Accurate label free classification of cancerous extracellular vesicles using nanoaperture optical tweezers and deep learning
Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) are easily accessible in biological fluids and carry the molecular “fingerprints” of their parent cells, making them compelling candidates for minimally invasive cancer diagnostics. For future translation into clinical settings, greater sensitivity and specificity are desired. Here, we achieve near-perfect classification for cancerous (2 types) and non-cancerous cell-derived EVs by using nanoaperture optical tweezers (NOTs) and deep learning. The NOT approach is label-free and has single-EV sensitivity – the signal acquired is simply the laser tweezer scattered light. The high level of specificity is achieved by the custom design of a four-layer convolution and Kolmogorov–Arnold linear layer deep learning model. Several other models are compared with this approach. Beyond diagnostics, this platform opens avenues for real-time EV profiling, deepening our biological understanding and advancing the development of minimally invasive personalized medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".