Proteoliposomes on 2D-MoS₂ plasmonic nanocavities for enhanced Raman spectroscopy with machine learning-based identification and classification
Bibliographic record
Abstract
Synthetic proteoliposomes functionalized with disease-relevant surface markers offer a powerful platform for modeling biological vesicles such as lipid nanoparticles and extracellular vesicles. The simplified composition of proteoliposomes facilitates the design and interpretation of analytic approaches for classifying vesicles and characterizing their contents. Here, we present a library of synthetic proteoliposomes incorporating tumor-associated surface biomarkers—EGFR, α6β4, and αvβ5—and nucleic acid cargo, to mimic cancer-derived extracellular vesicle phenotypes. For molecular fingerprinting, we employed a custom-designed 2D-plasmonic nanocavity platform that enables high-resolution, label-free Surface-Enhanced Raman Spectroscopy (SERS). Integrated with supervised machine learning algorithms, including Random Forest Classifier (RFC) and Support Vector Machine (SVM), this system achieved robust classification of proteoliposome subtypes with test accuracies of 82% and 76%, respectively. Our results demonstrate the power of combining synthetic vesicle engineering with advanced optical sensing for capturing subtle biomolecular differences. This platform enables standardized, interpretable diagnostic readouts and offers a versatile tool for probing molecular interactions in lipid-based systems such as virus-like particles and nanotherapeutics.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".