Direct measurement of lipid-soluble arsenic species in biological samples with HPLC-ICPMS
Bibliographic record
Abstract
Lipid-soluble arsenicals (arsenolipids) occur in a wide range of biological samples where they may play a key role in the biosynthesis of organoarsenic compounds from inorganic arsenic. The study of these compounds has been hindered, however, by the lack of a suitable analytical technique able to separate and measure the various lipid species. As a source of arsenolipids, we used 10 crude fish oils from various regions of the world. Total arsenic analyses on the fish oils, performed with ICPMS following acid digestion with microwave-assisted heating, gave concentrations from 4.3 to 10.5 mg As kg(-1). All of the arsenic was soluble in non-polar solvents such as hexane. Analysis of the fish oils for arsenolipids was performed by normal phase HPLC-ICPMS with various mixtures of organic solvents as mobile phases. Inherent problems of instability associated with the introduction of organic solvents to the plasma were overcome by the use of reduced column flow, a chilled spray chamber, and the addition of oxygen directly to the plasma. All ten fish oils appeared to contain the same 4-6 major arsenolipids, but in varying amounts depending on the origin of the fish. Further chromatography with both normal phase and reversed-phase conditions on some of the oils indicated the presence of many more minor arsenolipids. Quantification was achieved by external calibration against triphenylarsine oxide or triphenylarsine sulfide, and the sum of species following HPLC of the oils matched well the total arsenic results (92-107%). The method was applied to samples of food supplements (fish oil capsules) and a packaged food product (cod liver) whereby arsenolipids were measured and found to be significant arsenic constituents. This study represents the first attempt to directly measure intact arsenolipids and, with appropriate sample preparation, may be suitable for quantitative measurement of these arsenicals in a range of biological samples, including foodstuffs.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".