The Seleno Bis(<i>S</i>-glutathionyl) Arsinium Ion Is Assembled in Erythrocyte Lysate
Bibliographic record
Abstract
Approximately 75 million people are currently exposed to arsenic concentrations in drinking water, which is associated with the development of internal cancers. One way to ameliorate this undesirable situation is to remove arsenic (arsenite and arsenate) from drinking water. An alternative approach is the development of an inexpensive palliative dietary supplement that promotes the excretion of intestinally absorbed arsenite from the body. To this end, the simultaneous administration of New Zealand white rabbits with arsenite and selenite resulted in the biliary excretion of the seleno-bis (S-glutathionyl) arsinium ion, [(GS)2AsSe]-. This apparent detoxification mechanism has been recently extended to environmentally relevant doses [Gailer, J., Ruprecht, L., Reitmeir, P., Benker, B., and Schramel, P. (2004) Appl. Organometal. Chem. 18, 670-675]. The site of formation of this excretory product in the organism, however, is unknown. To investigate if [(GS)2AsSe]- is formed in rabbit blood, we added arsenite and selenite and analyzed blood aliquots using arsenic and selenium X-ray absorption spectroscopy. The characteristic arsenic and selenium X-ray absorption spectra of [(GS)2AsSe]- were detected within 2 min after addition and comprised 95% of the blood selenium 30 min after addition. To elucidate if erythrocytes are involved in the biosynthesis of [(GS)2AsSe]- in blood, arsenite and 77Se-selenite were added to rabbit erythrocyte lysate and the obtained solution was analyzed by 77Se NMR spectroscopy (273 K). This resulted in a 77Se NMR signal with a chemical shift identical to that of synthetic [(GS)2AsSe]- added to lysate. Combined, these results demonstrate that [(GS)2AsSe]- is rapidly formed in blood and that erythrocytes are an important site for the in vivo formation of this toxicologically important metabolite.
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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 | 0.001 |
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".