Uptake, Distribution, and Speciation of Selenoamino Acids by Human Cancer Cells: X-ray Absorption and Fluorescence Methods
Bibliographic record
Abstract
Selenium compounds exhibit chemopreventative properties at supranutritional doses, but the efficacy of selenium supplementation in cancer prevention is dependent on the chemical speciation of the selenium supplement and its metabolites. The uptake, speciation, and distribution of the common selenoamino acid supplements, selenomethionine (SeMet) and Se-methylselenocysteine (MeSeCys), in A549 human lung cancer cells were investigated using X-ray absorption and fluorescence spectroscopies. X-ray absorption spectroscopy of bulk cell pellets treated with the selenoamino acids for 24 h showed that while selenium was found exclusively in carbon-bound forms in SeMet-treated cells, a diselenide component was identified in MeSeCys-treated cells in addition to the carbon-bound selenium species. X-ray fluorescence microscopy of single cells showed that selenium accumulated with sulfur in the perinuclear region of SeMet-treated cells after 24 h, but microprobe selenium X-ray absorption near-edge spectroscopy in this region indicated that selenium was carbon-bound rather than sulfur-bound. X-ray absorption and X-ray fluorescence studies both showed that the selenium content of MeSeCys-treated cells was much lower than that of SeMet-treated cells. Selenium was distributed homogeneously throughout the MeSeCys-treated cells.
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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 | 0.001 | 0.001 |
| 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".