Probing the interaction of arsenobetaine with blood plasma constituents in vitro: an SEC-ICP-AES study
Bibliographic record
Abstract
Arsenobetaine, which is frequently ingested by humans via the consumption of seafood, is rapidly excreted unchanged in urine, but not much is known about its transport in the mammalian bloodstream. To assess whether this transport involves binding to plasma proteins, rabbit and human plasma were spiked with arsenobetaine and the mixture was analyzed (after 5 min and again after 6 h) by size-exclusion chromatography (SEC) coupled on-line to an inductively coupled plasma atomic emission spectrometer (ICP-AES). Simultaneous monitoring of the emission lines of As, Cu, Fe and Zn in the column effluent allowed us to determine the elution of arsenobetaine relative to that of the major Cu, Fe and Zn-containing metalloproteins. Over the investigated time period, a single As peak eluted near the inclusion volume on two different SEC columns with fractionation ranges of 600-10 KDa and 7000-100 Da. These results indicate that arsenobetaine did not bind to plasma proteins and that SEC-ICP-AES is a useful tool to rapidly probe toxicologically and pharmacologically-relevant interactions between organometalloid compounds and mammalian blood plasma constituents in vitro.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".