Transducing signals from antigen receptors to nuclear factor κB
Bibliographic record
Abstract
Signaling leading to the survival or apoptosis of immune system cells must be balanced to ensure the normal mounting and extinguishing of immune responses. One of the essential regulators of immune cell survival is the transcription factor nuclear factor kappaB (NF-kappaB). NF-kappaB is critical for the activation of T and B lymphocytes and is a central coordinator of innate and adaptive immunity. Pathogen recognition, whether mediated via the Toll-like receptors or via the antigen-specific T- and B-cell receptors, initiates the activation of distinct signal transduction pathways that activate NF-kappaB. Activation of NF-kappaB by these pathways is necessary for lymphocyte activation, expansion, and effector function in response to infection. In addition, recent work has shown that the aberrant activation of NF-kappaB by these pathways can contribute to the development of autoimmunity, chronic inflammation, or lymphoid malignancy. There is thus an urgent need to understand the exact molecular details of these signal transduction cascades so that we may develop novel therapeutics. This article will review the specific signal transduction pathways that mediate NF-kappaB activation in response to antigen receptor ligation in T and B lymphocytes. These newly defined pathways, which are essential for adaptive immune responses, are built around the key adapter protein, Bcl-10. Bcl-10 is known to participate in chromosomal translocations in human mucosa-associated lymphoid tissue lymphomas.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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".