Viral Quantitative Capillary Electrophoresis for Counting Intact Viruses
Bibliographic record
Abstract
The quantification of a virus plays an important role in vaccine development, clinical diagnostics, and environmental contamination assays. In all these cases, it is essential to calculate the concentration or number of intact virus particles (ivp) and estimate the degree of degradation and contamination of virus samples. In this work, we propose a cost-efficient, robust method for the quantification and characterization of intact viruses based on capillary zone electrophoresis. This separation method is demonstrated on vaccinia virus (VV) with oncolytic properties. After virus sample preparation, the solution contains intact VV as well as broken viruses and residual DNA from the host cell used for preparation. Regulatory requirements limit the amount of the host cell DNA that can be present in vaccines or human therapeutics. We apply capillary electrophoresis to separate intact virus particles and the residual DNA and to measure the level of virus contamination with DNA impurities. Intercalating YOYO-1 dye is used to detect the encapsulated and free DNA by laser-induced fluorescence. After soft lysis of VV with proteinase K, all encapsulated DNA is dissolved to the free DNA. The change in peak areas and a DNA calibration curve help determine the initial concentration of intact viruses. This viral quantitative capillary electrophoresis (Viral qCE) is able to quantify the oncolytic vaccinia virus in the range of 10(6) to 10(12) ivp/mL.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".