Partial Characterization of Bacteriocins Produced by Lactobacillus reuteri 2-20B and Pediococcus acidilactici 0-11A Isolated from Fura, a Millet-Based Fermented Food in Ghana
Bibliographic record
Abstract
Cell-free supernatants (CFS) produced by 369 LAB strains previously isolated from fura and identified based on sequencing of their 16S rRNA genes were screened for their antagonistic activities against pathogenic bacteria including Bacillus cereus PA24, Escherichia coli SKN 541, Enterococcus faecalis 103907 CIP, Staphylococcus aureus ATCC 19095 and Listeria monocytogenes Scott A, using the agar well diffusion method. Bacteriocins of Lactobacillus reuteri 2-20B and Pediococcus acidilactici 0-11A were further evaluated for their stability when subjected to a range of pH conditions, enzymatic and heat treatments. Growth and bacteriocin production rates as well as influence of media composition on bacteriocin production were also evaluated. Cell free supernatants of Lb. reuteri and Pd. acidilactici strains exhibited the widest inhibitory activities whereas CFS of Lb. fermentum exhibited the least inhibitory activity towards the tested pathogens. Bacteriocins of Lb. reuteri 2-20B and Pd. acidilactici 0-11A retained their antibacterial activities over a wide range of pH. Whereas the antimicrobial activity of Lb. reuteri 2-20B was lost after being subjected to temperature of 90°C for 1 h, CFS of Pd. acidilactici 0-11A remained stable after autoclaving at 121°C for 15 min. Again, the antimicrobial activities of both Lb. reuteri 2-20B and Pd. acidilactici 0-11A were lost when the CFSs were subjected to the action of proteolytic enzymes but remained active under the actions of catalase, lipase and a-amylase. Production of bacteriocins by both Lb. reuteri 2-20B and Pd. acidilactici 0-11A were growth associated and influeced by media composition.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".