Efficacy of Quaternary Ammonium Compounds for Control of Individual and Mixed Cultures of <i>Escherichia coli</i> with High- and Low-Quaternary Ammonium Compounds Resistance
Bibliographic record
Abstract
Escherichia coli is a well-characterized micro-organism in scientific literature. Similarly, quaternary ammonium compounds (QACs) are historical sanitizers in food processing. However, the use of QACs has been questioned due to bacterial resistance in some studies. Therefore, this study aimed to compare effects of single and mixed cultures of E. coli strains of different serogroups with either high (six strains) or low (five strains) resistance to QACs. Twenty-five combinations of strains with either high (H)- or low (L)-QAC resistance were analyzed (H + H vs. L + L). After exposure to QAC, combinations with statistical differences (p < 0.05) compared with individuals were selected and an inactivation model determined using GInaFit®. Only one combination of two strains (C23 and C20) with low-QAC resistance (mixture T18) had greater resistance (p < 0.05) than the individual isolates. The combination T18 and individual strain C23 presented a Weibull model, whereas the other isolated strain (C20) presented a biphasic inactivation model with a shoulder. Whole genome sequencing determined that unlike C20, C23 carried yehW, which may have led to Weibull inactivation. Possibly, very rapid interaction of C20 with the QAC favored increased survival of C23 and overall persistence of the T18 mixture. Consequently, our results indicate that individual E. coli with low-QAC resistance can synergistically interfere with QAC inactivation.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".