Multidrug Resistance and Distribution of <i>Salmonella</i> Serovars in Slaughtered Pigs
Bibliographic record
Abstract
The present study was undertaken to estimate the occurrence and distribution of multidrug resistance (MDR) among Salmonella serovars isolated from slaughtered pigs at Debre Zeit, Ethiopia. A total of 501 different samples were examined of which 42 (41.6%) of 101 mesenteric lymph nodes, 22 (21.8%) of 101 tongues, 17 (16.8%) of 101 caecal contents, 11 (11.1%) of 99 livers and two (2%) of 99 muscle (diaphragm and abdomen) samples were Salmonella positive. Of the 94 Salmonella isolates representing 15 different serovars, 69 (73.4%) were multidrug resistant (resistance to two or more antimicrobials). Among the Salmonella serovars a high level of MDR was observed in S. Hadar, S. Kentucky, S. Blockley and S. Enteritidis mainly to tetracycline (88.6%), streptomycin (82.9%), nitrofurantoin (74.3%), nalidixic acid and ciprofloxacin (42.9% each), sulfisoxazole (21.1%) and spectinomycin (20%). The pattern of MDR varied from two to eight antimicrobials among the resistant Salmonella serovars. The common profiles of resistance among the MDR serovars were the combined resistance to nitrofurantoin, streptomycin and tetracycline (R type NitStrTet, 51.4%), ciprofloxacin, nalidixic acid and nitrofurantoin (R type CipNalNit, 10%), ciprofloxacin, nalidixic acid, spectinomycin, streptomycin, sulfisoxazole and tetracycline (R type CipNalSptStrSulTet, 14.3%) and to ciprofloxacin, kanamycin, nalidixic acid, neomycin, nitrofurantoin, streptomycin and tetracycline (R type CipKanNalNeoNitStrTet, 10%). Results of the present study indicate the widespread occurrence and distribution of MDR Salmonella serovars in slaughtered pigs which could be a potential source of human MDR Salmonella infections.
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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".