Bibliographic record
Abstract
Significance: Regulation of cell adhesions during tissue repair is fundamentally important for cell migration, proliferation, and protein production. All cells interact with extracellular matrix proteins with cell surface integrin receptors that convey signals from the environment into the nucleus, regulating gene expression and cell behavior. Integrins also interact with a variety of other proteins, such as growth factors, their receptors, and proteolytic enzymes. Re-epithelialization and granulation tissue formation are crucially dependent on the temporospatial function of multiple integrins. This review explains how integrins function in wound repair. Recent Advances: Certain integrins can activate latent transforming growth factor beta-1 (TGF-β1) that modulates wound inflammation and granulation tissue formation. Dysregulation of TGF-β1 function is associated with scarring and fibrotic disorders. Therefore, these integrins represent targets for therapeutic intervention in fibrosis. Critical Issues: Integrins have multifaceted functions and extensive crosstalk with other cell surface receptors and molecules. Moreover, in aberrant healing, integrins may assume different functions, further increasing the complexity of their functionality. Discovering and understanding the role that integrins play in wound healing provides an opportunity to identify the mechanisms for medical conditions, such as excessive scarring, chronic wounds, and even cancer. Future Directions: Integrin functions in acute and chronic wounds should be further addressed in models better mimicking human wounds. Application of any products in acute or chronic wounds will potentially alter integrin functions that need to be carefully considered in the design.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".