Maintenance of Proliferative Capacity and Retroviral Transduction Efficiency of Human Fetal CD38 <sup>–</sup> /CD34 <sup>++</sup> Stem Cells
Bibliographic record
Abstract
Methods for the efficient transduction and expansion of fetal hematopoietic stem cells could lead to novel in utero therapies for blood cell disorders and enzymatic deficiencies. Here we describe a new assay to measure rapidly the effects of cytokines on the differentiation or expansion of primitive progenitors and stem cells found among CD38(-)CD34(++) lineage() cells isolated from human midgestation liver. Importantly, conditions that otherwise supported the expansion of clonogenic progenitors reduced their proliferative capacity. A combination of megakaryocyte growth and development factor and granulocyte-macrophage colony-stimulating factor maintained proliferative potential while also yielding an intermediate level of progenitor expansion. Retroviral transduction was achieved using Moloney murine leukemia virus-based vectors. Freshly isolated candidate stem cells could be transduced at almost 17% efficiency by a 1-h exposure to virus with centrifugation to aid transduction. This was increased to a mean 35.5% transduction efficiency after 1 day of culture. Additionally, the transduction efficiency of candidate stem cells isolated from fetal placental blood was 33.0%. These findings encourage further investigation into the feasibility of ex utero gene therapy whereby fetal cells are isolated from the circulation, transduced, and expanded ex utero before being returned to the fetus.
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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".