Effect of sample volume on the sensitivity of lateral flow assays through computational modeling

Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays

Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (<60 s) of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in more than 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. We found that images acquired between 45° and 90° provided accurate results with a small standard deviation and that all illumination conditions provided essentially identical results within the standard deviation. A statistically significant correlation was observed (Spearman correlation coefficient: 0.59, p = 0.008; Pearson correlation coefficient: 0.56, p = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.

Development and application of lateral flow strip with three test lines for detection of deoxynivalenol in wheat

Lateral flow strip was widely used and their qualitative and quantitative performance was in continuous improvement. However, the traditional strip was in a single-test-line format, which restricted operators to making a semi-quantitative judgment around a desired threshold concentration. Herein, a single strip with three test lines (TTLS) was developed for the semi-quantitative and quantitative determination of deoxynivalenol (DON). Four visual detection thresholds were obtained under optimized conditions and 35 wheat samples with DON content from 45 µg/kg to 2841 µg/kg were used to verify the method. The detection results were compared with that of the traditional strip and UPLC-MS/MS. In a three-test-line format, TTLS could reveal at least 200, 500, 1000, and 2000 µg/kg DON existed in different samples by the naked eye. The agreement analysis and statistical results indicated the new TTLS can be used as a useful tool for quantitative detection of DON with wide dynamic range.