Disease-associated Streptococcus suis (DASS) in lactation: detection patterns and implications for control
Bibliographic record
Abstract
BACKGROUND: Disease-associated Streptococcus suis (DASS) refers to strains of S. suis that cause systemic infections in swine, including meningitis, septicemia, and pneumonia, resulting in significant economic losses and welfare concerns. Effective control of DASS on swine farms requires accurately detecting these pathogenic strains and identifying carrier animals and reservoirs. In this study, the dynamics of DASS colonization in clinically healthy dams, their piglets, and the surrounding environment was investigated on two commercial swine farms using novel qPCR assays: one targeting the recN gene (species-specific) and another targeting the SSU_RS01130 gene (associated with disease-causing strains). The objectives were to identify optimal sampling sites, assess the impact of dam parity, and understand the disease dynamics of DASS to provide a baseline for future studies to improve control strategies. RESULTS: Gilts and their piglets consistently exhibited higher DASS colonization compared to sows, underscoring the need for parity-based interventions. Tonsil and nasal samples were the most reliable for DASS detection in dams, while udder, fecal, and the environmental sites may serve as potential reservoirs for piglet colonization. All piglets were colonized with S. suis within 24 h of birth, but not all carried DASS; notably, a substantial proportion, particularly on Farm 1, remained DASS-negative at weaning. DASS detection in piglets decreased at day 7 and rebounded by day 21, reflecting dynamic colonization patterns. Farm-specific differences highlighted the impact of management practices and strain variation, with Farm 2 showing consistently higher DASS prevalence and persistence. Notably, the consistent absence of DASS in some litters suggests that targeted management, good hygiene, and dam-related factors such as parity can effectively reduce transmission risk. CONCLUSION: This study highlights how parity, sampling site selection, and environmental reservoirs may influence DASS colonization and persistence. This study generated valuable data that can inform future investigations aimed at improving DASS control strategies, including parity segregation, batch farrowing, maternal immunity enhancement, improved colostrum management, and hygiene protocols. Additionally, the findings support the potential refinement of Medicated Early Weaning (MEW) strategies, integrating antimicrobial use, hygiene improvements, and dam-focused interventions to reduce DASS prevalence. The novel qPCR assays offer a reliable, culture-independent surveillance tool for DASS detection, enabling veterinarians to develop evidence-based programs for early detection of, and to mitigate the impact of DASS in swine herds.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".