Serotype diversity of Actinobacillus pleuropneumoniae detected by real-time PCR in clinical and subclinical samples from Spanish pig farms during 2017–2022
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Actinobacillus pleuropneumoniae is the causative agent of porcine pleuropneumonia, a challenging respiratory disease for the global swine industry. Variations in the serotypes associated with clinical disease have been observed in different regions worldwide. This study aimed to provide an updated epidemiological assessment of A. pleuropneumoniae serotypes in Spain, incorporating bacterial characterization through serotyping and toxinotyping. Serotypes 9/11, 2, 4, 5, 17, and 13 were frequently identified in diseased animals. Furthermore, qPCR of lung samples from an outbreak, even when samples were pooled, emerged as a robust diagnostic tool, enabling the rapid detection of A. pleuropneumoniae and their serotypes without the need for microbiological isolation. This technology also facilitates serotype monitoring of apparently healthy herds through the testing of oral fluids. The study revealed the frequent simultaneous presence of diverse serotypes within a farm. Serotypes 1, 7, 10, 12, 18, and 19 were frequently found in subclinically infected animals but were rarely detected in acute pleuropneumonia outbreaks in the current study. These results provide valuable information for interpreting the potential virulence of the different serotypes in Spain. However, other predisposing factors and the immune status of the herds such as type of vaccines used when appropriate, should be carefully considered before drawing definitive conclusions. Nevertheless, the study offers valuable insights that underscore the necessity for detailed regional data to contribute toward a comprehensive understanding of the disease dynamics and toward formulating effective control measures for porcine pleuropneumonia.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it