Viral Quantitative Capillary Electrophoresis for Counting and Quality Control of RNA Viruses
Bibliographic record
Abstract
The world of health care has witnessed an explosive boost to its capacity within the past few decades due to the introduction of viral therapeutics to its medicinal arsenal. As a result, a need for new methods of viral quantification has arisen to accommodate this rapid advancement in virology and associated requirements for efficiency, speed, and quality control. In this work, we apply viral quantitative capillary electrophoresis (viral qCE) to determine (i) the number of intact virus particles (ivp) in viral samples, (ii) the amount of DNA contamination, and (iii) the degree of viral degradation after sonication, vortexing, and freeze-thaw cycles. This quantification method is demonstrated on an RNA-based vesicular stomatitis virus (VSV) with oncolytic properties. A virus sample contains intact VSV particles as well as residual DNA from host cells, which is regulated by WHO guidelines, and may include some carried-over RNA. We use capillary zone electrophoresis with laser-induced fluorescent detection to separate intact virus particles from DNA and RNA impurities. YOYO-1 dye is used to stain all DNA and RNA in the sample. After soft lysis of VSV with proteinase K digestion of viral capsid and ribonucleoproteins, viral RNA is released. Therefore, the initial concentration of intact virus is calculated based on the gain of a nucleic acid peak and an RNA calibration curve. After additional NaOH treatment of the virus sample, RNA is hydrolyzed leaving residual DNA only, which is also calculated by a DNA calibration curve made by the same CE instrument. Viral qCE works in a wide dynamic range of virus concentrations from 10(8) to 10(13) ivp/mL. It can be completed in a few hours and requires minimum optimization of CE separation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".