Kit and Scl regulation of hematopoietic stem cells
Bibliographic record
Abstract
PURPOSE OF REVIEW: KIT tyrosine kinase receptor is essential for several tissue stem cells, especially for hematopoietic stem cells (HSCs). Moderately decreased KIT signaling is well known to cause anemia and defective HSC self-renewal, whereas gain-of-function mutations are infrequently found in leukemias. Thus, maintaining KIT signal strength is critically important for homeostasis. KIT signaling in HSCs involves effectors such as SHP2 and PTPN11. This review summarizes the recent developments on the novel mechanisms regulating or reinforcing KIT signal strength in HSCs and its perturbation in polycythemia vera. RECENT FINDINGS: Stem cell leukemia (SCL) is a transcription factor that is essential for HSC development. Genetic experiments indicate that Kit, protein tyrosine phosphatase, nonreceptor type 11 (Ptpn11), or Scl control long-term HSC self-renewal, survival, and quiescence in adults. Kit is now shown to be centrally involved in two feedforward loops in HSCs, one with Ptpn11 and the other with Scl. SUMMARY: Knowledge of the regulatory mechanisms that favor self-renewal divisions or a lineage determination process is central to the design of strategies to expand HSCs for the purpose of cell therapy. In addition, transcriptome and phosphoproteome analyses of erythroblasts in polycythemia vera identified lower SCL expression and hypophosphorylated KIT, suggesting that the KIT-SCL loop is relevant to the pathophysiology of human blood disorders as well.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".