Development of a Microelectronic Chip Array for High-Throughput Genotyping of Helicobacter Species and Screening for Antimicrobial Resistance
Bibliographic record
Abstract
A microelectronic array assay was developed to specifically genotype Helicobacter pylori versus Helicobacter heilmannii and to determine antimicrobial resistance. Helicobacter 16S rRNA and 23S rRNA genes were specifically generated with Helicobacter genus-specific primers, respectively. The single-nucleotide polymorphisms (SNPs) in 16S rRNA, 268T specific in the H. pylori sequence, and 263A specific in H. heilmannii were used as molecular markers for identification of H. pylori and H. heilmannii, respectively. A triple-base-pair resistant mutation, AGA965-967TTC in 16S rRNA, is known to be responsible for H. pylori tetracycline resistance and was detected to identify resistant strains. H. pylori macrolide resistance was determined by the identification of 3 defined mutations in the 23S rRNA gene using the same method. The assay could be directly used to detect H. pylori in feces. The assay performs multiple determinations, including identification of Helicobacter species and antibiotic resistances, on the same microelectronic platform and is highly amenable to the development of other DNA-based assays.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".