In-Depth Proteomic Analyses of Direct Expressed Prostatic Secretions
Bibliographic record
Abstract
It is expected that clinically obtainable fluids that are proximal to organs contain a repertoire of secreted proteins and shed cells reflective of the physiological state of that tissue and thus represent potential sources for biomarker discovery, investigation of tissue-specific biology, and assay development. The prostate gland secretes many proteins into a prostatic fluid that combines with seminal vesicle fluids to promote sperm activation and function. Proximal fluids of the prostate that can be collected clinically are seminal plasma and expressed prostatic secretion (EPS) fluids. In the current study, MudPIT-based proteomics was applied to EPS obtained from nine men with prostate cancer and resulted in the confident identification of 916 unique proteins. Systematic bioinformatics analyses using publicly available microarray data of 21 human tissues (Human Gene Atlas), the Human Protein Atlas database, and other published proteomics data of shed/secreted proteins were performed to systematically analyze this comprehensive proteome. Therefore, we believe this data will be a valuable resource for the research community to study prostate biology and potentially assist in the identification of novel prostate cancer biomarkers. To further streamline this process, the entire data set was deposited to the Tranche repository for use by other researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".