Point of care diagnostic for sickle cell disease

项目摘要 镰状细胞病(SCD)是一种严重的血液疾病，见于撒哈拉以南非洲和美国，影响 高达非洲新生儿人口的3%。在美国，这种疾病影响着10万美国人，但 非裔美国人承担的比例不成比例，每365名非裔美国人中就有一人受到影响。SCD 患者经历的血管闭塞危象(VOCs)是剧烈的疼痛发作。目前，治疗SCD的唯一方法是 骨髓移植，但这种手术的成本和难度导致SCD患者经常选择 取而代之的是通过治疗和常规监测来管理他们的疾病。然而，目前还没有客观的 测量或实验室测试以确定VOCs和相关的疼痛严重程度。红细胞(RBC)常规监测 然而，健康可以为避免VOCs提供一个窗口，并使更彻底的、定量的分析成为可能 治疗性干预。 该项目的目标是创建一种适合在护理点使用的监测SCD的原型设备 通过推进一种快速、自动化、高通量的红细胞(RBC)成像的新技术，我们为患者提供了一种新的治疗方法。 这种方法是基于定量相位成像(QPI)来创建一个独立的红细胞的全息图 样本。然后，可以使用机器学习来评估这些成像数据中镰状细胞的比例。 此外，由于在每个样本中成像多达10^6个红细胞，因此将获得大量数据，适用于 开发能够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，重量低 成本，这表明有很大的潜力转换为护理点设备。在这个项目中，我们将 寻求将我们的技术转化为具有已证明的高通量成像能力，以实现原型 适用于临床试验的装置。这些关键开发步骤包括成像设备的开发 以实现坚固、紧凑的外形因素，推动携带红细胞的微流控设备的设计 以及创建新的和改进的算法以实现对所获得的高吞吐量的分割和分析 成像数据。该输出将是一个原型设备，用于评估个人的红细胞健康，基于 少量抽血，可在当地进行分析，以便更快地做出治疗决定。

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.