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使光信号归一化可实现基于侧向流动生物传感器的稳健检测。

Normalizing the Optical Signal Enables Robust Assays with Lateral Flow Biosensors.

作者信息

Park Jin-Ho, Park Eung-Kyu, Cho Young Kwan, Shin Ik-Soo, Lee Hakho

机构信息

Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, United States.

Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, United States.

出版信息

ACS Omega. 2022 May 19;7(21):17723-17731. doi: 10.1021/acsomega.2c00793. eCollection 2022 May 31.

Abstract

Lateral flow assays (LFAs) are widely adopted for fast, on-site molecular diagnostics. Obtaining high-precision assay results, however, remains challenging and often requires a dedicated optical setup to control the imaging environment. Here, we describe quick light normalization exam (qLiNE) that transforms ubiquitous smartphones into a robust LFA reader. qLiNE used a reference card, printed with geometric patterns and color standards, for real-time optical calibration: a photo of an LFA test strip was taken along with the card, and the image was processed using a smartphone app to correct shape distortion, illumination brightness, and color imbalances. This approach yielded consistent optical signal, enabling quantitative molecular analyses under different illumination conditions. We adapted qLiNE to detect cortisol, a known stress hormone, in saliva samples at point-of-use settings. The assay was fast (15 min) and sensitive (detection limit, 0.16 ng/mL). The serial qLiNE assay detected diurnal cycles of cortisol levels as well as stress-induced cortisol increase.

摘要

侧向流动分析(LFA)被广泛用于快速、现场分子诊断。然而,获得高精度的检测结果仍然具有挑战性,通常需要专门的光学装置来控制成像环境。在此,我们描述了快速光归一化检测(qLiNE),它将无处不在的智能手机转变为强大的LFA读取器。qLiNE使用印有几何图案和颜色标准的参考卡进行实时光学校准:拍摄LFA测试条的照片时同时拍摄该卡,然后使用智能手机应用程序对图像进行处理,以校正形状失真、照明亮度和颜色不平衡。这种方法产生了一致的光学信号,能够在不同照明条件下进行定量分子分析。我们采用qLiNE在使用点设置下检测唾液样本中的皮质醇(一种已知的应激激素)。该检测快速(15分钟)且灵敏(检测限为0.16 ng/mL)。连续qLiNE检测可检测皮质醇水平的昼夜周期以及应激诱导的皮质醇增加。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7dc6/9161384/94b9e0364293/ao2c00793_0002.jpg

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