Designable nanoplasmonic biomarkers for direct microscopy cytopathology diagnostics
Bibliographic record
Abstract
Direct microscopy interpretation of fine-needle biopsy cytological samples is routinely used by practicing cytopathologists. Adding possibility to identify selective and multiplexed biomarkers on the same samples and with the same microscopy technique can greatly improve diagnostic accuracy. In this article, we propose to use biomarkers based on designable plasmonic nanoparticles (NPs) with unique optical properties and excellent chemical stability that can satisfy the above-mentioned requirements. By finely controlling the size and composition of gold-silver alloy NPs and gold nanorods, the NPs plasmonic resonance properties, such as scattering efficiency and resonance peak spectral position, are adjusted in order to provide reliable identification and chromatic differentiation by conventional direct microscopy. Efficient darkfield NPs imaging is performed by using a novel circular side illumination adaptor that can be easily integrated into any microscopy setup while preserving standard cytopathology visualization method. The efficiency of the proposed technology for fast visual detection and differentiation of three spectrally distinct NP-markers is demonstrated in different working media, thus confirming the potential application in conventional cytology preparations. It is worth emphasizing that the presented technology does not interfere with standard visualization with immunohistochemical staining, but should rather be considered as a second imaging modality to confirm the 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".