Limited Telomere Shortening in Hematopoietic Stem Cells after Transplantation
Bibliographic record
Abstract
The number of cell divisions in hematopoietic stem cells (HSCs) following transplantation of bone marrow or mobilized peripheral blood into myelo-ablated recipients is unknown. This number is expected to depend primarily on the number of transplanted stem cells, assuming that stem cells do not differ in engraftment potential and other functional properties. In a previous study, we found that the telomere length in circulating granulocytes in normal individuals shows a biphasic decline with age, most likely reflecting age-related changes in the turnover of HSCs. In order to study HSCs' proliferation kinetics following stem cells transplantation, we analyzed the telomere length in donor-derived nucleated blood cells in four HLA-matched bone marrow transplant recipients relative to comparable cells from the sibling donors. In each case, the telomeres in granulocytes were shorter in the recipient than in the donor. This difference was established in the first year post transplantation and did not change after that. The telomere length in naïve and memory T cells showed marked differences after transplantation, complicating the interpretation of telomere length data using unseparated nucleated blood cells. Interestingly, the telomere length in naïve T cells that were first observed six months post transplantation was very similar in donor and recipient pairs. Our observations are compatible with a limited number of additional cell divisions in stem cell populations after bone marrow transplantations and support the idea that different populations of stem cells contribute to short-term myeloid and long-term lympho myeloid hematopoiesis.
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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) | 0.000 | 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) | 0.001 | 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".