Integrins as Modulators of Transforming Growth Factor Beta Signaling in Dermal Fibroblasts During Skin Regeneration After Injury
Bibliographic record
Abstract
SIGNIFICANCE: Abnormal wound repair results from disorders in granulation tissue remodeling, and can lead to hypertrophic scarring and fibrosis. Excessive scarring can compromise tissue function and decrease tissue resistance to additional injuries. The development of potential therapies to minimize scarring is, thus, necessary to address an important clinical problem. RECENT ADVANCES: It has been clearly established that multiple cytokines and growth factors participate in the regulation of cutaneous wound healing. More recently, it has become apparent that these factors do not necessarily activate isolated signaling pathways. Rather, in some cases, there is cross-modulation of several cellular pathways involved in this process. Two of the key pathways that modulate each other during wound healing are activated by transforming growth factor-β and by extracellular matrix proteins acting through integrins. CRITICAL ISSUES: The pathogenesis of excessive scarring upon wound healing is not fully understood, as a result of the complexity of this process. However, the fact that many pathways combine to produce fibrosis provides multiple potential therapeutic targets. Some of them have been identified, such as focal adhesion kinase and integrin-linked kinase. Currently, a major challenge is to develop pharmacological inhibitors of these proteins with therapeutic value to promote efficient wound repair. FUTURE DIRECTIONS: The ability to better understand how different pathways crosstalk during wound repair and to identify and pharmacologically modulate key factors that contribute to the regulation of multiple wound-healing pathways could potentially provide effective therapeutic targets to decrease or prevent excessive scar formation and/or development of fibrosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".