Detection of Avian Retroviruses in Vaccines by Amplification on DF-1 Cells with Immunostaining and Fluorescent Product-Enhanced Reverse Transcriptase Endpoint Methods
Bibliographic record
Abstract
In order to ensure the safety of vaccines produced on avian cells, rigorous testing for the absence of avian retroviruses must be performed. Current methods used to detect avian retroviruses often exhibit a high invalid-test/false-positive rate, rely on hard-to-secure reagents, and/or have readouts that are difficult to standardize. Herein, we describe the development and validation of two consistent and sensitive methods for the detection of avian retroviruses in vaccines: viral amplification on DF-1 cells followed by immunostaining for the detection of avian leukosis virus (ALV) and viral amplification on DF-1 cells followed by fluorescent product-enhanced reverse transcriptase (F-PERT) for the detection of all avian retroviruses. Both assays share an infectivity stage on DF-1 cells followed by a different endpoint readout depending on the retrovirus to be detected. Validation studies demonstrated a limit of detection of one 50% cell culture infectious dose (CCID(50))/ml for retrovirus in a 30-ml test inoculum volume for both methods, which was as sensitive as a classical method used in the vaccine industry, namely, viral amplification on primary chicken embryo fibroblasts followed by the complement fixation test for avian leukosis virus (COFAL). Furthermore, viral amplification on DF-1 cells followed by either immunostaining or F-PERT demonstrated a sensitivity that exceeds the regulatory requirements for detection of ALV strains. A head-to-head comparison of the two endpoint methods showed that viral amplification on DF-1 cells followed by F-PERT is a suitable method to be used as a stand-alone test to ensure that vaccine preparations are free from infectious avian retroviruses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".