Mechanical signaling through the discoidin domain receptor 1 plays a central role in tissue fibrosis
Bibliographic record
Abstract
The preservation of tissue and organ architecture and function depends on tightly regulated interactions of cells with the extracellular matrix (ECM). These interactions are maintained in a dynamic equilibrium that balances intracellular, myosin-generated tension with extracellular resistance conferred by the mechanical properties of the extracellular matrix. Disturbances of this equilibrium can lead to the development of fibrotic lesions that are associated with a wide repertoire of high prevalence diseases including obstructive cardiovascular diseases, muscular dystrophy and cancer. Mechanotransduction is the process by which mechanical cues are converted into biochemical signals. At the core of mechanotransduction are sensory systems, which are frequently located at sites of cell-ECM and cell-cell contacts. As integrins (cell-ECM junctions) and cadherins (cell-cell contacts) have been extensively studied, we focus here on the properties of the discoidin domain receptor 1 (DDR1), a tyrosine kinase that mediates cell adhesion to collagen. DDR1 expression is positively associated with fibrotic lesions of heart, kidney, liver, lung and perivascular tissues. As the most common end-point of all fibrotic disorders is dysregulated collagen remodeling, we consider here the mechanical signaling functions of DDR1 in processing of fibrillar collagen that lead to tissue fibrosis.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".