Nanotrap-AI Integration Enables Ultra-Sensitive Point-of-Care HIV Testing

  • Park, Jeong Soo; 
  • Lee, Seungmin; 
  • Woo, Hyowon; 
  • Hong, Ji Hye; 
  • Yoon, Dae Sung; 
  • ... Chung, Seok; 
  • 외 1명
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초록

Early HIV detection using noninvasive samples remains challenging because oral fluid contains extremely low antibody levels and enzymatic inhibitors that limit the sensitivity of lateral-flow assays (LFAs). We introduce BE-SMART-HIV, a diagnostic platform that integrates a BEETLES2-inspired bioengineered enrichment (BE) nanotrap with a smartphone-based deep-learning reader (SMART). The BE nanotrap concentrates antibodies by similar to 20-fold while removing salivary inhibitors, enabling oral-fluid-equivalent samples to become detectable on commercial LFAs. A transfer-learning-refined AI model interprets weak test lines with 98.6% accuracy, outperforming untrained users and clinicians. In a longitudinal seroconversion panel, BE-SMART-HIV detected antibody emergence up to 8 days earlier than conventional LFAs and reproduced ELISA-like temporal patterns, including IgM onset and IgG maturation, even from samples diluted 1,000-fold. These findings demonstrate that enriched oral-fluid-level specimens can capture systemic antibody kinetics, establishing a practical route to early, noninvasive, high-fidelity HIV screening for repeated testing in resource-limited settings.

키워드

deep learning; nanotrap; sample preparation; LFA; HIV; TO-CHILD TRANSMISSION; INFECTION
제목
Nanotrap-AI Integration Enables Ultra-Sensitive Point-of-Care HIV Testing
저자
Park, Jeong Soo; Lee, Seungmin; Woo, Hyowon; Hong, Ji Hye; Yoon, Dae Sung; Chung, Seok; Lee, Jeong Hoon
DOI
10.1021/acsnano.6c01808
발행일
2026-04-16
유형
Article
저널명
ACS Nano
권
20
호
16
페이지
12639 ~ 12650