Aeromonas hydrophila RIT668 and Citrobacter portucalensis RIT669—Potential Zoonotic Pathogens Isolated from Spotted Turtles
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is one of the biggest challenges of the 21st century, and biofilm formation enables bacteria to resist antibiotic at much higher concentrations than planktonic cells. Earlier, we showed that the Gram-negative Aeromonas hydrophila RIT668 and Citrobacter portucalensis RIT669 (closely related to C. freundii NBRC 12681) from infected spotted turtles (Clemmys guttata), formed biofilms and upregulated toxin expression on plastic surfaces, and were predicted to possess multiple antibiotic resistance genes. Here, we show that they each resist several antibiotics in the planktonic phase, but were susceptible to neomycin, and high concentrations of tetracycline and cotrimoxazole. The susceptibility of their biofilms to neomycin and cotrimoxazole was tested using the Calgary device. For A. hydrophila, the minimum inhibitory concentration (MIC) = 500–1000, and the minimum biofilm eradication concentration (MBEC) > 1000 μg/mL, using cotrimoxazole, and MIC = 32.3–62.5, and MBEC > 1000 μg/mL, using neomycin. For C. freundii MIC = 7.8–15.6, and, MBEC > 1000 μg/mL, using cotrimoxazole, and MIC = 7.8, and MBEC > 1000 μg/mL, using neomycin. Both A. hydrophila and C. portucalensis activated an acyl homoserine lactone (AHL) dependent biosensor, suggesting that quorum sensing could mediate biofilm formation. Their multidrug resistance in the planktonic form, and weak biofilm eradication even with neomycin and cotrimoxazole, indicate that A. hydrophila and C. portucalensis are potential zoonotic pathogens, with risks for patients living with implants.
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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 | 0.001 |
| Research integrity | 0.001 | 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".