How does<i>Listeria monocytogenes</i>combat acid conditions?
Bibliographic record
Abstract
Listeria monocytogenes, a major foodborne pathogen, possesses a number of mechanisms that enable it to combat the challenges posed by acidic environments, such as that of acidic foods and the gastrointestinal tract. One mechanism employed by L. monocytogenes for survival at low pH is the adaptive acid tolerance response (ATR) in which a short adaptive period at a nonlethal pH induces metabolic changes that allow the organism to survive a lethal pH. Overcoming acid conditions by L. monocytogenes involves a variety of regulatory responses, including the LisRK 2-component regulatory system, the SOS response, components of the σ(B) regulon, changes in membrane fluidity, the F0F1-ATPase proton pump, and at least 2 enzymatic systems that regulate internal hydrogen ion concentration (glutamate decarboxylase and arginine deiminase). It is not clear if these mechanisms exert their protective effects separately or in concert, but it is probable that these mechanisms overlap. Studies using mutants indicate that the glutamate decarboxylase system can protect L. monocytogenes when the organism is present in acidic juices, yogurt, salad dressing, mayonnaise, and modified CO2 atmospheres. The glutamate decarboxylase system also has a role in protecting L. monocytogenes against the acidic environment of the stomach. There is a need to study other acid resistance mechanisms of L. monocytogenes to determine their effectiveness in protecting the organism in acidic foods or during transit through the acid stomach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".