Benefits of Combined All-Trans Retinoic Acid and Arsenic Trioxide Treatment of Acute Promyelocytic Leukemia Cells and Further Enhancement by Inhibition of Atypically Expressed Transglutaminase 2
Bibliographic record
Abstract
Randomized trials in acute promyelocytic leukemia patients have shown that treatment with a combination of all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) is superior in efficacy to monotherapy, with significantly decreased mortality. So far, there are little data available to explain the success of the ATRA and ATO combination treatment in molecular terms. We showed that ATRA- and ATO-treated cells had the same capacity for superoxide production, which was reduced by two-thirds in the combined treatment. Secreted inflammatory biomarkers (monocyte chemoattractant protein-1 [MCP-1], interleukin-1 beta [IL-1β] and tumor necrosis factor-α [TNF-α]) were significantly decreased and were further reduced in a transglutaminase 2 (TG2) expression-dependent manner. The amount of secreted TNF-α in the supernatant of NB4 TG2 knockout cells was close to 50 times lower than in ATRA-treated differentiated wild-type NB4 cells. The irreversible inhibitor of TG2 NC9 not only decreased reactive oxygen species production 28-fold, but decreased the concentration of MCP-1, IL-1β and TNF-α 8-, 15- and 61-fold, respectively in the combined ATRA + ATO-treated wild-type NB4 cell culture. We propose that atypical expression of TG2 leads to the generation of inflammation, which thereby serves as a potential target for the prevention of differentiation syndrome.
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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 | 0.000 | 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".