Use of repetitive sequence-based polymerase chain reaction for molecular epidemiologic analysis of Streptococcus equi subspecies equi
Bibliographic record
Abstract
OBJECTIVE: To determine whether repetitive sequence-based polymerase chain reaction (rep-PCR) could be used to differentiate Streptococcus equi isolates, to examine S equi isolates from throughout the world, and to determine whether a horse had > 1 subtype of S equi during an outbreak of disease. SAMPLE POPULATION: An initial group of 32 S equi isolates, 63 S equi isolates from various geographic areas, and 17 S equi isolates obtained during outbreaks of disease. PROCEDURE: An aliquot of S equi genomic DNA was amplified, using enterobacterial repetitive intergenic consensus primers. Gel electrophoresis was performed on 1.5% agarose gels, and a computed-assisted program was used to compare rep-PCR results. RESULTS: Use of these primers to analyze 100 ng of S equi genomic DNA resulted in patterns of 6 to 14 bands. The 32 initial isolates were separated into 7 rep-PCR subtypes. There were 30 rep-PCR subtypes found among 29 S equi isolates obtained from Minnesota, Michigan, Canada, and Australia and 34 S equi isolates obtained from Kentucky and other sources. Furthermore, the same clone was identified in several horses during an outbreak of disease. Infected horses on the same farm all had a single clone of S equi. CONCLUSION AND CLINICAL RELEVANCE: Analysis of these results suggests that rep-PCR is useful for delineating S equi into rep-PCR subtypes. Results revealed that isolates with the same geographic source or similar date of collection did not always have the same rep-PCR subtype. A single clone of S equi usually predominated during an outbreak of disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.001 |
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 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".