I-Corps: Clinical Opportunities for Lens Free Holographic Urinalysis

I-Corps:无透镜全息尿液分析的临床机会

基本信息

  • 批准号:
    2311169
  • 负责人:
  • 金额:
    $ 5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-05-01 至 2025-04-30
  • 项目状态:
    未结题

项目摘要

The broader impact/commercial potential of this I-Corps project is the development of a device for improving the management of indwelling urinary catheters (IUC) in the hospital setting. Tens of millions of IUCs are inserted each year in the United States to relieve urinary retention, monitor urine output, and manage urination. However, a significant problem occurs with catheter-associated urinary tract infections (CA-UTI) - the most common nosocomial infection in the United States – which are caused by bacterial colonization of the urinary tract following placement of an IUC. A significant risk factor for CA-UTI is prolonged use of an IUC, and a majority of these cases are believed to be preventable through timely intervention. Currently, the standard of practice is to examine urine with the naked eye for problems and measure volume intermittently. Early detection of inflammatory biomarkers and bacterial load in the urinary system using the proposed technology may enable objective and timely removal of the IUC before the onset of CA-UTI. In addition to enabling the development of evidence-based triggers for catheter removal, real-time monitoring of urine flow has the potential to increase fluid management precision and detect acute changes.This I-Corps project is based on the development of a lens-free imaging device capable of continuous, non-invasive analysis of urine for measuring clinical biomarkers to detect early signs of catheter-associated urinary tract infections (CA-UTI). The proposed device sits in-line between an indwelling urinary catheter (IUC) and an attached urine drainage bag, providing real-time urinalysis at the bedside. Lens-free digital holography is used to acquire holograms of the urine as it flows through the device. A 3D sparse phase recovery reconstruction algorithm transforms single raw 2D hologram images into reconstructed 3D volumes. Particulates in each 3D volume are classified and quantified for reporting clinically relevant information including bacteria colony forming units (CFUs), white blood cell count, and the presence of red blood cell casts. A benchtop prototype system has been validated using human urine with known additions of red and white blood cells and/or E. coli bacteria. Measurements made with the benchtop system were compared with measurements from conventional methods, and the system was confirmed to be sensitive to concentrations several orders of magnitude above and below thresholds of pyuria, hematuria, and bacterial CFUs used for the diagnosis of UTIs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该I-Corps项目的更广泛影响/商业潜力是开发一种用于改善医院环境中留置导尿管(IUC)管理的器械。在美国,每年有数千万个宫内节育器被插入,以缓解尿潴留,监测尿量和管理排尿。然而,导管相关性尿路感染(CA-UTI)-在美国最常见的医院感染-发生了显著的问题,其由放置IUC后尿路的细菌定植引起。CA-UTI的一个重要风险因素是长期使用IUC,大多数病例被认为是可以通过及时干预预防的。 目前,实践标准是用肉眼检查尿液是否有问题,并间歇性地测量体积。使用所提出的技术早期检测泌尿系统中的炎症生物标志物和细菌负荷,可以在CA-UTI发作之前客观和及时地去除IUC。除了能够开发基于证据的导管移除触发器外,尿流实时监测还具有提高液体管理精度和检测急性变化的潜力。I-Corps项目基于开发一种无透镜成像设备,该设备能够连续、非侵入性地分析尿液,用于测量临床生物标志物,以检测导管相关尿路感染(CA-UTI)的早期体征。 该装置位于留置导尿管(IUC)和连接的尿液引流袋之间,在床边提供实时尿液分析。无透镜数字全息术用于在尿液流过该装置时获取尿液的全息图。3D稀疏相位恢复重建算法将单个原始2D全息图像转换为重建的3D体积。对每个3D体积中的颗粒进行分类和定量,以报告临床相关信息,包括细菌菌落形成单位(CFU)、白色血细胞计数和是否存在红细胞管型。一个台式原型系统已被验证,使用人尿与已知的添加红和白色血细胞和/或大肠杆菌。大肠杆菌将用台式系统进行的测量与常规方法的测量进行比较,并且证实该系统对高于和低于脓尿、血尿该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响审查进行评估来支持的搜索.

项目成果

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Nicholas Durr其他文献

Evaluating a CO<sub>2</sub>-based cryoablation device for cancer treatment in low resource settings: a benchtop comparison and in vivo validation study in veterinary models
  • DOI:
    10.1016/j.cryobiol.2023.104760
  • 发表时间:
    2023-12-01
  • 期刊:
  • 影响因子:
  • 作者:
    Yixin Hu;Bailey Surtees;Naomi Gordon;Kathleen Gabrielson;Nicholas Durr;Dara Kraitchman
  • 通讯作者:
    Dara Kraitchman
A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit
一种深度学习方法,用于根据重症监护病房常规测量的颅外信号生成颅内压波形
  • DOI:
    10.1016/j.compbiomed.2024.108677
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    7.7
  • 作者:
    Shiker S. Nair;Alina Guo;Joseph Boen;Ataes Aggarwal;Ojas Chahal;A. Tandon;Meer Patel;Sreenidhi Sankararaman;Nicholas Durr;Tej D. Azad;Romain Pirracchio;Robert D. Stevens
  • 通讯作者:
    Robert D. Stevens

Nicholas Durr的其他文献

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{{ truncateString('Nicholas Durr', 18)}}的其他基金

CAREER: Computational structured light imaging for widefield mapping of histologic primitives
职业:用于组织学基元宽场绘图的计算结构光成像
  • 批准号:
    2146333
  • 财政年份:
    2022
  • 资助金额:
    $ 5万
  • 项目类别:
    Continuing Grant

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  • 批准号:
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  • 批准年份:
    2010
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    34.0 万元
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