Comparative genomic fingerprinting for the subtyping of Campylobacter jejuni and Campylobacter coli biotypes
Bibliographic record
Abstract
Introduction/Objective. Thermophilic campylobacters, especially Campylobacter jejuni (C. jejuni) and Campylobacter coli (C. coli), are the most important causes of bacterial diarrhea in developed and developing countries. The disease can occur as a sporadic infection or as large and small outbreaks. Phenotyping and genotyping methods are in use to determine similarities between strains as well their possible common origin. The goal of the study was to compare discriminatory power of biotyping tests and comparative genomic fingerprinting (CGF) 40 (100%), as well as a combination of the two tests in detection of clonality or epidemiological relatedness between the studied strains. Methods. We investigated 23 Campylobacter strains using biotyping and CGF typing. Results. We found that biotyping was a more discriminatory method for C. coli, and CGF for C. jejuni strains. In the discrimination of C. jejuni strains, CGF had better discriminatory power [Simpson?s index of diversity (ID) was 0.879] over the discrimination of C. coli strains (Simpson?s ID was 0.389). Conclusion. Biotyping and CGF can be complementary methods in detection of similarity, relatedness and possible common origin between strains since the combination of biotyping and CGF methods gives more precise data about diversity within C. coli and C. jejuni strains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".