Evidence of Hemoglobin Binding to Arsenic as a Basis for the Accumulation of Arsenic in Rat Blood
Bibliographic record
Abstract
Four trivalent arsenic species, inorganic arsenite (iAs(III)), monomethylarsonous acid (MMA(III)), dimethylarsinous acid (DMA(III)), and phenylarsine oxide (PhAs(III)O), have shown increasing binding affinity with the hemoglobin (Hb) of rats and humans. The binding stoichiometry was consistent with the number of reactive cysteine residues in the alpha and beta chains of Hb. Comparing the binding affinity of rat Hb and human Hb for the same trivalent arsenic species, rat Hb was 3-16 times stronger than human Hb as demonstrated by their apparent binding constants. Comparative experiments involving incubation of human and rat red blood cells (RBC) with iAs(III), MMA(III), and DMA(III) showed that 15-30-fold more arsenic species were bound to the Hb of rat RBC than that of human RBC. In vivo experiments using rats fed with an arsenic-supplemented diet showed that arsenic in RBC of the rats was predominantly found in the protein-bound form. Further characterization by nanoelectrospray mass spectrometry of the arsenic species in the RBC of these rats confirmed that most arsenic was bound to the alpha chain of Hb. Taken together, these results suggest that the stronger binding affinity of these arsenic species to rat Hb is responsible for the accumulation of arsenic in rat blood. The results provide a chemical basis to explain the previously observed intriguing difference in the retention of arsenic in the human and the rat. The techniques and approaches described can be applied to the studies of arsenic interactions with other functional proteins.
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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".