Development of a SERS-Based Rapid Vertical Flow Assay for Point-of-Care Diagnostics
Bibliographic record
Abstract
Point-of-care (POC) diagnostic testing platforms are a growing sector of the healthcare industry as they offer the advantages of rapid provision of results, ease of use, reduced cost, and the ability to link patients to care. While many POC tests are based on chromatographic flow assay technology, this technology suffers from a lack of sensitivity along with limited capacity for multiplexing and quantitative analysis. Several recent reports have begun to investigate the feasibility of coupling chromatographic flow platforms to more advanced read-out technologies which in turn enable on-site acquisition, storage, and transmission of important healthcare metrics. One such technology being explored is surface-enhanced Raman spectroscopy or SERS. In this work, SERS is coupled for the first time to a rapid vertical flow (RVF) immunotechnology for detection of anti-HCV antibodies in an effort to extend the capabilities of this commercially available diagnostic platform. High-quality and reproducible SERS spectra were obtained using reporter-modified gold nanoparticles (AuNPs). Serial dilution studies indicate that the coupling of SERS with RVF technology shows enormous potential for next-generation POC diagnostics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".