Multiplexed Plasmonic Nano-Labeling for Bioimaging of Cytological Stained Samples
Bibliographic record
Abstract
Reliable cytopathological diagnosis requires new methods and approaches for the rapid and accurate determination of all cell types. This is especially important when the number of cells is limited, such as in the cytological samples of fine-needle biopsy. Immunoplasmonic-multiplexed- labeling may be one of the emerging solutions to such problems. However, to be accepted and used by the practicing pathologists, new methods must be compatible and complementary with existing cytopathology approaches where counterstaining is central to the correct interpretation of immunolabeling. In addition, the optical detection and imaging setup for immunoplasmonic-multiplexed-labeling must be implemented on the same cytopathological microscope, not interfere with standard H&E imaging, and operate as a second easy-to-use imaging method. In this article, we present multiplex imaging of four types of nanoplasmonic markers on two types of H&E-stained cytological specimens (formalin-fixed paraffin embedded and non-embedded adherent cancer cells) using a specially designed adapter for SI dark-field microscopy. The obtained results confirm the effectiveness of the proposed optical method for quantitative and multiplex identification of various plasmonic NPs, and the possibility of using immunoplasmonic-multiplexed-labeling for cytopathological 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".