Impact of Vimentin on Regulation of Cell Signaling and Matrix Remodeling
Bibliographic record
Abstract
Vimentin expression contributes to cellular mechanoprotection and is a widely recognized marker of fibroblasts and of epithelial-mesenchymal transition. But it is not understood how vimentin affects signaling that controls cell migration and extracellular matrix (ECM) remodeling. Recent data indicate that vimentin controls collagen deposition and ECM structure by regulating contractile force application to the ECM and through post-transcriptional regulation of ECM related genes. Binding of cells to the ECM promotes the association of vimentin with cytoplasmic domains of adhesion receptors such as integrins. After initial adhesion, cell-generated, myosin-dependent forces and signals that impact vimentin structure can affect cell migration. Post-translational modifications of vimentin determine its adaptor functions, including binding to cell adhesion proteins like paxillin and talin. Accordingly, vimentin regulates the growth, maturation and adhesive strength of integrin-dependent adhesions, which enables cells to tune their attachment to collagen, regulate the formation of cell extensions and control cell migration through connective tissues. Thus, vimentin tunes signaling cascades that regulate cell migration and ECM remodeling. Here we consider how specific properties of vimentin serve to control cell attachment to the underlying ECM and to regulate mesenchymal cell migration and remodeling of the ECM by resident fibroblasts.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".