Novel virulence, antibiotic resistance and toxin gene-specific PCR-based assays for rapid pathogenicity assessment of Arcobacter faecis and Arcobacter lanthieri
Bibliographic record
Abstract
BACKGROUND: Arcobacter faecis and A. lanthieri are two newly classified species of genus Arcobacter. The prevalence and distribution of virulence, antibiotic resistance and toxin (VAT) genes in these species are required to assess their potential pathogenic health impacts to humans and animals. This study (i) developed species- and gene-specific primer pairs for the detection of six virulence, two antibiotic resistance, and three toxin genes in two target species; (ii) optimized eight single-tube multiplex and three monoplex PCR protocols using the newly developed species- and gene-specific primers; and (iii) conducted specificity and sensitivity evaluations as well as validation of eleven mono- and multiplex PCR assays by testing A. faecis (n= 29) and A. lanthieri (n= 10) strains isolated from various fecal and agricultural water sources to determine the prevalence and distribution of VAT genes and assess the degree of pathogenicity within the two species. RESULTS: Detection of all ten and eleven target VAT genes, and expression of cytolethal distending toxin (cdtA, cdtB and cdtC) genes in A. faecis and A. lanthieri reference strains with high frequency in field isolates suggest that they are potentially pathogenic strains. These findings indicate that these two species can pose a health risk to humans and animals. CONCLUSIONS: The study results show that the developed mono- and multiplex PCR (mPCR) assays are simple, rapid, reliable and sensitive for the simultaneous assessment of the potential pathogenicity and antibiotic resistance profiling of tet(O) and tet(W) genes in these two newly discovered species. Also, these assays can be useful in diagnostic and analytical laboratories to determine the pathotypes and assessment of the virulence and toxin factors associated to human and animal infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".