Sensitive sandwich ELISA based on a gold nanoparticle layer for cancer detection
Bibliographic record
Abstract
The availability of techniques for the sensitive detection of early stage cancer is crucial for patient survival. Our previous research (Langmuir, 2011, 27, 2155-2158) showed that gold nanoparticle layers (GNPL) used in indirect format ELISA amplified the signal, and gave a lower limit of detection (LOD) compared with commercial ELISA plates. However, due to its intrinsic limitations, indirect ELISA is not suitable for samples of complex composition, such as serum, plasma, etc., thus limiting the clinical performance of this kind of ELISA. In the work reported here, a GNPL-based sandwich format ELISA was developed, which showed superiority in terms of detection limit and sensitivity in the determination of rabbit IgG in buffer. More importantly, experiments using plasma spiked with carcinoembryonic antigen (CEA) as a representative biomarker showed that our GNPL-based ELISA assay amplified the signal and lowered the LOD compared to other assays, including commercialized CEA ELISA kits. This simple and cost-effective GNPL-based sandwich ELISA holds promise in clinical applications.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".