A protease-initiated model of wound detection
Bibliographic record
Abstract
Abstract Wounds trigger surrounding cells to initiate repair, but it is unclear how cells detect wounds. The first known wound response of epithelial cells is a dramatic increase in cytosolic calcium, which occurs within seconds, but it is not known what initiates this calcium response. Specifically, is there an instructive signal detected by cells surrounding wounds? Here we identify a signal transduction pathway in epithelial cells initiated by the G-protein coupled receptor Methuselah-like 10 (Mthl10) activated around wounds by its cytokine ligands, Growth-blocking peptides (Gbps). Gbps are present in unwounded tissue in latent form, requiring proteolytic activation for signaling. Multiple protease families can activate Gbps, suggesting it acts as a detector to signal the presence of several proteases. We present experimental and computational evidence that proteases released during cell lysis serve as the instructive signal from wounds, liberating Gbp ligands to diffuse to the Mthl10 receptors on epithelial cells and activate downstream release of calcium. Thus, the presence of a nearby wound is signaled by the activation of a Gbp protease detector, sensitive to multiple proteases released after cellular damage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".