Influence of LOX/COX inhibitors on cell differentiation induced by all-trans retinoic acid in neuroblastoma cell lines
Bibliographic record
Abstract
We investigated the possible modulation by LOX/ COX inhibitors of all-trans retinoic acid (ATRA)-induced cell differentiation in two established neuroblastoma cell lines, SH-SY5Y and SK-N-BE(2). Caffeic acid, as an inhibitor of 5-lipoxygenase, and celecoxib, as an inhibitor of cyclooxygenase-2, were chosen for this study. The effects of the combined treatment with ATRA and LOX/COX inhibitors on neuroblastoma cells were studied using cell morphology assessment, detection of differentiation markers by immunoblotting, measurement of proliferation activity, and cell cycle analysis and apoptosis detection by flow cytometry. The results clearly demonstrated the potential of caffeic acid to enhance ATRA-induced cell differentiation, especially in the SK-N-BE(2) cell line, whereas application of celecoxib alone or with ATRA led predominantly to cytotoxic effects in both cell lines. Moreover, the higher sensitivity of the SK-N-BE(2) cell line to combined treatment with ATRA and LOX/COX inhibitors suggests that cancer stem cells are a main target for this therapeutic approach. Nevertheless, further detailed study of the phenomenon of enhanced cell differentiation by expression profiling is needed.
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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) | 0.001 | 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 | 0.001 |
| 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".