Effect of Bacterial Endophytes Isolated from Tropical Fruits against Listeria monocytogenes and Cronobacter sakazakii in Model Food Products
Bibliographic record
Abstract
Listeria monocytogenes and Cronobacter sakazakii are two important foodborne bacterial pathogens. Bacterial endophytes, which reside in plant cells, can produce antimicrobial compounds to protect the host organism or inhibit pathogens. This study investigated the bacterial community of tropical fruits for their potential to inactivate L. monocytogenes or C. sakazakii in cantaloupe and liquid infant formula, respectively. Tropical fruits including papayas, dragon fruits, and sugar apples, were sourced from several countries. Candidate bacterial endophytes were recovered from these tropical fruits using blood agar and Reasoner’s 2A (R2A) agar and tested for potential inhibition against L. monocytogenes and C. sakazakii. A total of 196 bacterial endophytes were recovered from papayas, dragon fruits, and sugar apples. Among these bacterial endophytes, 33 (16.8%) and 13 (6.6%) of them demonstrated an inhibition zone against L. monocytogenes and C. sakazakii, respectively. The inhibitory strains were identified using 16S rRNA sequencing as Bacillus spp., Enterobacter spp., Klebsiella spp., Microbacterium spp., Pantoea spp., and Pseudomonas spp. A cocktail of Pantoea spp. and Enterobacter spp. was used in challenge studies with cantaloupe and significantly reduced the number of L. monocytogenes by approximately 2.5 log10 CFU/g. In addition, P. stewartii demonstrated antagonistic activity against C. sakazakii in liquid infant formula, i.e., it significantly decreased the number of C. sakazakii by at least 1 log10 CFU/mL. Thus, the use of bacterial endophytes recovered from fruits and vegetables could be a promising area of research. Their use as potential biocontrol agents to control bacterial pathogens in ready-to-eat foods warrants further investigation.
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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) | 0.001 | 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".