An in vitro selection scheme for oligonucleotide probes to discriminate between closely related DNA sequences
Bibliographic record
Abstract
Using an in vitro selection, we have obtained oligonucleotide probes with high discriminatory power against multiple, similar nucleic acid sequences, which is often required in diagnostic applications for simultaneous testing of such sequences. We have tested this approach, referred to as iterative hybridizations, by selecting probes against six 22-nt-long sequence variants representing human papillomavirus, (HPV). We have obtained probes that efficiently discriminate between HPV types that differ by 3-7 nt. The probes were found effective to recognize HPV sequences of the type 6, 11, 16, 18 and a pair of type 31 and 33, either when immobilized on a solid support or in a reverse configuration, as well to discriminate HPV types from the clinical samples. This methodology can be extended to generate diagnostic kits that rely on nucleic acid hybridization between closely related sequences. In this approach, instead of adjusting hybridization conditions to the intended set of probe-target pairs, we 'adjust', through in vitro selection, the probes to the conditions we have chosen. Importantly, these conditions have to be 'relaxed', allowing the formation of a variety of not fully complementary complexes from which those that efficiently recognize and discriminate intended from non-intended targets can be readily selected.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".