Phylogroup and virulence gene association with clinical characteristics of <i>Escherichia coli</i> urinary tract infections from dogs and cats
Bibliographic record
Abstract
Escherichia coli isolates from infections outside the gastrointestinal tract are termed extra-intestinal pathogenic E. coli (ExPEC) and can be divided into different subpathotypes; one of these is uropathogenic E. coli (UPEC). The frequency with which UPEC strains cause urinary tract infections in dogs and cats is not well documented. We used an oligonucleotide microarray to characterize 60 E. coli isolates associated with the urinary tract of dogs ( n = 45) and cats ( n = 15), collected from 2004 to 2007, into ExPEC and UPEC and to correlate results with patient clinical characteristics. Microarray analysis was performed, and phylogroup was determined by a quadruplex PCR assay. Isolates that were missing 1 or 2 of the gene determinants representative of a function (capsule, iron uptake related genes, or specific adhesins) were designated as "non-classifiable" by microarray. Phylogroup B2 was positively associated with the UPEC subpathotype ( p < 0.0005) and negatively associated with "non-classifiable" isolates ( p < 0.0005). Phylogroup D was positively associated with ExPEC pathotype ( p = 0.025) and negatively associated with UPEC subpathotype ( p = 0.014). The ExPEC pathotype was positively associated with hospitalization for one or more days ( p = 0.031). The UPEC subpathotype was negatively associated with previous antimicrobial therapy ( p = 0.045) and previous hospitalization within the 3 mo prior to the positive culture ( p = 0.041). The UPEC subpathotype was positively associated with prostatitis ( p = 0.073) and negatively associated with current immunosuppressive therapy ( p = 0.090). Our results indicate that the case history observations may be critically important during the interpretation of laboratory results to encourage judicious use of antimicrobials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".