Thermal Contrast Amplification Reader Yielding 8-Fold Analytical Improvement for Disease Detection with Lateral Flow Assays
Bibliographic record
Abstract
There is an increasing need for highly sensitive and quantitative diagnostics at the point-of-care. The lateral flow immunoassay (LFA) is one of the most widely used point-of-care diagnostic tests; however, LFAs generally suffer from low sensitivity and lack of quantification. To overcome these limitations, thermal contrast amplification (TCA) is a new method that is based on the laser excitation of gold nanoparticles (GNPs), the most commonly used visual signature, to evoke a thermal signature. To facilitate the clinical translation of the TCA technology, we present the development of a TCA reader, a platform technology that significantly improves the limit of detection and provides quantification of disease antigens in LFAs. This TCA reader provides enhanced sensitivity over visual detection by the human eye or by a colorimetric reader (e.g., BD Veritor System Reader). More specifically, the TCA reader demonstrated up to an 8-fold enhanced analytical sensitivity and quantification among LFAs for influenza, malaria, and Clostridium difficile. Systematic characterization of the laser, infrared camera, and other components of the reader and their integration into a working reader instrument are described. The development of the TCA reader enables simple, highly sensitive quantification of LFAs at the point-of-care.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".