Bidirectionally Favorable Platform: A Dual-Targeting Probe-Encoded Maple Leaf-Type Fluorescent Lateral Flow Immunoassay for Multiple Biomarker Detection
Bibliographic record
Abstract
In the traditional multiplexed lateral flow immunoassay (LFIA), different detection probes against different targets are necessary. However, the relative complexity and high cost of probe preparation, as well as the insufficient user-friendliness, limit the application of the multiplexed LFIA in disease diagnosis. Here, we reported a bidirectionally favorable LFIA (BDF-LFIA) platform to maximize convenience for both manufacturers and users. Red-emitting time-resolved fluorescent nanoparticles were coated with different antibodies to recognize multiple targets simultaneously, which greatly simplified probe preparation by the manufacturers. Ultrabright green-emitting gold nanoclusters were pre-embedded on the test line as a reference signal to achieve a target concentration-dependent maple leaf-type hue readout from green to yellow to red, which was quite user-friendly. Taking cancer biomarkers alpha-fetoprotein and carcinoembryonic antigen as examples, this assay achieved a visual detection limit of 2 ng/mL. Compared with the conventional fluorescent LFIA, the BDF-LFIA could generate a more discernible signal around the threshold concentration of the targets. Moreover, the assay successfully diagnosed 54 clinical samples. Overall, the BDF-LFIA showed bidirectional benefits for both manufacturers and users and provided a new concept for the LFIA in multiplexed detection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".