Identification of pathogenic Helicobacter species by chaperonin-60 differentiation on plastic DNA arrays
Bibliographic record
Abstract
A microarray method for bacterial species identification based on cpn60 and 16S rDNA hybridization was developed. Specific cpn60 or 16S rDNA oligonucleotides from various Helicobacter or Campylobacter species were printed and immobilized onto a proprietary plastic solid support. Using universal primers, fragments derived from either cpn60 or 16S rDNA genes from single isolates or from a complex human waste sludge DNA sample spiked with Helicobacter pylori were biotinylated and hybridized to the plastic slide. Subsequent querying with a streptavidin-horseradish peroxidase conjugate followed by color development using tetramethylbenzidine resulted in accurate Helicobacter species identification with no cross-hybridization to either the 16S rDNA or the cpn60 sequence of a closely related strain of Campylobacter jejuni. The combination of a nonfluorescence visual detection system with a polymer-based DNA microarray slide has resulted in a molecular tool that should prove useful in numerous applications requiring rapid, low-cost bacterial species identification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".