Molecular epidemiology and characterization of antibiotic resistance of <i>Pasteurella multocida</i> isolated from livestock population of Punjab, Pakistan
Bibliographic record
Abstract
Haemorrhagic septicaemia (HS) is an acute and life-threatening infection of livestock population caused by Pasteurella multocida (P. multocida), responsible for huge mortality, morbidity and production losses. The increase in antibiotic resistance is a growing concern, posing a significant threat to animals and public health. There is limited data on P. multocida disease burden, serotypes, antibiotic susceptibility, and resistance gene profiles in Pakistan. In the current study, 1017 nasal swabs from haemorrhagic septicaemic cattle and buffaloes were collected to isolate P. multocida through microbiological and molecular methods. Susceptibility against commonly used antibiotics was performed and antibiotic resistance genes were evaluated. A prevalence rate of 7.57% was found, where buffaloes were more prone to infection (8.3%) as compared to cows (6.7%). Molecular and sequence analysis confirmed P. multocida isolates in 94.8% (73/77) of samples. Capsular typing revealed all isolates belong to serotype B. Antibiogram analysis showed that enrofloxacin 85.7% (66/77) and ceftiofur 56/77 (72.7%) were the most effective antibiotics. The highest resistance was observed against trimethoprim/sulfamethoxazole 54/77 (70.1%), followed by erythromycin 52/77 (67.5%). Most of the isolates (31.5% (23/73)) carried β-lactamase resistance genes (blaTEM n = 10, blaROB-1 n = 6, blaOXA-2 n = 5, blaNDM n = 2) followed by trimethoprim/sulfamethoxazole (sul2) resistance genes (26% (19/73)). The current study indicates that HS is consistently circulating among the animal population in Punjab, Pakistan. The current scenario of higher resistance in P. multocida needs continuous surveillance of the infection and mass awareness programs about the non-prescribed and excessive use of antibiotics in the animal sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".