Proteoliposomes on 2D-MoS₂ plasmonic nanocavities for enhanced Raman spectroscopy with machine learning-based identification and classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it