Microenvironment proteinases, proteinase-activated receptor regulation, cancer and inflammation
Bibliographic record
Abstract
We propose that in the microenvironment of inflammatory tissues, including tumours, extracellular proteinases can modulate cell signalling in part by regulating proteinase-activated receptors (PARs). We have been exploring this mechanism in a variety of inflammation and tumour-related settings that include tumour-derived cultured cells from prostate and bladder cancer, as well as immune inflammatory cells that are involved in the pathology of inflammatory diseases including multiple sclerosis. Our work showed that proteinase signalling via the PARs affects prostate and bladder cancer-derived tumour cell behaviour and can regulate calcium signalling in human T-cell and macrophage-related inflammatory cells as well as in murine splenocytes. Further, we found that the tumour-derived prostate cancer cells and immune-related cells (Jurkat, THP1, mouse splenocytes) can produce PAR-regulating proteinases (including kallikreins: kallikrein-related peptidases), that can control tissue function by both a paracrine and autocrine mechanism. We suggest that this PAR-driven signalling process involving secreted microenvironment proteinases can play a key role in cancer and inflammatory diseases including multiple sclerosis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".