Speciation of selenium in cells by HPLC-ICP-MS after (on-chip) magnetic solid phase extraction
Bibliographic record
Abstract
Elemental speciation in cells is significant for metallomics research. In this study, novel methods of (on-chip) magnetic solid phase extraction (MSPE) combined with high performance liquid chromatography (HPLC)-inductively coupled plasma mass spectrometry (ICP-MS) were proposed for selenium speciation in selenium-enriched yeast cells. An integrated microfluidic chip consisting of reaction, mixing, and extraction units was designed and fabricated for on-chip MSPE. Sulfonated polystyrene-coated magnetic nanoparticles (Fe3O4@PSS MNPs) were prepared as adsorption material for MSPE of selenoamino acids and selenopeptide. The factors affecting the extraction performance of the target selenium species by (on-chip) MSPE-HPLC-ICP-MS were systematically investigated. The analytical performance of the (on-chip) MSPE-HPLC-ICP-MS was evaluated under individual optimal conditions. The limits of detection for five target selenium species were 0.025 μg L−1 to 0.090 μg L−1 and 0.057 μg L−1 to 0.149 μg L−1 for MSPE-HPLC-ICP-MS and on-chip MSPE-HPLC-ICP-MS, respectively. The MSPE-HPLC-ICP-MS method is sensitive, fast, easy-to-operate, and economical. The on-chip MSPE-HPLC-ICP-MS method has the unique advantages of low sample consumption and high integration; thus, it is suitable for selenium speciation in a small number (∼800) of selenium-enriched yeast cells. A Certified Reference Material of SELM-1 yeast was used to validate the accuracy of the developed (on-chip) MSPE-HPLC-ICP-MS methods. The proposed methods were successfully applied to the speciation of selenium in selenium-enriched yeast cells. Analysis of approximately 800 cells by on-chip MSPE-HPLC-ICP-MS revealed that the average amounts of selenocystine (SeCys2) and selenomethionine (SeMet) in a single selenium-enriched yeast cell are in the order of subpicograms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".