Waterborne Pathogen Detection by Use of Oligonucleotide-Based Microarrays
Bibliographic record
Abstract
A small-oligonucleotide microarray prototype was designed with probes specific for the universal 16S rRNA and cpn60 genes of several pathogens that are usually encountered in wastewaters. In addition to these two targets, wecE-specific oligonucleotide probes were included in the microarray to enhance its discriminating power within the Enterobacteriaceae family. Universal PCR primers were used to amplify variable regions of 16S rRNA, cpn60, and wecE genes directly in Escherichia coli and Salmonella enterica serovar Typhimurium genomic DNA mixtures (binary); E. coli, S. enterica serovar Typhimurium, and Yersinia enterocolitica genomic DNA mixtures (ternary); or wastewater total DNA. Amplified products were fluorescently labeled and hybridized on the prototype chip. The detection sensitivity for S. enterica serovar Typhimurium was estimated to be on the order of 0.1% (10(4) S. enterica genomes) of the total DNA for the combination of PCR followed by microarray hybridization. The sensitivity of the prototype could be increased by hybridizing amplicons generated by PCR targeting genes specific for a bacterial subgroup, such as wecE genes, instead of universal taxonomic amplicons. However, there was evidence of PCR bias affecting the detection limits of a given pathogen as increasing amounts of a different pathogen were spiked into the test samples. These results demonstrate the feasibility of using DNA microarrays in the detection of waterborne pathogens within mixed populations but also raise the problem of PCR bias in such experiments.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".