Characterization of virulence potential of Pseudomonas aeruginosa isolated from bovine meat, fresh fish, and smoked fish
Bibliographic record
Abstract
Pseudomonas aeruginosa owns a variability of virulence factors. These factors can increase bacterial pathogenicity and infection severity. Despite the importance of knowledge about them, these factors are not more characterized at level of strains derived from local food products. This study aimed to characterize the virulence potential of P. aeruginosa isolated from various animal products. Several structural and virulence genes of P. aeruginosa including lasB, exoS, algD, plcH, pilB, exoU, and nan1 were detected by polymerase chain reaction (PCR) on 204 strains of P. aeruginosa. They were isolated from bovine meat (122), fresh fish (49), and smoked fish (33). The 16S rRNA gene was detected on 91.1% of the presumptive strains as Pseudomonas. The rpoB gene showed that 99.5% of the strains were P. aeruginosa. The lasB gene (89.2%) was the most frequently detected (p < 0.05). In decreasing importance order, exoS (86.8%), algD (72.1%), plcH (72.1%), pilB (40.2%), and exoU (2.5%) were detected. The lasB gene was detected in all strains of P. aeruginosa serogroups O11 and O16. The prevalence of algD, exoS, and exoU genes in these strains varied from 51.2% to 87.4%. The simultaneous determination of serogroups and virulence factors is of interest for the efficacy of surveillance of infections associated with P. aeruginosa.
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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 | 0.001 | 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".