Extracellular vesicles from biological fluids as potential biomarkers for prostate cancer
Bibliographic record
Abstract
Abstract Purpose Despite the high incidence of false positives, prostate specific antigen (PSA) screening remains a widely used diagnostic test for prostate cancer, driving an urgent need for the discovery of better biomarkers for early prostate cancer detection. Extracellular vesicles (EV) secreted from cancer cells are present in various biological fluids, carrying distinctly different cellular components compared to normal cells, and have great potential to be used as a source of biomarkers. Methods EV from serum and urine of healthy men and prostate cancer patients were isolated, and characterised by transmission electron microscopy, particle size analysis, and western blot. Proteomic and cholesterol liquid chromatography-mass spectrometry (LC-MS) analyses were conducted. Results There was a successful enrichment of exosomes isolated from serum and urine. EV derived from biological fluids of prostate cancer patients had significant differences in composition when compared with those from healthy controls. Analysis of matched serum and urine samples from six prostate cancer patients revealed specific EV proteins common in both types of biological fluid for each patient. Conclusion Some of the EV proteins identified from our analyses have potential to be used as prostate cancer biomarkers, either to depict cancer progression or for disease diagnosis through non-invasive testing.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".