Tandem SERS and MS/MS Profiling of Plasma Extracellular Vesicles for Early Ovarian Cancer Biomarker Discovery
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
Extracellular vesicles (EVs) are vectors of biomolecular cargo that play essential roles in intercellular communication across a range of cells. Protein, lipid, and nucleic acid cargo harbored within EVs may serve as biomarkers at all stages of disease; however, the choice of methodology may challenge the specificity and reproducibility of discovery. To address these challenges, the integration of rigorous EV purification methods, cutting-edge spectroscopic technologies, and data analysis are critical to uncover diagnostic signatures of disease. Herein, we demonstrate an EV isolation and analysis pipeline using surface-enhanced Raman spectroscopy (SERS) and mass spectrometry (MS) techniques on plasma samples obtained from umbilical cord blood, healthy donor (HD) plasma, and plasma from women with early stage high-grade serous carcinoma (HGSC). Plasma EVs were purified by size exclusion chromatography and analyzed by surface-enhanced Raman spectroscopy (SERS), mass spectrometry (MS), and atomic force microscopy. After determining the fraction of highest EV purity, SERS and MS were used to characterize EVs from HDs, pooled donors with noncancerous gynecological ailments ( n = 6), and donors with early stage [FIGO (I/II)] with HGSC. SERS spectra were subjected to different machine learning algorithms such as PCA, logistic regression, support vector machine, naïve Bayes, random forest, neural network, and k nearest neighbors to differentiate healthy, benign, and HGSC EVs. Collectively, we demonstrate a reproducible workflow with the potential to serve as a diagnostic platform for HGSC.
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