Promising SINEs for Embargoing Nuclear–Cytoplasmic Export as an Anticancer Strategy
Bibliographic record
Abstract
Abstract In cancer cells, the nuclear–cytoplasmic transport machinery is frequently disrupted, resulting in mislocalization and loss of function for many key regulatory proteins. In this review, the mechanisms by which tumor cells co-opt the nuclear transport machinery to facilitate carcinogenesis, cell survival, drug resistance, and tumor progression will be elucidated, with a particular focus on the role of the nuclear–cytoplasmic export protein. The recent development of a new generation of selective inhibitors of nuclear export (XPO1 antagonists) and how these novel anticancer drugs may bring us closer to the implementation of this therapeutic strategy in the clinic will be discussed. Significance: The nuclear transport mechanism is dysregulated in many malignancies and is associated with dysfunction of many regulatory proteins. Targeting this mechanism as an anticancer strategy has been compelling, and novel agents that selectively inhibit the nuclear export pathway have demonstrated preliminary evidence of clinical efficacy with an acceptable safety profile. Cancer Discov; 4(5); 527–37. ©2014 AACR.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".