Post-Assay Chemical Enhancement for Highly Sensitive Lateral Flow Immunoassays: A Critical Review
Bibliographic record
Abstract
Lateral flow immunoassay (LFIA) has found a broad application for testing in point-of-care (POC) settings. LFIA is performed using test strips-fully integrated multimembrane assemblies containing all reagents for assay performance. Migration of liquid sample along the test strip initiates the formation of labeled immunocomplexes, which are detected visually or instrumentally. The tradeoff of LFIA's rapidity and user-friendliness is its relatively low sensitivity (high limit of detection), which restricts its applicability for detecting low-abundant targets. An increase in LFIA's sensitivity has attracted many efforts and is often considered one of the primary directions in developing immunochemical POC assays. Post-assay enhancements based on chemical reactions facilitate high sensitivity. In this critical review, we explain the performance of post-assay chemical enhancements, discuss their advantages, limitations, compared limit of detection (LOD) improvements, and required time for the enhancement procedures. We raise concerns about the performance of enhanced LFIA and discuss the bottlenecks in the existing experiments. Finally, we suggest the experimental workflow for step-by-step development and validation of enhanced LFIA. This review summarizes the state-of-art of LFIA with chemical enhancement, offers ways to overcome existing limitations, and discusses future outlooks for highly sensitive testing in POC conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".