Low dose docosahexaenoic acid protects normal colonic epithelial cells from araC toxicity
Bibliographic record
Abstract
BACKGROUND: The nucleoside analogue arabinosylcytosine (araC) has been used for many years in the treatment of acute leukemia. Evidence in the literature suggests that araC may inhibit the growth of human colon carcinoma cell lines as well. Because araC action interferes with normal nucleoside metabolism, it is highly toxic to a number of normal cell types including bone marrow and intestinal mucosa cells. Here we investigate whether the omega-3 fatty acid docosahexaenoic acid (DHA) could selectively target araC toxicity toward colonic tumor cells while protecting the normal cells in vitro. RESULTS: Cultures of normal rat colonic epithelial cells (4D/WT) and those transformed by v-src (D/v-src) were supplemented with graded concentrations of DHA or arachidonic acid (AA) alone or in combination with araC. AraC was only 1.6 fold more toxic to D/v-src than 4D/WT in cultures without added fatty acids. Supplementing with as little as 3 muM of either AA or DHA increased araC toxicity by more than 30-fold in the tumorigenic cells. The toxic effect of araC on the normal cells was also increased by the fatty acid supplementation. IC50 values were decreased 1.7 fold by DHA in the 4D/WT cells but a more than 7-fold decrease was observed during AA supplementation. As a result, the therapeutic index of araC (IC50 normal/IC50 tumor) was more than 3-fold higher in the DHA than the AA supplemented cells. The expression of protein kinase C isoform epsilon was decreased in AA alone supplemented D/v-src cultures but in combination with araC decreased only in DHA supplemented 4D/WT cells. CONCLUSION: Low dose DHA supplementation may enhance araC chemotherapy in colon cancer while protecting normal tissues, possibly through control of PKC signalling pathways.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".