Young bone marrow transplantation delays bone aging in old mice
Bibliographic record
Abstract
Recent discoveries have shown that systemic manipulations, such as parabiosis, blood exchange, and young plasma transfer, can counteract many hallmarks of aging. This rejuvenation effect has been attributed to circulatory factors produced by cells from both hematopoietic and non-hematopoietic lineages. However, the specific involvement of bone marrow (BM) or hematopoietic cells in producing such factors and their effects on aging is still unclear. We developed a model of aged mice with transplanted young or old BM cells and assessed the impact on the aging process, specifically on energy metabolism and bone remodeling parameters. The donor BM cell engraftment in the aged mice was confirmed by flow cytometry using a transplanted cell-specific marker (green fluorescent protein). Energy metabolism was assessed using Oxymax indirect calorimetry system after 3 months of transplantation. Tibiae and L3-L4 vertebrae were analyzed using micro-CT, a three-point bending test and bone histomorphometry. Moreover, bone marrow proteome was assessed using proteomics, and blood serum/plasma was collected and analyzed using the Luminex assay. Our results showed that while the effect on energy metabolism was insignificant, rejuvenating the BM through young bone marrow transplantation reversed age-associated low bone mass traits in old mice. Specifically, young bone marrow transplantation improved bone trabecular microarchitecture both in tibiae and vertebrae of old mice and increased the number of osteoblasts and osteoclasts compared to old bone marrow transplantation. In conclusion, young bone marrow cells may represent a future therapeutic strategy for age-related diseases such as osteoporosis. The findings of this study provide important insights into our understanding of aging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".