Cytopathology diagnosis by multiplexed plasmonic biomarkers (Conference Presentation)
Bibliographic record
Abstract
We present the development of a cost-effective, sensitive and specific diagnostic methodology to improve the reliability of the cytopathology diagnosis. The methodology will be based on a new cytology protocol where immunolabeling is performed on fresh cells before fixation using spectrally distinctive plasmonic NPs conjugated with antibodies as optical biomarkers. Metallic NPs, typically gold, silver and Au/Ag alloys, are widely used due to their unique plasmonic properties, photo-stability, water solubility and biocompatibility for in vitro and in vivo biomedical applications. The very distinctive NPs chromatic signature depends on their composition, size, and geometry and provides excellent opportunities for a reliable multicolor imaging and multiplexed immunolabeling. The presented methodology includes a new multispectral and hyperspectral dark-field microscopy for reliable multiplexed and quantitative immunoplasmonic markers optical detection. We applied two optical encoding strategies of immunoplasmonic microscopy (IPM) for immunoplasmonic NPs detection in the NPs-cells complex. The first method is based on reflected light microscopy mode (Patskovsky, S. et al J Biophotonics 8 (5), 401-407 (2015))combined with compact hyperspectral scanning source. It provides spectral differentiation, precise spatial localization and multiplexed quantification of NPs labels. The second approach uses a multispectral side-illumination dark-field microscopy that allows to design a compact module for optical imaging and spectroscopic identification of individual plasmonic NPs in fixed or live cell preparations. The presented approach can provide a convenient and routine method for immunoplasmonic markers visualization by the pathologist. It can be easily adaptable to the microscopes currently used in the clinical setting thus facilitating and accelerating its adoption.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".