Two-Color Lateral Flow Assay for Multiplex Detection of Causative Agents Behind Acute Febrile Illnesses
Bibliographic record
Abstract
Acute undifferentiated febrile illnesses (AFIs) represent a significant health burden worldwide. AFIs can be caused by infection with a number of different pathogens including dengue (DENV) and Chikungunya viruses (CHIKV), and their differential diagnosis is critical to the proper patient management. While rapid diagnostic tests (RDTs) for the detection of IgG/IgM against a single pathogen have played a significant role in enabling the rapid diagnosis in the point-of-care settings, the state-of-the-art assay scheme is incompatible with the multiplex detection of IgG/IgM to more than one pathogen. In this paper, we present a novel assay scheme that uses two-color latex labels for rapid multiplex detection of IgG/IgM. Adapting this assay scheme, we show that 4-plex detection of the IgG/IgM antibodies to DENV and CHIKV is possible in 10 min by using it to correctly identify 12 different diagnostic scenarios. We also show that blue, mixed, and red colorimetric signals corresponding to IgG, IgG/IgM, and IgM positive cases, respectively, can be associated with distinct ranges of hue intensities, which could be exploited by analyzer systems in the future for making accurate, automated diagnosis. This represents the first steps toward the development of a single RDT-based system for the differential diagnosis of numerous AFIs of interest.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".