Some Like It Hot: Heat Resistance of Escherichia coli in Food
Bibliographic record
Abstract
Heat treatment and cooking are common interventions for reducing the numbers of vegetative cells and eliminating pathogenic microorganisms in food. Current cooking method requires the internal temperature of beef patties to reach 71 °C. However, some pathogenic Escherichia coli such as the beef isolate E. coli AW 1.7 are extremely heat resistant, questioning its inactivation by current heat interventions in beef processing. To optimize the conditions of heat treatment for effective decontaminations of pathogenic E. coli strains, sufficient estimations and explanations are necessary on mechanisms of heat resistance of target strains. The heat resistance of E. coli depends on the variability of strains and properties of food formulations including salt and water activity. Heat induces alterations of E. coli cells including membrane, cytoplasm, ribosome and DNA, particularly on proteins including protein misfolding and aggregations. Resistant systems of E. coli act against these alterations, mainly through gene regulations of heat response including EvgA, heat shock proteins, σE and σS, to re-fold of misfolded proteins, and achieve antagonism to heat stress. Heat resistance can also be increased by expression of key proteins of membrane and stabilization of membrane fluidity. In addition to the contributions of the outer membrane porin NmpC and overcome of osmotic stress from compatible solutes, the new identified genomic island locus of heat resistant performs a critical role to these highly heat resistant strains. This review aims to provide an overview of current knowledge on heat resistance of E. coli, to better understand its related mechanisms and explore more effective applications of heat interventions in food industry.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".