Immunoaffinity based methods are superior to kits for purification of prostate derived extracellular vesicles from plasma samples
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
BACKGROUND: The ability to isolate extracellular vesicles (EVs) such as exosomes or microparticles is an important method that is currently not standardized. While commercially available kits offer purification of EVs from biofluids, such purified EV samples will also contain non-EV entities such as soluble protein and nucleic acids that could confound subsequent experimentation. Ideally, only EVs would be isolated and no soluble protein would be present in the final EV preparation. METHODS: We compared commercially available EV isolation kits with immunoaffinity purification techniques and evaluated our final EV preparations using atomic force microscopy (AFM) and nanoscale flow cytometry (NFC). AFM is the only modality capable of detecting distinguishing soluble protein from EVs which is important for downstream proteomics approaches. NFC is the only technique capable of quantitating the proportion of target EVs to non-target EVs in the final EV preparation. RESULTS: To determine enrichment of prostate derived EVs relative to non-target MPs, anti-PSMA (Prostate Specific Membrane Antigen) antibodies were used in NFC. Antibody-based immunoaffinity purification generated the highest quality of prostate derived EV preparations due to the lack of protein and RNA present in the samples. All kits produced poor purity EV preparations that failed to deplete the sample of plasma protein. CONCLUSIONS: While attractive due to their ease of use, EV purification kits do not provide substantial improvements in isolation of EVs from biofluids such as plasma. Immunoaffinity approaches are more efficient and economical and will also eliminate a significant portion of plasma proteins which is necessary for downstream approaches.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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