CG-NAP/Kinase Interactions Fine-Tune T Cell Functions
Bibliographic record
Abstract
CG-NAP, also known as AKAP450, is an anchoring/adaptor protein that streamlines signal transduction in various cell types by localizing signaling proteins and enzymes with their substrates. Great efforts are being devoted to elucidating functional roles of this protein and associated macromolecular signaling complex. Increasing understanding of pathways involved in regulating T lymphocytes suggests that CG-NAP can facilitate dynamic interactions between kinases and their substrates and thus fine-tune T cell motility and effector functions. As a result, new binding partners of CG-NAP are continually being uncovered. Here, we review recent advances in CG-NAP research, focusing on its interactions with kinases in T cells with an emphasis on the possible role of this anchoring protein as a target for therapeutic intervention in immune-mediated diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".