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Latex microspheres lateral flow immunoassay with smartphone-based device for rapid detection of Cryptococcus

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机构： [1]Department of Respiratory and Critical Care, Emergency and Critical Care Medical Center, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China [2]Shandong Second Medical University, Weifang, 261053, China [3]Datang Telecom Convergence Communications Technology Co., Ltd, Beijing, 100094, China [4]Chinese Academy of Fishery Sciences, Beijing, 100141, China [5]Department of Clinical Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China [6]School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China [7]Medical Laboratory Center, The First Medical Centre, Chinese PLA General Hospital, Beijing, 1000853, China [8]Department of Cadres, 971 Hospital of the Chinese People’s Liberation Army Navy, Qingdao, 266000, China [9]Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China

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Cryptococcus is a pathogenic fungus that poses a threat to human health. Conventional detection methods have limited the rapid and accurate qualitative and quantitative analysis of Cryptococcus, affecting early diagnosis and treatment. In this study, we developed a Point-of-Care Testing (POCT) platform that integrates lateral flow immunoassay (LFIA) with smartphones, enabling both rapid qualitative and quantitative detection of Cryptococcus. The LFIA strip utilizes latex microspheres (LMs) as labeling probes, achieving a detection limit of 3000 CFU/mL and presenting higher sensitivity than the Colloidal Gold Nanoparticles Lateral Flow Immunoassay (AuNPs-LFIA) strip, and approximately eight times that of the AuNPs-LFIA strip. Additionally, it exhibiting no cross-reactivity with over 24 common pathogens and validated in clinical samples. For quantitative analysis, artificial intelligence algorithms were employed to convert smartphone-captured images into grayscale values. Eleven feature values were utilized as a dataset for machine learning to construct a linear regression model, with Mean Squared Error (MSE) and R2 reaching 0.45 and 0.91, respectively. Moreover, the recovery rates in the serum samples ranged from 90.0 % to 108 %, indicating a good practicability. This research presents a rapid diagnostic technology for Cryptococcus and lays the theoretical and technical groundwork for detecting other pathogens.Copyright © 2024. Published by Elsevier B.V.

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出版当年[2023]版：

大类 | 1 区

化学

小类 | 1 区分析化学

最新[2023]版：

大类 | 1 区

化学

小类 | 1 区分析化学

第一作者：

第一作者机构： [1]Department of Respiratory and Critical Care, Emergency and Critical Care Medical Center, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China [2]Shandong Second Medical University, Weifang, 261053, China

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通讯机构： [1]Department of Respiratory and Critical Care, Emergency and Critical Care Medical Center, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China [2]Shandong Second Medical University, Weifang, 261053, China

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