Electrophoresis‐Assisted Multilayer Assembly of Nanoparticles for Sensitive Lateral Flow Immunoassay**
Bibliographic record
Abstract
Abstract Lateral flow immunoassay (LFIA) is a rapid, simple, and inexpensive point‐of‐need method. 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 by electrophoresis. The nanoparticles link to immunocomplexes 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 −1 , making it clinically acceptable. Testing 118 clinical samples for hepatitis B showed that signal enhancement increased the diagnostic sensitivity of LFIA from 73 % to 98 % while not affecting its 95 % specificity. Electrophoresis‐driven enhancement of LFIA is universal (antigen‐independent), takes two minutes, and can be performed by an untrained person.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".