Bibliographic record
Abstract
Studies in the past several years highlight important features of the messenger RNA (mRNA) export process. For instance, groups of mRNAs acting in the same biochemical processes can be retained or exported in a coordinated manner thereby impacting on specific biochemistries and ultimately on cell physiology. mRNAs can be transported by either bulk export pathways involving NXF1/TAP or more specialized pathways involving chromosome region maintenance 1 (CRM1). Studies on primary tumor specimens indicate that many common and specialized mRNA export factors are dysregulated in cancer including CRM1, eukaryotic translation initiation factor 4E (eIF4E), HuR, nucleoporin 88, REF/Aly, and THO. This positions these pathways as potential therapeutic targets. Recently, specific targeting of the eIF4E-dependent mRNA export pathway in a phase II proof-of-principle trial with ribavirin led to impaired eIF4E-dependent mRNA export correlating with clinical responses including remissions in leukemia patients. Here, we provide an overview of these mRNA export pathways and highlight their relationship to cancer.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".