Effect of gamma radiation on heat shock protein expression of four foodborne pathogens
Bibliographic record
Abstract
AIMS: The effects of gamma radiation on three heat shock proteins (Hsps) (GroEL, DnaK and GroES) synthesis in two Gram-negative (Escherichia coli and Salmonella serotype Typhimurium) and two Gram-positive (Staphylococcus aureus and Listeria monocytogenes) bacteria were investigated. METHODS AND RESULTS: The bacterial strains were treated with three radiation doses to induce cell damage, to obtain a viable but nonculturable state, and to cause cell death. Western blot analysis and quantification of Hsps in bacteria were performed immediately after irradiation treatment. In the four foodborne pathogens, GroEL was strongly induced by gamma rays in a dose-dependent manner, confirming the involvement of this protein in the cellular response to the stress generated by ionizing radiation. In addition, it was found that E. coli exposed to gamma radiation showed a significantly induction of DnaK and GroES proteins when compared with nonirradiated bacteria, whereas a GroES slight induction and a DnaK inhibition were observed in Salm. Typhimurium. CONCLUSIONS: The gamma rays influence the synthesis of Hsps in foodborne pathogen in a way that critically depends on the radiation dose. SIGNIFICANCE AND IMPACT OF THE STUDY: The study of stress response to several radiation doses was undertaken to elucidate how bacteria can survive in harsh conditions and cope with gamma radiation used to control foodborne pathogens and to characterize their adaptative response to this treatment.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".