Selection and evaluation of an efficient method for the recovery of viral nucleic acids from complex biologicals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it