Prostasomes: a role in prostatic disease?
Bibliographic record
Abstract
Prostasomes are found in high concentrations in seminal plasma.Under electron microscopy there appear to be two distinct morphological types: smaller, 'dark' prostasomes with tightly packed electron-dense contents, and larger 'light' less dense structures (Fig. 1).Their diameter is 40-500 nm [1].They have a lipid bilayer membrane which may be arranged in a multilamellar fashion, and has a characteristically high concentration of cholesterol.Consequently, prostasomes have a membrane which is less permeable to small water-soluble molecules.Analogous structures are also shed by prostate cell lines into culture media [2]. PREPARATION OF PROSTASOMESProstasomes can be prepared from both in-vitro and in-vivo sources, using semen, prostatic tissue (benign, primary and secondary malignancy) or prostate cancer cell lines.Although the finer details of extraction will depend upon the source, the protocols used to isolate prostasomes contain the following elements:• Removal of cells from semen by low-speed centrifugation.• Ultracentrifugation of seminal plasma, e.g. 100 000 g for 2 h.• Re-suspension of the prostasome pellet, typically in isotonic Tris-HCl buffer.• Gel chromatography, e.g. using a 'superdex' column, to purify the re-suspended prostasome containing fractions.Neuroendocrine components, e.g.neuropeptide Y, chromogranin A and B, and vasoactive intestinal peptide, have also been detected in prostasomes by radioimmunoassay.Skibinsky et al. [10] identified the secretory granule protein
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".