Electrophoresis-assisted multilayer assembly of nanoparticles for sensitive lateral flow immunoassay
Bibliographic record
Abstract
Lateral flow immunoassay (LFIA) is a rapid, simple, and inexpensive method for point-of-need analysis. A major limitation of LFIA is a high limit of detection (LOD), which impacts its diagnostic sensitivity. To overcome this limitation, we introduce a signal-enhancement procedure that is performed after completing LFIA and involves controllably moving biotin- and streptavidin-functionalized gold nanoparticles along the test strip by electrophoresis. The nanoparticles link to immunocomplexes and each other forming multilayer aggregates on the test strip, thus, enhancing the signal. Here, we demonstrate lowering the LOD of hepatitis B surface antigen from approximately 8 to 0.12 ng/mL, making it clinically acceptable. Testing 76 clinical samples of serum and plasma for hepatitis B revealed that signal enhancement increased diagnostic sensitivity of LFIA from 72% to 98% while not affecting its 90% specificity. Electrophoresis-driven detection enhancement of LFIA is universal (antigen-independent), takes two minutes, and can be performed by an untrained person using an inexpensive accessory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".