Telomere Length in Subpopulations of Human Hematopoietic Cells
Bibliographic record
Abstract
In order to test the hypothesis that the telomere length in human hematopoietic cells correlates with their proliferative potential, we analyzed the telomere length in highly purified subpopulations of bone marrow cells. Cells were sorted on the basis of CD34 and CD38 cell surface markers, and two samples were additionally sorted on the basis of Hoechst 33342 dye efflux allowing isolation of side population (SP) cells. The telomere length in limiting numbers of sorted cells was analyzed using a newly developed fluorescence in situ hybridization (flow-FISH) method in which hybridization of telomere probe in cells of interest is measured relative to control cells in the same tube. In all seven bone marrow samples analyzed, the telomere length in CD34(+)CD38(-) cells was longer than in CD34(+)CD38(+) cells from the same donor (p < 0.02). Results with sorted SP cells were less clear: the telomere fluorescence in these cells was very heterogeneous, and a reproducible difference in telomere length relative to CD34(+)CD38(-) cells could not be observed. We conclude that the telomere length in subpopulations of hematopoietic cells does appear to be correlated with the known proliferative potential of such cells and that further characterization of cells on the basis of telomere length is warranted for enrichment of very rare precursors of hematopoietic and other tissues.
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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".