Isolation of Extracellular Vesicles from Minimal Volume Ascites Fluid Using Strong Anion Exchange Beads
Bibliographic record
Abstract
Ovarian cancer (OC) remains a leading cause of gynecologic cancer mortality due to late-stage diagnosis and limited early detection strategies. Ascites fluid, a pathological hallmark of OC, is a rich source of tumor-derived extracellular vesicles (EVs) that reflect the tumor microenvironment and hold promise for biomarker discovery. However, isolating EVs from minimal ascites volumes (<100 µL) poses technical challenges using conventional methods like ultracentrifugation or size-exclusion chromatography (SEC). This study explores the application of strong anion exchange (SAX) magnetic beads (Mag-Net) for efficient EV isolation from as little as 2 µL of ascites fluid from both murine models and a human patient with mucinous borderline tumor. We demonstrate that SAX achieves robust EV capture at 10µl of input volume, enabling comprehensive proteomic profiling and single-EV surface-enhanced Raman spectroscopy (SERS) with a >2-fold increase in proteomic depth compared to raw ascites. Notably, this study was able to identify 1000 proteins not previously annotated in Vesiclepedia for OC-derived EVs, alongside distinct SERS signatures, highlighting the potential for multiomic analysis. Comparative analysis with UC revealed enhanced proteomic depth obtained with SAX beads, albeit we also observed differential detection of canonical markers (e.g., CD9, CD81) between input volumes of ascites fluid. These findings establish SAX as a scalable, low-input platform for EV-based biomarker discovery, paving the way for improved early detection and molecular insights into OC progression.
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.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".