Analysis of <i>Staphylococcus</i> enterotoxin B using differential isotopic tags and liquid chromatography quadrupole ion trap mass spectrometry
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
Staphylococcus aureus produces enterotoxins, which are causative agents of foodborne intoxications. Enterotoxins are single-chain polypeptides and have a molecular weight of about 26-28 kDa. The consumption of food contaminated with Staphylococcus aureus enterotoxins results in the onset of acute gastroenteritis within 2-6 h. The objective of this study was the development of a new method for the quantification of Staphylococcal enterotoxin B (SEB) in food matrices. Tryptic peptide map was generated and nine proteolytic fragments were clearly identified (sequence coverage of 35%). Among these, three specific tryptic peptides were selected to be used as surrogate peptides and internal standards for quantitative analysis using an isotopic tagging strategy along with analysis by LC-MS/MS. The linearity of the measurement by LC-MS/MS was evaluated by combining mixtures of both isotopes at 0.1, 0.2, 0.5, 1.0 and 2.0 ¹H/²H molar ratios with a slope near to 1, values of R² above 0.98 and %CV obtained from six repeated measurement was below 8%. The precision and accuracy of the method were assessed using SEB spiked in chicken meat homogenate samples. SEB was fortified at 0.2, 1 and 2 pmol/g. The accuracy results indicated that the method can provide accuracy within a 84.9-91.1% range. Overall, the results presented in this manuscript show that proteomics-based methods can be effectively used to detect, confirm and quantify SEB in food matrices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it