Novel High-throughput Approach for Purification of Infectious Virions
Bibliographic record
Abstract
Viruses are extensively studied as pathogens and exploited as molecular tools and therapeutic agents. Existing methods to purify viruses such as gradient ultracentrifugation or chromatography have limitations, for example demand for technical expertise or specialized equipment, high time consumption, and restricted capacity. Our laboratory explores mutations in oncolytic reovirus that could improve oncolytic activity, and makes routine use of numerous virus variants, genome reassortants, and reverse engineered mutants. Our research pace was limited by the lack of high-throughput virus purification methods that efficiently remove confounding cellular contaminants such as cytokines and proteases. To overcome this shortcoming, we evaluated a commercially available resin (Capto Core 700) that captures molecules smaller than 700 kDa. Capto. Core 700 chromatography produced virion purity and infectivity indistinguishable from CsCl density gradient ultracentrifugation as determined by electron microscopy, gel electrophoresis analysis and plaque titration. Capto Core 700 resin was then effectively adapted to a rapid in-slurry pull-out approach for high-throughput purification of reovirus and adenovirus. The in-slurry purification approach offered substantially increased virus purity over crude cell lysates, media, or high-spin preparations and would be especially useful for high-throughput virus screening applications where density gradient ultracentrifugation is not feasible.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".