Identification, serotyping and antimicrobial susceptibility of Riemerella anatipestifer isolated from ducks in Vietnam
Bibliographic record
Abstract
Background: Septicaemia caused by Riemerella anatipestifer (R. anatipestifer) is a serious problem in the duck industry worldwide, and it is currently one of the major concerns for duck farming in Vietnam. Aim: This study was conducted to identify the causative agent of septicemia in ducks in Vietnam. The antimicrobial susceptibility and serotypes of R. anatipestifer isolates were also determined to provide valuable information for disease treatment and vaccine development. Methods: R. anatipestifer was isolated using blood agar and chocolate agar media. The commercial API 20NE microtest system and partial nucleotide sequence analysis of the 16s rRNA were used to identify R. anatipestifer strains. Serotypes were determined by slide agglutination test using standard antisera against R. anatipestifer. The disc diffusion method was utilized to investigate the antimicrobial susceptibility of R. anatipestifer isolated strains. Results: A total of 408 samples were collected from ducks with typical symptoms of septicemia for R. anatipestifer isolation. Sixty-nine R. anatipestifer strains were identified. Serotyping results showed that 30 out of 69 bacterial strains were classified as serotypes 1, 6, 8, 10, and 20, with serotype 10 being the most prevalent. The antimicrobial susceptibility test revealed that 100% of the bacterial isolates were susceptible to Amoxicillin/clavulanic acid and Imipenem. In contrast, the majority of R. anatipestifer strains were resistant to Nalidixic acid (89.9%), Streptomycin (75.4%), and Norfloxacin (72.5%). Conclusion: This is the first ever report in term of identification, serotyping and antimicrobial susceptibility tests of R. anatipestifer causing septicaemia in ducks of Vietnam, providing useful scientific informations for treatment as well as vaccine development to control the disease.
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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) | 0.000 | 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".