Capillary Electrophoresis Separation of Protein Composition of γ-Irradiated Food Pathogens Listeria monocytogenes and Staphylococcus aureus
Bibliographic record
Abstract
BACKGROUND: A capillary electrophoresis method using UV detection was developed to analyse protein composition of the lysates of two foodborne pathogens, Listeria monocytogenes and Staphylococcus aureus which were previously treated at different irradiation doses. METHODOLOGY AND PRINCIPAL FINDINGS: Bacterial samples were γ-irradiated at different doses to produce damage cells, to kill cells and to provoke viable but non culturable cells (VBNC) in order to evaluate the respective expression of stress proteins. In Listeria monocytogenes, two proteins (MW of 70.2 and 85.4 kDa) were significantly changed (P ≤ 0.05) at different doses of irradiation. In Staphyloccocus aureus, one protein (50 S ribosomal protein) with the MW of 16.3 kDa was significantly decreased at a low dose of irradiation treatment and the other protein (transcriptional regulator CtsR) with the MW of 17.7 kDa was increased significantly (P ≤ 0.05) at all doses of irradiation treatment compared to control. CONCLUSION: Expression of two proteins from the acyltransferase family in Listeria monocytogenes was statistically changed during irradiation treatment (P ≤ 0.05). In Staphylococcus aureus, expression of the 50 S ribosomal protein decreased and the transcriptional regulator CtsR espression increased significantly (P ≤ 0.05) following irradiation treatment. These expressed proteins do not belong to the well-known heat shock proteins family of Listeria monocytogenes and Staphylococcus aureus. The research further confirmed that capillary electrophoresis is a useful method to separate and analyse proteins expression which may be related to the resistance or sensitivity of food pathogens to γ-irradiation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".