Ilyas Shazia, Sher Mazhar, Du E, Asghar Waseem
Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA; Asghar-Lab, Micro and Nanotechnology in Medicine, College of Engineering and Computer Science, Boca Raton, FL, 33431, USA.
Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL, 33431, USA; Department of Biological Sciences (Courtesy Appointment), Florida Atlantic University, Boca Raton, FL, 33431, USA.
Biosens Bioelectron. 2020 Oct 1;165:112417. doi: 10.1016/j.bios.2020.112417. Epub 2020 Jul 9.
Sickle cell disease (SCD) is a worldwide hematological disorder causing painful episodes, anemia, organ damage, stroke, and even deaths. It is more common in sub-Saharan Africa and other resource-limited countries. Conventional laboratory-based diagnostic methods for SCD are time-consuming, complex, and cannot be performed at point-of-care (POC) and home settings. Optical microscope-based classification and counting demands a significant amount of time, extensive setup, and cost along with the skilled human labor to distinguish the normal red blood cells (RBCs) from sickled cells. There is an unmet need to develop a POC and home-based test to diagnose and monitor SCD and reduce mortality in resource-limited settings. An early-stage and timely diagnosis of SCD can help in the effective management of the disease. In this article, we utilized a smartphone-based image acquisition method for capturing RBC images from the SCD patients in normoxia and hypoxia conditions. A computer algorithm is developed to differentiate RBCs from the patient's blood before and after cell sickling. Using the developed smartphone-based technique, we obtained similar percentage of sickle cells in blood samples as analyzed by conventional method (standard microscope). The developed method of testing demonstrates the potential utility of the smartphone-based test for reducing the overall cost of screening and management for SCD, thus increasing the practicality of smartphone-based screening technique for SCD in low-resource settings. Our setup does not require any special storage requirements. This is the characteristic advantage of our technique as compared to other hemoglobin-based POC diagnostic techniques.
镰状细胞病（SCD）是一种全球性血液疾病，会引发疼痛发作、贫血、器官损伤、中风甚至死亡。它在撒哈拉以南非洲和其他资源有限的国家更为常见。传统的基于实验室的SCD诊断方法耗时、复杂，且无法在即时护理（POC）和家庭环境中进行。基于光学显微镜的分类和计数需要大量时间、广泛的设置和成本，还需要熟练的人工来区分正常红细胞（RBC）和镰状细胞。开发一种用于诊断和监测SCD并降低资源有限地区死亡率的POC和家庭检测方法的需求尚未得到满足。SCD的早期及时诊断有助于有效管理该疾病。在本文中，我们利用基于智能手机的图像采集方法，在常氧和低氧条件下从SCD患者采集红细胞图像。开发了一种计算机算法，用于区分患者血液中细胞镰变前后的红细胞。使用所开发的基于智能手机的技术，我们在血样中获得的镰状细胞百分比与传统方法（标准显微镜）分析的结果相似。所开发的检测方法证明了基于智能手机的检测在降低SCD筛查和管理总成本方面的潜在效用，从而提高了基于智能手机的SCD筛查技术在低资源环境中的实用性。我们的设置不需要任何特殊的存储要求。与其他基于血红蛋白的POC诊断技术相比，这是我们技术的独特优势。
