Characterization of Cord Blood Hematopoietic Stem Cells
Bibliographic record
Abstract
A major problem hampering the development of effective stem cell-based therapies is the absence of a clear understanding of the composition of the hematopoietic stem cell (HSC) pool in humans and how ex vivo manipulation can differentially affect the various HSC classes. This paper will review recent advances in the use of the NOD/SCID xenotransplant assay to characterize the human stem cell compartment and to determine how ex vivo culture affects stem cells. Using lentivector-mediated clonal tracking we found that only 4 days of culture can significantly reduce the number of SCID-repopulating cells (SRCs) contributing to the human graft. Similar results were seen with a competitive assay strategy where non-cultured cells marked with the RFP-lentivector markedly outcompete cultured cells marked with a EGFP-lentivector both transplanted into the same NOD/SCID mouse. A novel intrafemoral (IF) assay was developed to permit the transplantation of human stem cells that might be difficult to detect using the traditional IV injection method. With the IF assay we identified a novel class of human stem cell with the ability to rapidly generate a large graft of human myeloid and erythroid cells within 2 weeks post transplant.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".