Point-of-care microchip electrophoresis for integrated anemia and hemoglobin variant testing

An, Ran; Man, Yuncheng; Iram, Shamreen; Kucukal, Erdem; Hasan, Mahmud; Huang, Yu‐Ning; Goreke, Utku; Bode, Allison; Hill, Ailis; Cheng, Kevin; Sekyonda, Zoe; Ahuja, Sanjay P.; Little, Jane A.; Hinczewski, Michael; Gürkan, Umut A.

doi:10.1039/d1lc00371b

Cited by 22 publications

(19 citation statements)

References 59 publications

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“…Quantification of HbA and key Hb variants including HbF and Hb HbA2 are essential for accurate detection of β-Thal. The previous Gazelle system has demonstrated its utility in detecting anemia [23] and hemoglobinopathies including SCD, sickle cell trait, Hemoglobin C disorder, and Hemoglobin E Disorder [15][16][17][18][19][20][21][22]. Here, we report the updated version Gazelle which enables, for the first time, POC detection of β-Thal.…”

Section: Discussionmentioning

confidence: 99%

“…A customized data analysis algorithm was integrated in the Gazelle system. This data analysis algorithm automatically identifies β-Thal major, β-Thal intermedia, and β-Thal trait based on Hb band migration pattern as described previously [21, 23]. The data analysis algorithm also automatically quantifies the relative percentages of HbA, HbF, and HbA 2 in addition to other hemloglobins as previously reported.…”

Section: Methodsmentioning

confidence: 99%

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First Point-of-Care Diagnostic Test for Beta-Thalassemia

Avanaki

Thota

et al. 2022

Preprint

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Hemoglobin (Hb) disorders are among the most common monogenic diseases affecting nearly 7% of the world population. Among various Hb disorders, approximately 1.5% of the world population carry beta thalassemia (beta Thal), affecting 40,000 newborns every year. Early screening and timely diagnosis are essential in beta thalassemia patients for prevention and management of later clinical complications. However, in Africa to Southern Europe, Middle East, and Southeast Asia, where beta thalassemia is most prevalent, diagnosis and screening of beta thalassemia is still challenging due to the cost and logistical burden of laboratory diagnostic tests. Here, we present Gazelle, a paper-based microchip electrophoresis platform, that enables the first point-of-care diagnostic test for beta thalassemia. We evaluated the accuracy of Gazelle for beta Thal screening in 372 subjects in the age range of 4 to 63 years at Apple Diagnostics lab in Mubai, India. Additionally, 30 blood samples were prepared to mimic beta Thal intermediate and beta Thal major samples. The Gazelle detected levels of Hb A, Hb F, and Hb A2 demonstrated high correlations with the results reported by the laboratory gold standard, high performance liquid chromatography (HPLC) yielding a Pearson Correlation Coefficient = 0.99. The ability to obtain rapid and accurate results suggest that Gazelle can be suitable for large-scale screening and diagnosis of beta-Thal.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

First Point-of-Care Diagnostic Test for Beta-Thalassemia

Avanaki

Thota

et al. 2022

Preprint

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“…To achieve a highly accurate classification of the Raman spectra, we built an ANN to extract the characteristic information of the data (Figure 4a) (see the details of data processing and ANN architecture in Materials and methods). Different from the ANN used for image classification, [49,50] our ANN uses one-dimensional convolutional layers and removes the pooling layers to reduce the computational cost because the Raman spectra are onedimensional data that do not require pooling layers. Moreover, the presence of pooling layers leads to the loss of details in spectra and thus reduces the accuracy of classification.…”

Section: Acquisition Of Raman Spectra and Ann Architecturementioning

confidence: 99%

Optofluidic identification of single microorganisms using fiber‐optical‐tweezer‐based Raman spectroscopy with artificial neural network

Lin

et al. 2023

BMEMat

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Rapid and accurate detection of microorganisms is critical to clinical diagnosis.As Raman spectroscopy promises label-free and culture-free detection of biomedical objects, it holds the potential to rapidly identify microorganisms in a single step. To stabilize the microorganism for spectrum collection and to increase the accuracy of real-time identification, we propose an optofluidic method for single microorganism detection in microfluidics using opticaltweezing-based Raman spectroscopy with artificial neural network. A fiber optical tweezer was incorporated into a microfluidic channel to generate optical forces that trap different species of microorganisms at the tip of the tweezer and their Raman spectra were simultaneously collected. An artificial neural network was designed and employed to classify the Raman spectra of the microorganisms, and the identification accuracy reached 94.93%. This study provides a promising strategy for rapid and accurate diagnosis of microbial infection on a lab-on-a-chip platform.Chenghong Lin and Xiaofeng Li contributed equally to this work.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…MCE has been widely used in analyzing biological species based on its specific advantages such as its low sample consumption, high throughput, and short separation time. − Much effort has been paid to combining MCE with nucleic-acid-based circle amplification methods, including PCR, exonuclease-assisted circle amplification, RCA, HCR, and CHA, to improve the detection sensitivity of MCE. As we know, foodborne pathogenic bacteria can induce various infectious diseases at low concentrations.…”

Section: Microscale Systems “See” In Bacteriamentioning

confidence: 99%