Point of care diagnostic for sickle cell disease

镰状细胞病的护理点诊断

• 批准号: 10739074
• 负责人: Adam Wax
• 金额: $ 122.21万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10739074
• 关键词: Acute; Acute Pain; Affect; Africa; Africa South of the Sahara; African American; African American population; Algorithms; American; Analgesics; Archives; Behavior; Biological Assay; Birth; Black American; Blood; Blood specimen; Bone Marrow Transplantation; Businesses; Cells; Clinical; Clinical Research; Clinical Trials; Complete Blood Count; Computer software; Cytology; Cytometry; Data; Descriptor; Development; Devices; Diagnostic; Discrimination; Disease; Economic Burden; Elements; Erythrocytes; Evaluation; Family; Goals; Hand; Health; Healthcare Systems; Hematological Disease; Holography; Image; Imaging Device; Individual; Inherited; Laboratories; Lasers; Lighting; Machine Learning; Measurement; Measures; Mechanics; Medical Device; Microfluidic Microchips; Microfluidics; Monitor; Morphology; Motion; Newborn Infant; Optical Instrument; Outcome; Output; Pain; Patient Care; Patient Monitoring; Patients; Performance; Persons; Phase; Physiologic pulse; Population; Procedures; Property; Pump; Research; Research Personnel; Resource-limited setting; Risk; Role; Sampling; Severities; Severity of illness; Sickle Cell; Sickle Cell Anemia; Syringes; System; Systems Analysis; Technology; Testing; Therapeutic; Therapeutic Intervention; Translating; Whole Blood; Work; artificial intelligence algorithm; base; biophysical properties; cellular imaging; clinically actionable; cost; cost efficient; data streams; design; diagnostic tool; economic evaluation; experience; field study; high throughput analysis; hologram; imaging approach; imaging capabilities; implementation cost; improved; instrument; instrumentation; laboratory equipment; laptop; machine learning algorithm; new technology; novel diagnostics; novel strategies; novel therapeutic intervention; parallel computer; point of care; point-of-care diagnostics; pressure; prototype; screening; sensor; time use; tool; translational potential; vaso-occlusive crisis

Project Summary Sickle cell disease (SCD) is severe hematological disease seen in Sub-Saharan Africa and the U.S., affecting up to 3% of the newborn population Africa. In the US, the disease affects 100,000 Americans, but is disproportionately borne by African Americans with one out of every 365 African-Americans affected. SCD patients experience vaso-occlusive crises (VOCs) as acute bouts of pain. Currently, the only cure for SCD is a bone marrow transplant but the cost and difficulty of this procedure results in SCD patients often choosing to instead manage their disease with treatments and routine monitoring. However, there is currently no objective measure or lab test to determine VOCs and associated pain severity. Routine monitoring of red blood cell (RBC) health, however, could provide a window for avoiding VOCs and enable more thorough, quantitative analysis of therapeutic interventions. The goal of this project is to create a prototype device suitable for use at the point of care for monitoring SCD patients by advancing a novel technology for rapid, automated, high throughput red blood cell (RBC) imaging. The approach is based on quantitative phase imaging (QPI) to create a hologram of every individual RBC in a sample. These imaging data can then be evaluated for the proportion of sickled cells using machine learning. Further, since up to 10^6 RBCs are imaged in each sample, a large volume of data will be obtained, suitable for developing AI algorithms that can reveal additional information. Significantly, the device is compact and low cost, indicating that there is significant potential for translation to a point of care device. In this project, we will seek to translate our technology, with demonstrated high throughput imaging capabilities, to realize a prototype device suitable for use in clinical trials. They key development steps include development of the imaging device to enable a robust, compact form factor, advancing the design of the microfluidic device that carries the RBC's and creating new and improved algorithms to enable high throughput segmentation and analysis of the obtained imaging data. The output will be a prototype device for assessing the RBC health of individuals based using a small blood draw which can be analyzed locally to enable treatment decisions more quickly.

项目概要 镰状细胞病 (SCD) 是撒哈拉以南非洲和美国常见的严重血液疾病，影响 高达非洲新生儿人口的 3%。在美国，这种疾病影响了 10 万美国人，但 非裔美国人承担的比例过高，每 365 名非裔美国人中就有 1 人受到影响。 SCD 患者会经历血管闭塞危象（VOC），表现为急性疼痛。目前，治疗 SCD 的唯一方法是 骨髓移植，但该手术的成本和难度导致 SCD 患者经常选择 相反，通过治疗和常规监测来控制疾病。但目前尚无目标 测量或实验室测试以确定挥发性有机化合物和相关的疼痛严重程度。红细胞（RBC）的常规监测 然而，健康可以为避免挥发性有机化合物提供一个窗口，并能够对挥发性有机化合物进行更彻底的定量分析。 治疗干预。 该项目的目标是创建一款适合在护理点监测 SCD 的原型设备 通过推进快速、自动化、高通量红细胞 (RBC) 成像新技术，为患者提供服务。 该方法基于定量相位成像 (QPI)，可创建每个个体红细胞的全息图。 样本。然后可以使用机器学习评估这些成像数据的镰状细胞比例。 此外，由于每个样本中多达 10^6 个红细胞成像，因此将获得大量数据，适合 开发可以揭示更多信息的人工智能算法。值得注意的是，该设备结构紧凑、体积小 成本，表明转化为护理点设备的潜力巨大。在这个项目中，我们将 寻求将我们的技术转化为具有高通量成像能力的技术，以实现原型 适用于临床试验的设备。他们的关键开发步骤包括成像设备的开发 实现坚固、紧凑的外形，推进携带 RBC 的微流体装置的设计 创建新的和改进的算法，以实现对所获得的数据进行高通量分割和分析 成像数据。输出将是一个原型设备，用于评估个人的红细胞健康状况 抽取少量血液，可以在本地进行分析，以便更快地做出治疗决策。