Complexity of the human acute myeloid leukemia stem cell compartment: Implications for therapy
Bibliographic record
Abstract
Emerging evidence suggests cancer stem cells CSCs) sustain neoplasms; however, little is undertood of the healthy cell initially targeted and the esultant CSCs [1-3]. The most advanced understandng comes from the hematologic malignancies because f the availability of quantitative functional assays for ormal stem cells (hematopoietic stem cells; HSCs) nd progenitor cells (colony-forming cells), as well as or leukemic stem cells (LSCs) and progenitor cells acute myeloid leukemia [AML]-colony-forming ells). Advances in the ability to identify the biological roperties of individual human HSCs and LSCs by sing retroviral-mediated clonal tracking coupled with he nonobese diabetic/severe combined immunodefiiency (NOD/SCID) mouse xenotransplantation asay have been critical to progress [4-6]. These studies emonstrated that LSCs are not functionally homoeneous but, like the normal HSC compartment, are omposed of distinct hierarchically arranged LSC lasses. Thus, the AML clone is organized as a hierrchy that originates from human SCID leukemianitiating cells (SL-ICs), which produce AML colonyorming units (AML-CFUs) and leukemic blasts. oreover, similarities between SL-ICs and normal SCs support a hypothesis that the target cell of rigin of AML LSCs often lies within the normal stem ell compartment, although, as noted below, under ome circumstances LSCs could arise through the cquisition of additional mutations in downstream rogenitors [1,3]. LSCs hold the key to understanding the origin nd maintenance of AML and possess biological proprties that are different from the bulk of the leukemic lones; this makes them difficult to eradicate. Thus, h
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".