Essential growth factor receptors for fibroblast homeostasis and activation: Fibroblast Growth Factor Receptor (FGFR), Platelet Derived Growth Factor Receptor (PDGFR), and Transforming Growth Factor β Receptor (TGFβR)
Bibliographic record
Abstract
Fibroblasts are cells of mesenchymal origin that are found throughout the body. While these cells have several functions, their integral roles include maintaining tissue architecture through the production of key extracellular matrix components, and participation in wound healing after injury. Fibroblasts are also key mediators in disease progression during fibrosis, cancer, and other inflammatory diseases. Under these perturbed states, fibroblasts can activate into inflammatory fibroblasts or contractile myofibroblasts. Fibroblasts require various growth factors and mitogenic molecules for survival, proliferation, and differentiation. While the activity of mitogenic growth factors on fibroblasts in vitro was characterized as early as the 1970s, the proliferation and differentiation effects of growth factors on these cells in vivo are unclear. Recent work exploring the heterogeneity of fibroblasts raises questions as to whether all fibroblast cell states exhibit the same growth factor requirements. Here, we will examine and review existing studies on the influence of fibroblast growth factor receptors (FGFRs), platelet-derived growth factor receptors (PDGFRs), and transforming growth factor β receptor (TGFβR) on fibroblast cell states.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".