Cell cycle distribution of primitive haematopoietic cells stimulated <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
A novel approach is used to study the proliferating behaviour of primitive haematopoietic cell populations in response to different stimuli. A mathematical model based on the average proportion of apoptotic, dividing and quiescent cells in primitive haematopoietic cell populations is developed to describe the mitotic history of 5- (and 6-) carboxyfluorescein diacetate succinimidyl ester-labelled cells. The cell cycle distributions in different cytokine-supplemented cultures of primitive human and mouse bone marrow cells are determined and compared with those found in vivo. The results indicate that a combination of flt-3 ligand, Steel factor and interleukin-11 or hyper-interleukin-6 provide a level of mitogenic stimulation similar to that existing in vivo after a myeloablative radiation dose. The comparison of the cell cycle distribution obtained for different cultures of human bone marrow CD34(+)(45RA/71)(-) cells demonstrates that the addition of flt-3 ligand in these cultures decreases apoptosis significantly but does not reduce quiescence. In addition, in vivo and in vitro, it was found that more than 3 days of stimulation are required to recruit a maximum number of quiescent cells into active cell cycle. These kinetics of cell cycle activation are found to be similar to those identified for the haematopoietic stem cells compartment in the same cultures. This mathematical analysis provides a useful tool for the development of haematopoietic stem cell culture processes for clinical applications.
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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".