Critical Role of Transforming Growth Factor Beta in Different Phases of Wound Healing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SIGNIFICANCE: This review highlights the critical role of transforming growth factor beta (TGF-β)1-3 within different phases of wound healing, in particular, late-stage wound healing. It is also very important to identify the TGF-β1-controlling factors involved in slowing down the healing process upon wound epithelialization. RECENT ADVANCES: TGF-β1, as a growth factor, is a known proponent of dermal fibrosis. Several strategies to modulate or regulate TGF's actions have been thoroughly investigated in an effort to create successful therapies. This study reviews current discourse regarding the many roles of TGF-β1 in wound healing by modulating infiltrated immune cells and the extracellular matrix. CRITICAL ISSUES: It is well established that TGF-β1 functions as a wound-healing promoting factor, and thereby if in excess it may lead to overhealing outcomes, such as hypertrophic scarring and keloid. Thus, the regulation of TGF-β1 in the later stages of the healing process remains as critical issue of which to better understand. FUTURE DIRECTIONS: One hypothesis is that cell communication is the key to regulate later stages of wound healing. To elucidate the role of keratinocyte/fibroblast cross talk in controlling the later stages of wound healing we need to: (1) identify those keratinocyte-released factors which would function as wound-healing stop signals, (2) evaluate the functionality of these factors in controlling the outcome of the healing process, and (3) formulate topical vehicles for these antifibrogenic factors to improve or even prevent the development of hypertrophic scarring and keloids as a result of deep trauma, burn injuries, and any type of surgical incision.
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.
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) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it