PML links aberrant cytokine signaling and oncogenic stress to cellular senescence
Bibliographic record
Abstract
Senescence is a tumor suppressor mechanism triggered by oncogenic stimuli and characterized by a permanent cell cycle arrest mediated by tumor suppressors such as p53, Rb and PML. PML itself is critical for the formation of nuclear bodies (PML bodies) that accumulate in senescent cells rendering them suitable markers for the senescence phenotype. The mechanism of PML-induction during senescence is complex and includes increased PML gene transcription by p53 or transcription factors of the interferon/Jak/Stat pathway. In turn, PML engages both p53 and Rb, although the precise molecular processes are unknown. PML interacts with the DNA-binding domain of p53 facilitating p53 modifications. PML can also interact with Rb and may play a role in Rb-dependent gene silencing during senescence. Recent studies suggest an additional connection between PML and the senescence program. Senescence involves a constitutive activation of the DNA damage response. Intriguingly, proteins that signal DNA damage or help repairing it localize to PML bodies, suggesting that PML may play a role in the DNA damage response during senescence. We think that the discovery of factors acting upstream or downstream PML may help to understand how cells bypass senescence on their way to tumorigenesis. More importantly the PML pathway may eventually lead to novel anti-cancer therapies.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".