FES/FER kinase signaling in hematopoietic cells and leukemias
Bibliographic record
Abstract
FES and FES-related (FER) comprise a unique subfamily of protein-tyrosine kinases (PTKs) that signal downstream of several classes of receptors involved in regulating hematopoietic cell development, survival, migration, and inflammatory mediator release. Activated alleles of FES are potent inducers of myeloid differentiation, however FES-deficient mice have only subtle differences in hematopoiesis. This may reflect overlapping function of other kinases such as FER. Studies of FES- and FER-deficient mice have revealed more prominent roles in regulating the activation of mature innate immune cells, including macrophages and mast cells. Recently, new insights into regulation of FES/FER kinases has emerged with the characterization of their N-terminal phospholipid-binding and membrane targeting FER/CIP4 homology-Bin/Amphyphysin/Rvs (F-BAR) and F-BAR extension (FX) domains. The F-BAR/FX domains regulate subcellular localization and FES/FER kinase activation. FES kinase activity is also enhanced upon ligand binding to its SH2 domain, which may lead to further phosphorylation of the same ligand, or other ligand-associated proteins. In mast cells, SH2 ligands of FES/FER include KIT receptor PTK, and the high affinity IgE receptor (FceRI) that trigger rapid activation of FES/FER and signaling to regulators of the actin cytoskeleton and membrane trafficking. Recently, FES/FER have also been implicated in growth and survival signaling in leukemias driven by oncogenic KIT and FLT3 receptors. With further definition of their roles in immune cells and their progenitors, FES/FER may emerge as relevant therapeutic targets in inflammatory diseases and leukemias.
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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) | 0.000 | 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) | 0.001 | 0.000 |
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".