Enhancement of sensitivity and detection limit of lateral flow immunoassays using lock-in thermography
Bibliographic record
Abstract
Lateral flow immunoassays (LFAs) have received much attention in recent years for detecting THC (a psychoactive ingredient of the cannabis plant) in oral fluids for point-of-care (POC) diagnostics. Specific advantages of screening oral fluids for THC include ease of sample collection in public and correlation of presence of THC in oral fluid with recent use of cannabis. However, despite their popularity, the detection limit of LFA is normally limited to greater than 25 ng/ml of THC in oral fluid which impedes the implementation of per se regulations in many jurisdictions (i.e., 1-5 ng/ml). To address this shortcoming, several LFA reader technologies have been developed in recent years but none of them have satisfied the required performance criteria of <80% sensitivity, specificity, and accuracy at per se limit, set by Driving Under the Influence of Drugs, Alcohol, and Medicines (DRUID). In this work, we explore Lock-In thermography (LIT) method for detecting THC in saliva-based LFA strips, utilizing thermal signatures of gold nanoparticles (GNPs) for interpretation of LFAs. Our results suggest that LIT enhances the limit of detection of the commercially available LFA by over an order of magnitude and promises an affordable solution that allows for proper enforcement of per se regulations worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".