A ligand-receptor signaling threshold model of stem cell differentiation control: a biologically conserved mechanism applicable to hematopoiesis
Bibliographic record
Abstract
A major limitation to the widespread use of hematopoietic stem cells (HSC) is the relatively crude level of our knowledge of how to maintain these cells in vitro without loss of the long-term multilineage growth and differentiation properties required for their clinical utility. An experimental and theoretical framework for predicting and controlling the outcome of HSC stimulation by exogenous cytokines would thus be useful. An emerging theme from recent HSC expansion studies is that a net gain in HSC numbers requires the maintenance of critical signaling ligand(s) above a threshold level. These ligand-receptor complex thresholds can be maintained, for example, by high concentrations of soluble cytokines or by extracellular matrix- or cell-bound cytokine presentation. According to such a model, when the relevant ligand-receptor interaction falls below a critical level, the probability of a differentiation response is increased; otherwise, self-renewal is favored. Thus, in addition to the identity of a particular receptor-ligand interaction being important to the regulation of stem cell responses, the quantitative nature of this interaction, as well as the dynamics of receptor expression, internalization, and signaling, may have a significant influence on stem cell fate decisions. This review uses examples from hematopoiesis and other tissue systems to examine existing evidence for a role of receptor activation thresholds in regulating hematopoietic stem cell self-renewal versus differentiation events. (Blood. 2000;96:1215-1222)
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".