Quantitation of Human Cells that Produce Neutrophils and Platelets in Vivo Obtained from Normal Donors Treated with Granulocyte Colony–Stimulating Factor and/or Plerixafor
Bibliographic record
Abstract
Plerixafor (P) together with granulocyte colony–stimulating factor (G) is now recognized as an important strategy for mobilizing hematopoietic cells for use in patients given myelosuppressive therapies. However, quantitative comparisons of their ability to mobilize human cells with different hematopoietic activities in vitro or in vivo (in immunodeficient mice) and their interrelationships have not been investigated. To address these questions, we collected samples from 5 normal adult volunteers before and after administering P alone and from another 5 before and after a 4-day course of G and again after a subsequent injection of P. Measurements of their blood content of CD34 + cells, in vitro myeloid colony–forming cells, 3- and 6-week long-term culture (LTC) cell outputs, and levels of circulating human platelets, as well as myeloid and lymphoid cells obtained in immunodeficient mice that received transplants, showed all activities were maximal 4 hours after P preceded by G, and 3-week LTC outputs showed the highest concordance with the 3-week circulating human neutrophil levels obtained in mice that received transplants. Thus, human cells capable of producing neutrophils rapidly in vivo were optimally mobilized by the G + P protocol, and the 3-week LTC assay appears to offer a more specific predictor of their levels than conventional CD34 + cell or colony-forming cell counts.
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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) | 0.001 | 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) | 0.003 | 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".