • 资助金额:
    $ 53.08万
  • 项目类别:
SCH: A physics-informed machine learning approach to dynamic blood flow analysis from static subtraction computed tomographic angiography imaging
SCH:一种基于物理的机器学习方法,用于从静态减影计算机断层血管造影成像中进行动态血流分析
  • 批准号:
    2205265
  • 财政年份:
    2022
  • 资助金额:
    $ 53.08万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了