Interaction of Arsenic Trioxide As <sub>2</sub> O <sub>3</sub> with DNA and RNA
Bibliographic record
Abstract
Arsenic salts have been used for centuries to treat a variety of medical conditions ranging from infectious disease to cancer. More recently, trivalent arsenic trioxide was found to exhibit high antitumor activity towards hematological malignancies. Even though much is known about antitumor activity and DNA damage by As2O3, there has been no report on the interaction of arsenic trioxide with isolated DNA or RNA. Therefore, it was of interest to examine the interaction of As2O3 with DNA and RNA in aqueous solution at physiological pH. FTIR and UV-visible difference spectroscopic methods were used to characterize the nature of drug-DNA and drug-RNA interactions and to determine the As binding site, the binding constant, the sequence selectivity, the helix stability, and the biopolymer secondary structure in the As2O3-polynucleotide complexes in vitro. The FTIR spectroscopic studies were conducted with As2O3-polynucleotide (phosphate) ratios of 1/40, 1/20, 1/10, and 1/5, with a final DNA (P) or RNA (P) concentration of 6.25 mmol/l. Spectroscopic results showed As2O3 binds to DNA and RNA at G-C, A-T, and A-U bases, and no interaction with the backbone PO2 group. As2O3-DNA and -RNA adducts showed one type of binding with overall binding constant of K(As2O3-DNA) = 1.24 x 10(5) M(-1) and K(As2O3-RNA) = 2.60 x 10(5) M(-1). The As2O3-polynucleotide complexation is associated with a partial biopolymer aggregation and no major alterations of B-DNA or A-RNA structure.
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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) | 0.000 | 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) | 0.001 | 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".