A Sensitive Assay for Virus Discovery in Respiratory Clinical Samples
Bibliographic record
Abstract
In 5-40% of respiratory infections in children, the diagnostics remain negative, suggesting that the patients might be infected with a yet unknown pathogen. Virus discovery cDNA-AFLP (VIDISCA) is a virus discovery method based on recognition of restriction enzyme cleavage sites, ligation of adaptors and subsequent amplification by PCR. However, direct discovery of unknown pathogens in nasopharyngeal swabs is difficult due to the high concentration of ribosomal RNA (rRNA) that acts as competitor. In the current study we optimized VIDISCA by adjusting the reverse transcription enzymes and decreasing rRNA amplification in the reverse transcription, using hexamer oligonucleotides that do not anneal to rRNA. Residual cDNA synthesis on rRNA templates was further reduced with oligonucleotides that anneal to rRNA but can not be extended due to 3'-dideoxy-C6-modification. With these modifications >90% reduction of rRNA amplification was established. Further improvement of the VIDISCA sensitivity was obtained by high throughput sequencing (VIDISCA-454). Eighteen nasopharyngeal swabs were analysed, all containing known respiratory viruses. We could identify the proper virus in the majority of samples tested (11/18). The median load in the VIDISCA-454 positive samples was 7.2 E5 viral genome copies/ml (ranging from 1.4 E3-7.7 E6). Our results show that optimization of VIDISCA and subsequent high-throughput-sequencing enhances sensitivity drastically and provides the opportunity to perform virus discovery directly in patient material.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.013 |
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".