Genotyping of Measles Virus in Clinical Specimens on the Basis of Oligonucleotide Microarray Hybridization Patterns
Bibliographic record
Abstract
An oligonucleotide microarray hybridization method for identification of most known measles virus (MV) genotypes was developed. Like the conventional genotyping method, the microarray relied on detecting sequence differences in the 450-nucleotide region coding for the COOH-terminal 150 amino acids of the nucleoprotein (N). This region was amplified using PCR primers binding to all known MV genotypes. The microarray included 71 pairs of oligonucleotide probes (oligoprobes) immobilized on glass slides. Each pair consisted of a genotype-specific oligoprobe, which matched the sequence of only one target genotype, and a control oligoprobe, which contained mismatches at the nucleotide positions unique to this genotype. A pattern recognition algorithm based on cluster analysis of the ratios of hybridization signals from specific and control oligoprobes was used to identify the specific MV genotype. Following the initial validation, the method was used for rapid genotyping of two panels of coded samples. The results of this study showed good sensitivity (90.7%), specificity (100%), and genotype agreement (91.8%) for the new method compared to the results of genotyping conducted using phylogenetic analysis of viral sequences of the C terminus of the N gene. In addition, the microarray demonstrated the ability to identify potential new genotypes of MV based on the similarity of their hybridization patterns with those of known MV genotypes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".