Bibliographic record
Abstract
Signal transduction induced by tumor necrosis factor (TNF) family members and their receptors has been an intensive area of research for several years. The major impact of these studies has been the delineation of apoptotic and cell survival signaling pathways. These discoveries, coupled with major advances in the study of mammalian apoptotic machinery, constitute a promising blueprint of the molecular network governing the fate of all living cells. In this review, we concentrate on the fate of cells in the immune system, where regulation of cell death and cell survival is a frequent and important exercise. A small imbalance in favor of either fate can result in disastrous pathological outcomes, such as cancer, autoimmunity or immune deficiency. It is an insurmountable task to discuss all molecules reported in the literature that are implicated in lymphocyte death or survival. We have therefore focused on discoveries made by mouse gene targeting, as these studies provide the most physiologically relevant information on each molecule. We begin with a description of signaling channels initiated by TNF receptor type 1 engagement, which can lead to either cell survival or to cell death. The point of bifurcation of this pathway and the decision-making molecules FADD, TRAF2 and RIP are discussed. We then follow apoptotic and survival pathways from upstream to downstream, describing many important players involved in signal transduction. Molecules important for NF-kappaB and JNK/stress-activated protein kinase activation such as IKKbeta, NEMO, MAP3K and TRAF6 are discussed, as is the impact of BAFF and its receptors on B-cell survival. Mouse mutants that have helped to define the mammalian apoptosis execution machinery, including animals lacking Apaf-1, caspase-3 and caspase-9, are also described. We conclude with a brief analysis of the potential therapeutic options arising from this body of work.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".