Signaling pathways mediated by tumor necrosis factor α
Bibliographic record
Abstract
Tumor necrosis factor a (TNFα) has been shown to trigger many signaling pathways. Following oligomerization by TNFα, the receptors TNF-RI and TNF-RI1 associate with adapter molecules via specific protein-protein interactions. The subsequent recruitment of downstream molecules to the receptor complex enables propagation of the TNFα signal. l k o cellular responses to TNFα have been well documented, the induction of cell death and the activation of gene transcription for cell survival. TNFα-induced apoptosis involves the activation of caspase cascades, which culminate in the cleavage of specific cellular substrates to effect cell death. TNFα has also been implicated in various caspase-independent cell death processes. Two transcription factors activated by TNFα are nuclear factor KB (NFKB) and activating protein 1 (AP-1). Pathways that promote the activation of these transcription factors involve signaling molecules such as kinases, phospholipases, and sphingomyelinases. In addition to increased survival (anti-apoptotic) gene expression, NFKB and AP-1 also induce the expression of genes involved in inflammation, cell growth, and signal regulation. The past decade has witnessed the identification of numerous signaling intermediates implicated in TNFα cellular responses. This article reviews the molecular mechanisms of TNFα signal transduction. In particular, pathways involved in cell death and transcription factor activation are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".