RSC Adv. 2025 Nov 7;15(51):43322-43333. doi: 10.1039/d5ra05719a. eCollection 2025 Nov 6.
ABSTRACT
Point-of-care (PoC) detection of antibodies in blood enables rapid, on-site diagnosis. However, these devices often face challenges related to user variability due to the requirement of multiple manual operations. To address this issue, we designed and developed a disposable microfluidic device that requires minimal user input for rapid detection of SARS-CoV-2 antibodies (ABs) in total blood and antigens associated with blood types. Here, we present a passive pressure-driven pumping technique that rapidly mixes blood samples with reagents, delivering results within three minutes. The device requires 15 μL of capillary blood and can detect SARS-CoV-2 ABs across a concentration range of 0 to 60 μg mL-1. Additionally, we demonstrated the versatility of the microfluidic device by implementing blood typing functionality, highlighting its potential for broader serological testing applications. We also developed a support vector machine (SVM) algorithm as a proof-of-concept to demonstrate the potential application of machine learning (ML)-based analysis to complement visual interpretation of results. We evaluated the performance and predictive accuracy of the SVM model and compared it to human interpretations. The analysis showed that the SVM model achieved a statistically significant improvement in predicting varying degrees of agglutination when compared to human interpretation. This device addresses the need for a user-friendly, rapid COVID-19 AB testing solution and blood-typing assay and also provides a model for the future development of diagnostic devices that are integrated with ML models for improved diagnostic accuracy and accessibility in both clinical and non-clinical environments.
PMID:41209517 | PMC:PMC12593424 | DOI:10.1039/d5ra05719a
