Cellular diamine levels in cancer chemoprevention: modulation by ibuprofen and membrane plasmalogens
Bibliographic record
Abstract
BACKGROUND: To develop effective strategies in cancer chemoprevention, an increased understanding of endogenous biochemical mediators that block metastatic processes is critically needed. Dietary lipids and non-steroidal anti-inflammatory drugs (NSAIDs) have a published track record of providing protection against gastrointestinal malignancies. In this regard, we examined the effects of membrane plasmalogens and ibuprofen on regulation of cellular levels of diamines, polyamine mediators that are augmented in cancer cells. For these studies we utilized Chinese hamster ovary (CHO) cells and NRel-4 cells, a CHO cell line with defective plasmalogen synthesis. RESULTS: NRel-4 cells, which possess cellular plasmalogen levels that are 10% of control CHO cells, demonstrated 2- to 3-fold increases in cellular diamine levels. These diamine levels were normalized by plasmalogen replacement and significantly reduced by ibuprofen. In both cases the mechanism of action appears to mainly involve increased diamine efflux via the diamine exporter. The actions of ibuprofen were not stereospecific, supporting previous studies that cyclooxygenase (COX) inhibition is unlikely to be involved in the ability of NSAIDs to reduce intracellular diamine levels. CONCLUSIONS: Our data demonstrate that ibuprofen, a drug known to reduce the risk of colorectal cancer, reduces cellular diamine levels via augmentation of diamine efflux. Similarly, augmentation of membrane plasmalogens can increase diamine export from control and plasmalogen-deficient cells. These data support the concept that membrane transporter function may be a therapeutic point of intervention for dietary and pharmacological approaches to cancer chemoprevention.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".