Systemic Mesenchymal Stromal Cell Transplantation Prevents Functional Bone Loss in a Mouse Model of Age-Related Osteoporosis
Bibliographic record
Abstract
UNLABELLED: Age-related osteoporosis is driven by defects in the tissue-resident mesenchymal stromal cells (MSCs), a heterogeneous population of musculoskeletal progenitors that includes skeletal stem cells. MSC decline leads to reduced bone formation, causing loss of bone volume and the breakdown of bony microarchitecture crucial to trabecular strength. Furthermore, the low-turnover state precipitated by MSC loss leads to low-quality bone that is unable to perform remodeling-mediated maintenance--replacing old damaged bone with new healthy tissue. Using minimally expanded exogenous MSCs injected systemically into a mouse model of human age-related osteoporosis, we show long-term engraftment and markedly increased bone formation. This led to improved bone quality and turnover and, importantly, sustained microarchitectural competence. These data establish proof of concept that MSC transplantation may be used to prevent or treat human age-related osteoporosis. SIGNIFICANCE: This study shows that a single dose of minimally expanded mesenchymal stromal cells (MSCs) injected systemically into a mouse model of human age-related osteoporosis display long-term engraftment and prevent the decline in bone formation, bone quality, and microarchitectural competence. This work adds to a growing body of evidence suggesting that the decline of MSCs associated with age-related osteoporosis is a major transformative event in the progression of the disease. Furthermore, it establishes proof of concept that MSC transplantation may be a viable therapeutic strategy to treat or prevent human age-related osteoporosis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".