Proteinase-Activated Receptor-1 and Immunomodulatory Effects of a PAR1-Activating Peptide in a Mouse Model of Prostatitis
Bibliographic record
Abstract
BACKGROUND: Nonbacterial prostatitis has no established etiology. We hypothesized that proteinase-activated receptor-1 (PAR1) can play a role in prostatitis. We therefore investigated the effects of PAR1 stimulation in the context of a new model of murine nonbacterial prostatitis. METHODS: Using a hapten (ethanol-dinitrobenzene sulfonic acid- (DNBS-)) induced prostatitis model with both wild-type and PAR1-null mice, we examined (1) the location of PAR1 in the mouse prostate and (2) the impact of a PAR1-activating peptide (TFLLR-NH2: PAR1-TF) on ethanol-DNBS-induced inflammation. RESULTS: Ethanol-DNBS-induced inflammation was maximal at 2 days. In the tissue, PAR1 was expressed predominantly along the apical acini of prostatic epithelium. Although PAR1-TF on its own did not cause inflammation, its coadministration with ethanol-DNBS reduced all indices of acute prostatitis. Further, PAR1-TF administration doubled the prostatic production of interleukin-10 (IL-10) compared with ethanol-DNBS treatment alone. This enhanced IL-10 was not observed in PAR1-null mice and was not caused by the reverse-sequence receptor-inactive peptide, RLLFT-NH2. Surprisingly, PAR1-TF, also diminished ethanol-DNBS-induced inflammation in PAR1-null mice. CONCLUSIONS: PAR1 is expressed in the mouse prostate and its activation by PAR1-TF elicits immunomodulatory effects during ethanol-DNBS-induced prostatitis. However, PAR1-TF also diminishes ethanol-DNBS-induced inflammation via a non-PAR1 mechanism by activating an as-yet unknown receptor.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".