A miniaturized microtiter plate protocol for the determination of selenomethionine in selenized yeast via enzymatic hydrolysis of protein-bound selenium
Bibliographic record
Abstract
This paper describes a simple/low volume enzymatic extraction method for selenomethionine (SeMet) determination in selenized yeast samples. In contrast to traditional methods which generally utilize large sample volumes consuming significant amounts of costly enzymes, the modified protocol employs a microtiter plate format allowing a reduction of the required sample volumes to 1 mL per extract. The extraction is performed in a parallel (5 × 4 = 20 position microtiter plate) reaction platform made out of sintered silicon carbide, fitted with standard disposable glass HPLC/GC vials. Due to the high thermal conductivity of silicon carbide, this set-up can be placed on a standard hotplate to accurately maintain the desired extraction conditions (37 °C, 20 h) for all positions of the microtiter plate. Hydrolysis of selenium-enriched yeast with a combination of protease XIV and lipase VII (ratio 2 : 1, w/w) using these low-volume conditions provided identical results to the more traditional high-volume method. The amount of SeMet was determined by HPLC/ICPMS and confirmed a high recovery rate for SeMet (93 ± 2%, n = 3) for the certified reference material SELM-1.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".