Selection and evaluation of an efficient method for the recovery of viral nucleic acids from complex biologicals
Bibliographic record
Abstract
Abstract There is a need for a broad and efficient testing strategy for the detection of both known and novel viral adventitious agents in vaccines and biologicals. High-throughput sequencing (HTS) is an approach for such testing; however, an optimized testing method is one with a sample-processing pipeline that can help detect any viral adventitious agent that may be present. In this study, 11 commercial methods were assessed for efficient extraction of nucleic acids from a panel of viruses. An extraction strategy with two parallel arms, consisting of both the Invitrogen PureLink™ Virus RNA/DNA kit for total nucleic acid extraction and the Wako DNA Extractor® kit with an RNase A digestion for enrichment of double-stranded nucleic acid, was selected as the strategy for the extraction of all viral nucleic acid types (ssRNA, dsRNA, and dsDNA). Downstream processes, such as double-strand DNA synthesis and whole-genome amplification (WGA), were also assessed for the retrieval of viral sequences. Double-stranded DNA synthesis yielded larger numbers of viral reads, whereas WGA exhibited a strong bias toward amplification of double-stranded DNA, including host cellular DNA. The final sample-processing strategy consisted of the dual extraction approach followed by double-stranded DNA synthesis, which yielded a viral population with increased detection of some viruses by 8600-fold. Here we describe an efficient extraction procedure to support viral adventitious agent detection in cell substrates used for biological products using HTS.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".