Ceramide regulation of nuclear protein import
Bibliographic record
Abstract
Nucleocytoplasmic trafficking is an essential and responsive cellular mechanism that directly affects cell growth and proliferation, and its potential to address metabolic challenge is incompletely defined. Ceramide is an antiproliferative sphingolipid found within vascular smooth muscle cells in atherosclerotic plaques, but its mechanism of action remains unclear. The hypothesis that ceramide inhibits cell growth through nuclear transport regulation was tested. In smooth muscle cells, exogenously supplemented ceramide inhibited classical nuclear protein import that involved the activation of cytosolic p38 mitogen-activated protein kinase (MAPK). After application of SB 202190, a specific and potent pharmacological antagonist of p38 MAPK, sphingolipid impingement on nuclear transport was corrected. Distribution pattern assessments of two essential nuclear transport proteins, importin-alpha and Cellular Apoptosis Susceptibility, revealed ceramide-mediated relocalization that was reversed upon the addition of SB 202190. Furthermore, cell counts, nuclear cyclin A, and proliferating cell nuclear antigen expression, markers of cellular proliferation, were diminished after ceramide treatment and effectively rescued by the addition of inhibitor. Together, these data demonstrate, for the first time, the sphingolipid regulation of nuclear import that defines and expands the adaptive capacity of the nucleocytoplasmic transport machinery.
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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) | 0.000 | 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".