Bibliographic record
Abstract
Interaction of cytokines with their cognate receptors leads to the activation of latent transcription factors, the signal transducer and activator of transcription (STAT) proteins. Numerous studies have identified the critical roles played by STAT proteins in regulating cell proliferation, differentiation and survival. Consequently, the activity of STAT proteins is negatively regulated by a variety of different mechanisms, which include alternative splicing, covalent modifications, protein-protein interactions with negative regulatory proteins and proteolytic processing by proteases. Cleavage of STAT proteins by proteases results in the generation of C-terminally truncated proteins, called STATgamma, which lack the transactivation domain and behave as functional dominant-negative proteins. Currently, STATgamma isoforms have been identified for Stat3, Stat5a, Stat5b and Stat6 in different cellular contexts and biological processes. Evidence is mounting for the role of as yet unidentified serine proteases in the proteolytic processing of STAT proteins, although at least one cysteine protease, calpain is also known to cleave these STATs in platelets and mast cells. Recently, studies of acute myeloid leukaemia and cutaneous T cell lymphoma patients have revealed important roles for the aberrant expression of Stat3gamma and Stat5gamma proteins in the pathology of these diseases. Together, these findings indicate that proteolytic processing is an important mechanism in the regulation of STAT protein biological activity and provides a fertile area for future studies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".