Signal transducer and activator of transcription 3
Bibliographic record
Abstract
PURPOSE OF REVIEW: Signal transducer and activator of transcription 3 (STAT3) is an important transcription factor involved in a wide variety of cellular functions. Germline loss-of-function mutations are known to cause hyper-IgE immunodeficiency (autosomal dominant hyper IgE syndrome), whereas somatic gain-of-function mutations have been described in large granular cell leukemia, and polymorphisms in STAT3 have been associated with inflammatory bowel disease and other solid organ tumors. The review examines recent discoveries in our understanding of the nonmalignant disease processes affected by STAT3 mutations in human disease. RECENT FINDINGS: Germline STAT3 gain-of-function mutations have recently been identified in patients with an early-onset autoimmunity/lymphoproliferative syndrome. STAT3 plays a previously unrecognized role in several facets of the pathogenesis of allergy. Loss-of-function STAT3 mutations revealed critical roles for STAT3 in the development and function of several lymphocyte populations and in their role in host defense. SUMMARY: The discovery of new gain-of-function mutations in STAT3, as well as new studies among patients with loss-of-function mutations, expand the understanding of the pathophysiology of STAT3 function and its importance in regulating the immune system. These findings contribute to elucidating STAT3 biology and clinical symptoms in patients with the different disease phenotypes.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.046 |
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".