Mechanisms of pressure-mediated cell death and injury in Escherichia coli: from fundamentals to food applications
Bibliographic record
Abstract
High hydrostatic pressure is commercially applied to extend the shelf life of foods, and to improve food safety. Current applications operate at ambient temperature and 600 MPa or less. However, bacteria that may resist this pressure level include the pathogens Staphylococcus aureus and strains of Escherichia coli, including shiga-toxin producing E. coli. The resistance of E. coli to pressure is variable between strains and highly dependent on the food matrix. The targeted design of processes for the safe elimination of E. coli thus necessitates deeper insights into mechanisms of interaction and matrix-strain interactions. Cellular targets of high pressure treatment in E. coli include the barrier properties of the outer membrane, the integrity of the cytoplasmic membrane as well as the activity of membrane-bound enzymes, and the integrity of ribosomes. The pressure-induced denaturation of membrane bound enzymes results in generation of reactive oxygen species and subsequent cell death caused by oxidative stress. Remarkably, pressure resistance at the single cell level relates to the disposition of misfolded proteins in inclusion bodies. While the pressure resistance E. coli can be manipulated by over-expression or deletion of (stress) proteins, the mechanisms of pressure resistance in wild type strains is multi-factorial and not fully understood. This review aims to provide an overview on mechanisms of pressure-mediated cell death in E. coli, and the use of this information for optimization of high pressure processing of foods.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".