In‐depth proteomic analyses of exosomes isolated from expressed prostatic secretions in urine
Bibliographic record
Abstract
Expressed prostatic secretions (EPS) are proximal fluids of the prostate that are increasingly being utilized as a clinical source for diagnostic and prognostic assays for prostate cancer (PCa). These fluids contain an abundant amount of microvesicles reflecting the secretory function of the prostate gland, and their protein composition remains poorly defined in relation to PCa. Using expressed prostatic secretions in urine (EPS-urine), exosome preparations were characterized by a shotgun proteomics procedure. In pooled EPS-urine exosome samples, ~900 proteins were detected. Many of these have not been previously observed in the soluble proteome of EPS generated by our labs or other related exosome proteomes. We performed systematic comparisons of our data against previously published, prostate-related proteomes, and global annotation analyses to highlight functional processes within the proteome of EPS-urine derived exosomes. The acquired proteomic data have been deposited to the Tranche repository and will lay the foundation for more extensive investigations of PCa derived exosomes in the context of biomarker discovery and cancer biology.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".