A Review of the Current Impact of Inhibitors of Apoptosis Proteins and Their Repression in Cancer
Bibliographic record
Abstract
The Inhibitor of Apoptosis (IAP) family possesses the ability to inhibit programmed cell death through different mechanisms; additionally, some of its members have emerged as important regulators of the immune response. Both direct and indirect activity on caspases or the modulation of survival pathways, such as nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), have been implicated in mediating its effects. As a result, abnormal expression of inhibitor apoptosis proteins (IAPs) can lead to dysregulated apoptosis promoting the development of different pathologies. In several cancer types IAPs are overexpressed, while their natural antagonist, the second mitochondrial-derived activator of caspases (Smac), appears to be downregulated, potentially contributing to the acquisition of resistance to traditional therapy. Recently developed Smac mimetics counteract IAP activity and show promise in the re-sensitization to apoptosis in cancer cells. Given the modest impact of Smac mimetics when used as a monotherapy, pairing of these compounds with other treatment modalities is increasingly being explored. Modulation of molecules such as tumor necrosis factor-α (TNF-α) present in the tumor microenvironment have been suggested to contribute to putative therapeutic efficacy of IAP inhibition, although published results do not show this consistently underlining the complex interaction between IAPs and cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".