<i>Ex vivo</i> expansion of megakaryocyte progenitor cells from normal bone marrow and peripheral blood and from patients with haematological malignancies
Bibliographic record
Abstract
A number of haematological and non-haematological malignancies can be successfully treated using high-dose chemotherapy +/- irradiation followed by haematopoietic progenitor cell transplantation. Post transplant, thrombocytopenia and neutropenia always occur and patients require platelet transfusions. It may be possible to reduce the period of thrombocytopenia by re-infusion of ex vivo expanded megakaryocyte progenitors (MP), derived from the progenitor cell graft. We have investigated the expansion of MP from CD34+ enriched cells from normal bone marrow (NBM) and peripheral blood (PB) and remission BM or PB samples from patients with haematological malignancies. CD34+ cells were cultured in serum-free medium supplemented with thrombopoietin (TPO), interleukin 1 (IL-1), IL-6 and stem cell factor (SCF) for 7 d, then cell proliferation was assessed by flow cytometry using lineage-specific markers. It was possible to significantly expand the number of MP cells from all sources. There were no major differences in yields of MP from normal BM or PB, or BM from multiple myeloma and non-Hodgkin's lymphoma patients. However, expansion of MP in acute myeloid leukaemia samples was lower than all other samples and the number of megakaryocyte colony-forming units was reduced. Several cytokine combinations were evaluated to optimize MP expansion from NBM. Equivalent yields of MP were obtained using TPO and one of IL-1, IL-3, granulocyte-macrophage colony-stimulating factor or SCF, suggesting that large cytokine combinations are not necessary for this procedure. It should be possible to scale up the culture conditions described to produce effective MP doses for clinical transplantation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".