Evaluation of different strategies for determination of selenomethionine (SeMet) in selenized yeast by asymmetrical flow field flow fractionation coupled to inductively coupled plasma mass spectrometry (AF4-ICP-MS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This manuscript exemplifies the prospective use of asymmetrical flow field flow fractionation (AF4) coupled to inductively coupled plasma mass spectrometry (ICP-MS) as a simple tool for chemical speciation of selenomethionine (SeMet) in selenized yeast. Several popular sample preparation methods were evaluated for their suitability to determine selenomethionine (SeMet) in selenized yeast by AF4-ICP-MS. These included water, methanesulfonic acid (MSA), formic acid (FA) and alkaline extractions. Alkaline extraction (using sodium dodecyl sulfate buffer) provided the best recovery/determination conditions for SeMet based on analysis of NRC certified reference material (CRM) SELM-1 since it minimized hydrolysis of the protein peptide bonds optimally required for the AF4 separation. The analytical performance of three different AF4 membranes (5, 10 and 500 kDa regenerated cellulose) was also evaluated. No significant difference in the recovery of SeMet was observed when using 5 and 10 kDa RC membranes, whereas the 500 kDa membrane resulted in a significant loss. The proposed method presents appropriate instrument and intra-assay precisions of 4.4-9.2% and 3.8% RSD, respectively, a detection limit of 0.49 μg L-1 SeMet as Se and good linearity with correlation coefficients (R) between 0.996 - 0.999. This is the first report of use of AF4-ICP-MS for species specific quantitation of SeMet in selenized yeast demonstrating its efficient use as an alternative method to other traditional chromatographic techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it