Rapid Identification of <i>Escherichia coli</i> Pathotypes by Virulence Gene Detection with DNA Microarrays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One approach to the accurate determination of the pathogenic potential (pathotype) of isolated Escherichia coli strains would be through a complete assessment of each strain for the presence of all known E. coli virulence factors. To accomplish this, an E. coli virulence factor DNA microarray composed of 105 DNA PCR amplicons printed on glass slides and arranged in eight subarrays corresponding to different E. coli pathotypes was developed. Fluorescently labeled genomic DNAs from E. coli strains representing known pathotypes were initially hybridized to the virulence gene microarrays for both chip optimization and validation. Hybridization pattern analysis with clinical isolates permitted a rapid assessment of their virulence attributes and determination of the pathogenic group to which they belonged. Virulence factors belonging to two different pathotypes were detected in one human E. coli isolate (strain H87-5406). The microarray was also tested for its ability to distinguish among phylogenetic groups of genes by using gene probes derived from the attaching-and-effacing locus (espA, espB, tir). After hybridization with these probes, we were able to distinguish E. coli strains harboring espA, espB, and tir sequences closely related to the gene sequences of an enterohemorrhagic strain (EDL933), a human enteropathogenic strain (E2348/69), or an animal enteropathogenic strain (RDEC-1). Our results show that the virulence factor microarray is a powerful tool for diagnosis-based studies and that the concept is useful for both gene quantitation and subtyping. Additionally, the multitude of virulence genes present on the microarray should greatly facilitate the detection of virulence genes acquired by horizontal transfer and the identification of emerging pathotypes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 it