Classification of single extracellular vesicles in a double nanohole optical tweezer for cancer detection
Bibliographic record
Abstract
Abstract A major challenge in cancer prognostics is finding early biomarkers that can accurately identify cancer. Circulating tumor cells are rare and circulating tumor DNA can not provide information about the originating cell. Extracellular vesicles (EVs) contain cell specific information, are abundant in fluids, and have unique properties between cancerous and non-cancerous. Fluorescence measurements have limitations from intrinsic fluorescent background signals, photobleaching, non-specific labelling, and EV structural modifications. Here, we demonstrate a label-free approach to classification of 3 different EVs, derived from non-malignant, non-invasive cancerous, and invasive cancerous cell lines. Using double nanohole optical tweezers, the scattering from single trapped EVs is measured, and using a 1D convolutional neural network, we are able to classify the time series optical signal into its respective EV class with greater than 90% accuracy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".