T-Lymphocytes Enable Osteoblast Maturation via IL-17F during the Early Phase of Fracture Repair
Bibliographic record
Abstract
While it is well known that the presence of lymphocytes and cytokines are important for fracture healing, the exact role of the various cytokines expressed by cells of the immune system on osteoblast biology remains unclear. To study the role of inflammatory cytokines in fracture repair, we studied tibial bone healing in wild-type and Rag1(-/-) mice. Histological analysis, µCT stereology, biomechanical testing, calcein staining and quantitative RNA gene expression studies were performed on healing tibial fractures. These data provide support for Rag1(-/-) mice as a model of impaired fracture healing compared to wild-type. Moreover, the pro-inflammatory cytokine, IL-17F, was found to be a key mediator in the cellular response of the immune system in osteogenesis. In vitro studies showed that IL-17F alone stimulated osteoblast maturation. We propose a model in which the Th17 subset of T-lymphocytes produces IL-17F to stimulate bone healing. This is a pivotal link in advancing our current understanding of the molecular and cellular basis of fracture healing, which in turn may aid in optimizing fracture management and in the treatment of impaired bone healing.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".