A minimal ING1b fragment that improves the efficacy of HDAC-based cancer cell killing
Bibliographic record
Abstract
ING1b is a type II tumor suppressor that is inactive in a broad range of cancers, many of which show resistance to apoptosis. It is a stoichiometric member of histone deacetylase (HDAC) complexes 1 and targets them to the H3K4Me3 histone mark 2 to regulate transcription. ING1b's effects on transcription are then thought to affect cellular functions relevant to tumorigenesis, including proliferation, senescence and apoptosis. 3 Many independent studies have reported strong pro-apoptotic effects triggered by ectopic expression of ING1b in cancer cells, 4 , 5 , 6 and some studies have suggested that ING1b requires the activity of p53 to effectively induce apoptosis. In a recent study published in Cell Death Discovery by Boyko et al., 7 the minimal polypeptide of ING1b required to efficiently induce apoptosis was defined using a series of deletion mutations followed by different tests to determine their ability to induce cell death. The deletion mutant that showed the greatest apoptotic activity was also tested to determine whether it was able to act synergistically with HDAC inhibitors that target the epigenome, to help define the regions of ING1b that can selectively enhance cancer-cell sensitivity towards cytotoxic agents.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".