Predicción molecular de serotipos de Streptococcus suis aislados de granjas porcinas en México
Bibliographic record
Abstract
Infections caused by Streptococcus suis (S. suis) pose a problem for the pig industry worldwide. Pigs often carry multiple serotypes of S. suis in the upper respiratory tract, where S. suis is frequently isolated from. The clinical diagnosis of the infection is presumptive and is generally based on clinical signs, the age of the animal and macroscopic lesions. In the laboratory, identification of S. suis is performed biochemically, and then, serotyping is performed with antisera to determine the serotype, but these tests can be inconclusive. To date, there are few studies that have documented the presence and diversity of S. suis serotypes in Mexico. In the present study, it was characterized S. suis strains from Mexican pig farms using molecular approaches; samples were first processed by PCR of the gdh gene to detect S. suis. Positive samples were then subjected to a two-step multiplex PCR (cps PCR) to detect and characterize each strain; the first step consisted of a grouping PCR and the second step consisted of a typing PCR. The serotypes detected in the pig farming areas of Mexico included 1/2, 2, 3, 5, 7, 8, 9, 17, and 23. These findings are important for the characterization of serotypes present in Mexico and for outbreak prevention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".