Cross-protective effect of acid adaptation on ethanol tolerance in Salmonella Enteritidis
Bibliographic record
Abstract
Cross protection can undermine the effectiveness of control measures on foodborne pathogens, and therefore brings major implications for food safety. In this work, the capacity of Salmonella Enteritidis to mount ethanol tolerance following acid adaptation was characterized by analysis of cell viability and cell membrane property. It was observed that preadaptation to pH 4.5 significantly (P < 0.05) increased the tolerance of log-phase cells to ethanol; in contrast, stationary-phase cells displayed reduced ethanol tolerance after acid adaptation. However, acid adaptation did not cause cell leakage and morphological change in both log-phase and stationary-phase S. Enteritidis. Fatty acid analysis further revealed that the amount of C14:0, C17:0 cyclo and C19:0 cyclo fatty acids was increased, while that of C16:1ω7c and C18:1ω7c fatty acids was decreased, respectively, in response to acid adaptation, regardless of bacterial growth phase. Notably, acid adaptation significantly (P < 0.05) increased the proportion of C16:0 fatty acid in log-phase cells, but this effect did not occur in stationary-phase cells. Moreover, exogenous addition of C16:0 fatty acid to stationary-phase acid-adapted cultures was able to enhance bacterial ethanol tolerance. Taken together, C16:0 fatty acid is involved in the growth-phase-dependent protective effect of acid adaptation on ethanol tolerance in S. Enteritidis.
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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 | 0.000 |
| 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".