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

文献类型： 外文期刊

作者： Zang, Xuelei 1 ; Zhou, Yangyu 1 ; Li, Shuming 3 ; Shi, Gang 4 ; Deng, Hengyu 2 ; Zang, Xuefeng 1 ; Cao, Jingrong 5 ; Yang, Ruonan 6 ; Lin, Xuwen 1 ; Deng, Hui 1 ; Huang, Yemei 1 ; Yang, Chen 7 ; Wu, Ningxin 8 ; Song, Chao 9 ; Wu, Lidong 4 ; Xue, Xinying 1 ;

作者机构： 1.Capital Med Univ, Beijing Shijitan Hosp, Emergency & Crit Care Med Ctr, Dept Resp & Crit Care, Beijing 100038, Peoples R China

2.Shandong Second Med Univ, Weifang 261053, Peoples R China

3.Datang Telecom Convergence Commun Technol Co Ltd, Beijing 100094, Peoples R China

4.Chinese Acad Fishery Sci, Beijing 100141, Peoples R China

5.Capital Med Univ, Xuanwu Hosp, Dept Clin Lab, Beijing 100053, Peoples R China

6.Nanjing Normal Univ, Sch Food Sci & Pharmaceut Engn, Nanjing 210023, Peoples R China

7.Chinese Peoples Liberat Army Gen Hosp, Med Lab Ctr, Med Ctr 1, Beijing 1000853, Peoples R China

8.971 Hosp Chinese Peoples Liberat Army Navy, Dept Cadres, Qingdao 266000, Peoples R China

9.Chinese Acad Fishery Sci, Freshwater Fisheries Res Ctr, Wuxi 214081, Peoples R China

期刊名称：TALANTA （ 影响因子：6.1； 五年影响因子：5.5 ）

ISSN： 0039-9140

年卷期： 2025 年 284 卷

页码：

收录情况： SCI

摘要： 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.

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