Robust hybridization-based genotyping probes for HPV 6, 11, 16 and 18 obtained via in vitro selection
Bibliographic record
Abstract
This paper describes the technical and analytical performance of a novel set of hybridization probes for the four GARDASIL® vaccine-relevant HPV types (6, 11, 16 and 18). These probes are obtained through in vitro selection from a pool of random oligonucleotides, rather than the traditional “rational design” approach typically used as the initial step in assay development. The type-specific segment of the HPV genome was amplified using a GP5+/6+ PCR protocol and 39 synthetic oligonucleotide templates derived from each of the HPV types, as PCR targets. The robust performance of the 4 selected hybridization probes was demonstrated by monitoring the preservation of the specificity and sensitivity of the typing assay over all 39 HPV types, using a different spectrum of HPV (genome equivalent: 103-109) and human DNA concentrations (10-100 ng) as well as temperature and buffer composition variations. To the Authors’ knowledge, this is a unique hybridization-based multiplex typing assay. It performs at ambient temperatures, does not require the strict temperature control of hybridization conditions, and is functional with a number of different non-denaturing buffers, thereby offering downstream compatibility with a variety of detection methods. Studies aimed at demonstrating clinical performance are needed to validate the applicability of this strategy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".