Transcriptome analysis and tumor suppressor requirements of STAT5‐induced senescence
Bibliographic record
Abstract
Although it is acknowledged that senescent cells accumulate with age, the molecular mechanisms leading to cell senescence as a function of age remain to be identified. In cell culture models, it has been clearly shown that senescence involves the activation of a DNA damage response secondary to short telomeres or oncogene expression. Oncogenes are altered versions of genes coding for proteins that mediate signals from extracellular factors such as cytokines, growth factors, and hormones. In particular, we show here that constitutive activation of the JAK/STAT5 signaling pathway induces senescence in both mouse and human normal cells. The process involves activation of the p53 and Rb tumor suppressor pathways and mitochondrial dysfunction. Gene expression analysis of STAT5-induced senescence revealed changes in the expression of genes coding for cytokines, proteins in cytokine signaling pathways, and several metabolic enzymes. We discuss a model called senescence-induced senescence, in which cytokines secreted by senescent cells can propagate the process as a function of age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".