Department of Mechanical and Aerospace Engineering, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH 44106, USA.
Department of Physics, Case Western Reserve University, Cleveland, OH 44106, USA.
Lab Chip. 2021 Oct 12;21(20):3863-3875. doi: 10.1039/d1lc00371b.
Anemia affects over 25% of the world's population with the heaviest burden borne by women and children. Genetic hemoglobin (Hb) variants, such as sickle cell disease, are among the major causes of anemia. Anemia and Hb variant are pathologically interrelated and have an overlapping geographical distribution. We present the first point-of-care (POC) platform to perform both anemia detection and Hb variant identification, using a single paper-based electrophoresis test. Feasibility of this new integrated diagnostic approach is demonstrated testing individuals with anemia and/or sickle cell disease. Hemoglobin level determination is performed by an artificial neural network (ANN) based machine learning algorithm, which achieves a mean absolute error of 0.55 g dL and a bias of -0.10 g dL against the gold standard (95% limits of agreement: 1.5 g dL) from Bland-Altman analysis on the test set. Resultant anemia detection is achieved with 100% sensitivity and 92.3% specificity. With the same tests, subjects with sickle cell disease were identified with 100% sensitivity and specificity. Overall, the presented platform enabled, for the first time, integrated anemia detection and hemoglobin variant identification using a single point-of-care test.
贫血影响着全球超过 25%的人口，其中妇女和儿童所受影响最为严重。遗传性血红蛋白（Hb）变体，如镰状细胞病，是贫血的主要原因之一。贫血和 Hb 变体在病理上相互关联，且具有重叠的地理分布。我们首次提出了一种即时检测（POC）平台，可同时进行贫血检测和 Hb 变体识别，使用的是单一基于纸质的电泳测试。通过对患有贫血症和/或镰状细胞病的个体进行测试，验证了这种新的集成诊断方法的可行性。血红蛋白水平的测定是通过基于人工神经网络（ANN）的机器学习算法进行的，该算法在测试集上的 Bland-Altman 分析中实现了 0.55g/dL 的平均绝对误差和 0.10g/dL 的偏差（95%一致性界限：1.5g/dL）。由此得出的贫血检测结果具有 100%的灵敏度和 92.3%的特异性。使用相同的测试，镰状细胞病患者的识别具有 100%的灵敏度和特异性。总的来说，该平台首次实现了使用单一即时检测来进行集成的贫血检测和血红蛋白变体识别。
