From cytopenia to leukemia: the role of Gfi1 and Gfi1b in blood formation
Bibliographic record
Abstract
The DNA-binding zinc finger transcription factors Gfi1 and Gfi1b were discovered more than 20 years ago and are recognized today as major regulators of both early hematopoiesis and hematopoietic stem cells. Both proteins function as transcriptional repressors by recruiting histone-modifying enzymes to promoters and enhancers of target genes. The establishment of Gfi1 and Gfi1b reporter mice made it possible to visualize their cell type-specific expression and to understand their function in hematopoietic lineages. We now know that Gfi1 is primarily important in myeloid and lymphoid differentiation, whereas Gfi1b is crucial for the generation of red blood cells and platelets. Several rare hematologic diseases are associated with acquired or inheritable mutations in the GFI1 and GFI1B genes. Certain patients with severe congenital neutropenia carry mutations in the GFI1 gene that lead to the disruption of the C-terminal zinc finger domains. Other mutations have been found in the GFI1B gene in families with inherited bleeding disorders. In addition, the Gfi1 locus is frequently found to be a proviral integration site in retrovirus-induced lymphomagenesis, and new, emerging data suggest a role of Gfi1 in human leukemia and lymphoma, underlining the role of both factors not only in normal hematopoiesis, but also in a wide spectrum of human blood diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".