Transforming growth factor-β in stem cells and tissue homeostasis
Bibliographic record
Abstract
TGF-β 1-3 are unique multi-functional growth factors that are only expressed in mammals, and mainly secreted and stored as a latent complex in the extracellular matrix (ECM). The biological functions of TGF-β in adults can only be delivered after ligand activation, mostly in response to environmental perturbations. Although involved in multiple biological and pathological processes of the human body, the exact roles of TGF-β in maintaining stem cells and tissue homeostasis have not been well-documented until recent advances, which delineate their functions in a given context. Our recent findings, along with data reported by others, have clearly shown that temporal and spatial activation of TGF-β is involved in the recruitment of stem/progenitor cell participation in tissue regeneration/remodeling process, whereas sustained abnormalities in TGF-β ligand activation, regardless of genetic or environmental origin, will inevitably disrupt the normal physiology and lead to pathobiology of major diseases. Modulation of TGF-β signaling with different approaches has proven effective pre-clinically in the treatment of multiple pathologies such as sclerosis/fibrosis, tumor metastasis, osteoarthritis, and immune disorders. Thus, further elucidation of the mechanisms by which TGF-β is activated in different tissues/organs and how targeted cells respond in a context-dependent way can likely be translated with clinical benefits in the management of a broad range of diseases with the involvement of TGF-β.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".