Circulating cancer cell detection using an optical fiber aptasensor
Bibliographic record
Abstract
The detection of circulating tumor cells (CTCs) represents an important goal in oncological diagnosis and treatment, as CTCs are responsible for metastasis in several forms of cancer and are present at very low concentration. Their detection should occur at around 1-10 cells/mL of blood for diagnosis purpose. In this work, we propose an all-fiber plasmonic aptasensor featuring multiple narrowband resonances in the near-infrared wavelength range to detect metastatic breast cancer cells. To this aim, specific aptamers against mammaglobin-A proteins were selected and immobilized as bioreceptors on the optical fiber surface. In vitro assays confirm that label-free and real-time detection of cancer cells (LOD of 49 cells/mL) occurs within 5 minutes, while the additional use of functionalized gold nanoparticles allows a two-fold amplification of the biosensor response. Differential measurements on selected optical resonances were used to process the sensor response and results were confirmed by microscopy analysis. The detection of only 10 cancer cells/mL was performed with relevant specificity against non-target cells with comparable sizes and shapes.
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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) | 0.000 | 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".