Consistent Up-regulation of Stat3 Independently of Jak2 Mutations in a New Murine Model of Essential Thrombocythemia
Bibliographic record
Abstract
Janus-activated kinase 2 (JAK2) mutations are common in myeloproliferative disorders; however, although they are detected in virtually all polycythemia vera patients, they are found in approximately 50% of essential thrombocythemia (ET) patients, suggesting that converging pathways/abnormalities underlie the onset of ET. Recently, the chromosomal translocation 3;21, leading to the fusion gene AML1/MDS1/EVI1 (AME), was observed in an ET patient. After we forced the expression of AME in the bone marrow (BM) of C57BL/6J mice, all the reconstituted mice died of a disease with symptoms similar to ET with a latency of 8 to 16 months. Peripheral blood smears consistently showed an elevated number of dysplastic platelets with anisocytosis, degranulation, and giant size. Although the AME-positive mice did not harbor Jak2 mutations, the BM of most of them had significantly higher levels of activated Stat3 than the controls. With combined biochemical and biological assays we found that AME binds to the Stat3 promoter leading to its up-regulation. Signal transducers and activators of transcription 3 (STAT3) analysis of a small group of ET patients shows that in about half of the patients, there is STAT3 hyperactivation independently of JAK2 mutations, suggesting that the hyperactivation of STAT3 by JAK2 mutations or promoter activation may be a critical step in development of ET.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".