Enrichment and ratiometric detection of circulating tumor cells using PSMA- and folate receptor-targeted magnetic and surface-enhanced Raman scattering nanoparticles
Bibliographic record
Abstract
The presence of circulating tumor cells (CTCs) in a patient’s bloodstream is a hallmark of metastatic cancer. The detection and analysis of CTCs is a promising diagnostic and prognostic strategy as they may carry useful genetic information from their derived primary tumor, and the enumeration of CTCs in the bloodstream has been known to scale with disease progression. However, the detection of CTCs is a highly challenging task owing to their sparse numbers in a background of billions of background blood cells. To effectively utilize CTCs, there is a need for an assay that can detect CTCs with high specificity and can locally enrich CTCs from a liquid biopsy. We demonstrate a versatile methodology that addresses these needs by utilizing a combination of nanoparticles. Enrichment is achieved using targeted magnetic nanoparticles and high specificity detection is achieved using a ratiometric detection approach utilizing multiplexed targeted and non-targeted surface-enhanced Raman Scattering Nanoparticles (SERS-NPs). We demonstrate this approach with model prostate and cervical circulating tumor cells and show the ex vivo utility of our methodology for the detection of PSMA or folate receptor over-expressing CTCs. Our approach allows for the mitigation of interference caused by the non-specific uptake of nanoparticles by other cells present in the bloodstream and our results from magnetically trapped CTCs reveal over a 2000% increase in targeted SERS-NP signal over non-specifically bound SERS-NPs.
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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) | 0.000 | 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 | 0.001 | 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".