Serotypes of Streptococcus suis isolated from healthy pigs in Phayao Province, Thailand
Bibliographic record
Abstract
BACKGROUND: Streptococcus suis (S. suis) is an important swine and human pathogen. There are 33 serotypes that have been described. Zoonotic cases are very common the Northern part of Thailand, especially in Phayao Province. However, the prevalence of S. suis and, more particularly the different serotypes, in pigs in this region is poorly known and needed to be addressed. THE CONTEXT AND PURPOSE OF THE STUDY: Distribution of S. suis serotypes varies depending on the geographical area. Knowledge of the serotype distribution is important for epidemiological studies. Consequently, 180 tonsil samples from slaughterhouse pigs in Phayao Province had been collected for surveillance, from which 196 S. suis isolates were recovered. Each isolate was subcultured and its serotype identified using multiplex PCR. Slide agglutination combined with precipitation tests were used following multiplex PCR to differentiate the isolates showing similar sizes of amplified products specific to either serotype 1 or 14 and 2 or 1/2. Non-typable isolates by multiplex PCR were serotyped by the coagglutination test. RESULTS: Of the 196 isolates, 123 (62.8%) were typable and 73 (37.2%) were non-typable. This study revealed the presence of serotypes 1, 1/2, 2, 3, 4, 5, 7, 9, 11, 12, 13, 14, 21, 22, 23, 24, 25, 29, and 30. Serotype 23 was the most prevalent (20/196, 10.2%), followed by serotype 9 (16/196, 8.2%), serotype 7 (16/196, 8.2%), and serotype 2 (11/196, 5.6%). The latter is the serotype responsible for most human cases. CONCLUSION: Almost all serotypes previously described are present in Northern Thailand. Therefore, this report provides useful data for future bacteriological studies.
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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 | 0.001 |
| 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".