Overexpression of Bmi1 in Lymphocytes Stimulates Skeletogenesis by Improving the Osteogenic Microenvironment
Bibliographic record
Abstract
To investigate whether overexpression of Bmi1 in lymphocytes can stimulate skeletogenesis by improving the osteogenic microenvironment, we examined the skeletal phenotype of EμBmi1 transgenic mice with overexpression of Bmi1 in lymphocytes. The size of the skeleton, trabecular bone volume and osteoblast number, indices of proliferation and differentiation of bone marrow mesenchymal stem cells (BM-MSCs) were increased significantly, ROS levels were reduced and antioxidative capacity was enhanced in EμBmi1 mice compared to WT mice. In PTHrP1-84 knockin (Pthrp(KI/KI)) mice, the expression levels of Bmi1 are reduced and potentially can mediate the premature osteoporosis observed. We therefore generated a Pthrp(KI/KI) mice overexpressing Bmi1 in lymphocytes and compared them with Pthrp(KI/KI) and WT littermates. Overexpression of Bmi1 in Pthrp(KI/KI) mice resulted in a longer lifespan, increased body weight and improvement in skeletal growth and parameters of osteoblastic bone formation with reduced ROS levels and DNA damage response parameters. Our results demonstrate that overexpression of Bmi1 in lymphocytes can stimulate osteogenesis in vivo and partially rescue defects in skeletal growth and osteogenesis in Pthrp(KI/KI) mice. These studies therefore indicate that overexpression of Bmi1 in lymphocytes can stimulate skeletogenesis by inhibiting oxidative stress and improving the osteogenic microenvironment.
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".