Influence of the TGF-β Superfamily on Osteoclasts/Osteoblasts Balance in Physiological and Pathological Bone Conditions
Bibliographic record
Abstract
The balance between bone forming cells (osteoblasts/osteocytes) and bone resorbing cells (osteoclasts) plays a crucial role in tissue homeostasis and bone repair. Several hormones, cytokines, and growth factors-in particular the members of the TGF-β superfamily such as the bone morphogenetic proteins-not only regulate the proliferation, differentiation, and functioning of these cells, but also coordinate the communication between them to ensure an appropriate response. Therefore, this review focuses on TGF-β superfamily and its influence on bone formation and repair, through the regulation of osteoclastogenesis, osteogenic differentiation of stem cells, and osteoblasts/osteoclasts balance. After introducing the main types of bone cells, their differentiation and cooperation during bone remodeling and fracture healing processes are discussed. Then, the TGF-β superfamily, its signaling via canonical and non-canonical pathways, as well as its regulation by Wnt/Notch or microRNAs are described and discussed. Its important role in bone homeostasis, repair, or disease is also highlighted. Finally, the clinical therapeutic uses of members of the TGF-β superfamily and their associated complications are debated.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".