The Evolving Role of TRAFs in Mediating Inflammatory Responses
Bibliographic record
Abstract
TRAFs [tumor necrosis factor (TNF) receptor associated factors] are a family of signaling molecules that function downstream of multiple receptor signaling pathways and play a pivotal role in the biology of innate, and adaptive immune cells. Following receptor ligation, TRAFs generally function as adapter proteins to mediate the activation of intracellular signaling cascades. With the exception of TRAF1 that lacks a Ring domain, TRAFs have an E3 ubiquitin ligase activity which also contributes to their ability to activate downstream signaling pathways. TRAF-mediated signaling pathways culminate in the activation of several transcription factors, including nuclear factor-κB (NF-κB), mitogen-activated protein kinases (MAPKs; e.g., ERK-1 and ERK-2, JNK, and p38), and interferon-regulatory factors (IRF; e.g., IRF3 and IRF7). The biological role of TRAFs is largely due to their ability to positively or negatively regulate canonical and non-canonical NF-κB signaling. While TRAF-mediated signaling regulates various immune cell functions, this review is focused on the recent advances in our knowledge regarding the molecular mechanisms through which TRAF proteins regulate, positively and negatively, inflammatory signaling pathways, including Toll-IL-1 receptors, RIG-I like receptors, and Nod-like receptors. The review also offers a perspective on the unanswered questions that need to be addressed to fully understand how TRAFs regulate inflammation.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".