The Effect of Carbohydrates and Bacteriocins on the Growth Kinetics and Resistance of Listeria monocytogenes
Bibliographic record
Abstract
The aim of this study was to determine if different carbohydrates influence the growth of Listeria monocytogenes in the presence of carnocyclin A or leucocin A. Carnobacterium maltaromaticum ATCC PTA-5313 and Leuconostoc gelidum UAL187 were used to produce carnocyclin A and leucocin A, respectively. Growth curves were modelled for 5 strains of L. monocytogenes grown in basal medium supplemented with glucose, sucrose, fructose, mannose, or cellobiose, in the presence of carnocyclin A or leucocin A. The growth of L. monocytogenes to leucocin A or carnocyclin A was influenced by carbohydrate and/or strain. Carnocyclin A inhibited the growth of L. monocytogenes more than leucocin A. Growth in media containing glucose, mannose, and fructose increased the sensitivity of some strains of L. monocytogenes to bacteriocins, while growth in cellobiose and sucrose increased the resistance of L. monocytogenes to bacteriocins, as evidenced by a shorter lag phase. Strains of L. monocytogenes developed resistance to both leucocin A and carnocyclin A, but the time to develop resistance was longer when strains are treated with carnocyclin A. Carbohydrate influences the development of resistance of L. monocytogenes to the bacteriocins, but the ability of strains to develop resistance to leucocin A or carnocyclin A differs. Results of this study indicate that carbohydrates influence the ability of L. monocytogenes to grow in the presence of bacteriocins.
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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".