Characterization of Extracellular vesicles isolated from different Liquid biopsies of uveal melanoma patients
Bibliographic record
Abstract
Purpose: Uveal melanoma (UM) is the most common intraocular malignant tumor in adults. Extracellular vesicles (EVs) have been extensively studied as a biomarker to monitor disease in patients. The study of new biomarkers in melanoma patients could prevent metastasis by earlier diagnosis. In this study, we determined the proteomic profile of EVs isolated from aqueous humor (AH), vitreous humor (VH), and plasma from UM patients in comparison with cancer-free control patients. Methods: AH, VH and plasma were collected from seven patients with UM after enucleation; AH and plasma were collected from seven cancer-free patients with cataract (CAT; control group). EVs were isolated using the membrane-based affinity binding column method. Nanoparticle tracking analysis (NTA) was performed to determine the size and concentration of EVs. EV markers, CD63 and TSG101, were assessed by immunoblotting, and the EV proteome was characterized by mass spectrometry. Results: Mean EV concentration was higher in all analytes of UM patients compared to those in the CAT group. In the UM cohort, the mean concentration of EVs was significantly lower in AH and plasma than in VH. In contrast, the mean size and size distribution of EVs was invariably identical in all analyzed analytes and in both studied groups (UM vs. CAT). Mass spectrometry analyses from the different analytes from UM patients showed the presence of EV markers. Conclusion: EVs isolated from AH, VH, and plasma from patients with UM showed consistent profiles and support the use of blood to monitor UM patients as a noninvasive liquid biopsy.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".