Characterization of primitive hematopoietic cells from patients with dyskeratosis congenita
Bibliographic record
Abstract
Dyskeratosis congenita (DC) is an inherited bone marrow (BM) failure syndrome associated with mutations in telomerase genes and the acquisition of shortened telomeres in blood cells. To investigate the basis of the compromised hematopoiesis seen in DC, we analyzed cells from granulocyte colony-stimulating factor mobilized peripheral blood (mPB) collections from 5 members of a family with autosomal dominant DC with a hTERC mutation. Premobilization BM samples were hypocellular, and percentages of CD34(+) cells in marrow and mPB collections were significantly below values for age-matched controls in 4 DC subjects. Directly clonogenic cells, although present at normal frequencies within the CD34(+) subset, were therefore absolutely decreased. In contrast, even the frequency of long-term culture-initiating cells within the CD34(+) DC mPB cells was decreased, and the telomere lengths of these cells were also markedly reduced. Nevertheless, the different lineages of mature cells were produced in normal numbers in vitro. These results suggest that marrow failure in DC is caused by a reduction in the ability of hematopoietic stem cells to sustain their numbers due to telomere impairment rather than a qualitative defect in their commitment to specific lineages or in the ability of their lineage-restricted progeny to execute normal differentiation programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".