Fibulin2: a negative regulator of BMSC osteogenic differentiation in infected bone fracture healing
Bibliographic record
Abstract
Bone fracture remains a common occurrence, with a population-weighted incidence of approximately 3.21 per 1000. In addition, approximately 2% to 50% of patients with skeletal fractures will develop an infection, one of the causes of disordered bone healing. Dysfunction of bone marrow mesenchymal stem cells (BMSCs) plays a key role in disordered bone repair. However, the specific mechanisms underlying BMSC dysfunction caused by bone infection are largely unknown. In this study, we discovered that Fibulin2 expression was upregulated in infected bone tissues and that BMSCs were the source of infection-induced Fibulin2. Importantly, Fibulin2 knockout accelerated mineralized bone formation during skeletal development and inhibited inflammatory bone resorption. We demonstrated that Fibulin2 suppressed BMSC osteogenic differentiation by binding to Notch2 and inactivating the Notch2 signaling pathway. Moreover, Fibulin2 knockdown restored Notch2 pathway activation and promoted BMSC osteogenesis; these outcomes were abolished by DAPT, a Notch inhibitor. Furthermore, transplanted Fibulin2 knockdown BMSCs displayed better bone repair potential in vivo. Altogether, Fibulin2 is a negative regulator of BMSC osteogenic differentiation that inhibits osteogenesis by inactivating the Notch2 signaling pathway in infected bone.
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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) | 0.001 | 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) | 0.001 | 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 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".