Mechanisms of action of arsenic trioxide.
Bibliographic record
Abstract
Arsenic trioxide has shown substantial efficacy in treating both newly diagnosed and relapsed patients with acute promyelocytic leukemia (APL). As a single agent, it induces complete remissions, causing few adverse effects and only minimal myelosuppression. These successes have prompted investigations to elucidate the mechanisms of action underlying these clinical responses. Substantial data show that arsenic trioxide produces remissions in patients with APL at least in part through a mechanism that results in the degradation of the aberrant PML-retinoic acid receptor alpha fusion protein. Studies have also investigated concerns about the toxicity and potential carcinogenicity of long-term exposure to environmental arsenic. Arsenic apparently affects numerous intracellular signal transduction pathways and causes many alterations in cellular function. These actions of arsenic may result in the induction of apoptosis, the inhibition of growth and angiogenesis, and the promotion of differentiation. Such effects have been observed in cultured cell lines and animal models, as well as clinical studies. Because arsenic affects so many cellular and physiological pathways, a wide variety of malignancies, including both hematologic cancer and solid tumors derived from several tissue types, may be susceptible to therapy with arsenic trioxide. These multiple actions of arsenic trioxide also highlight the need for additional mechanistic studies to determine which actions mediate the diverse biological effects of this agent. This information will be critical to realizing the potential for synergy between arsenic trioxide and other chemotherapeutic agents, thus providing enhanced benefit in cancer therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".