A Multiplex Real-Time Reverse Transcription Polymerase Chain Reaction Assay With Enhanced Capacity to Detect Vesicular Stomatitis Viral Lineages of Central American Origin
Bibliographic record
Abstract
Vesicular stomatitis virus (VSV) causes a disease in susceptible livestock that is clinically indistinguishable from foot-and-mouth disease. Rapid testing is therefore critical to identify VSV and rule out FMD. We previously developed and validated a multiplex real-time reverse transcription polymerase chain reaction assay (mRRT-PCR) for detection of both VS New Jersey virus (VSNJV) and VS Indiana virus (VSIV). However, it was subsequently apparent that this assay failed to detect some VSNJV isolates in Mexico, especially in genetic group II, lineage 2.1. In order to enhance the sensitivity of the mRRT-PCR for VSNJV, parts of the assay were redesigned and revalidated using new and improved PCR chemistries. The redesign markedly improved the assay by increasing the VSNJV detection sensitivity of lineage 2.1 and thereby allowing detection of all VSNJV clades. The new assay showed an increased capability to detect VSNJV. Specifically, the new mRRT-PCR detected VSNJV in 100% (87/87) of samples from Mexico in 2006-2007 compared to 74% for the previous mRRT-PCR. Furthermore, the analytical sensitivity of the new mRRT-PCR was enhanced for VSNJV. Importantly, the modified assay had the same sensitivity and specificity for VSIV as the previously published assay. Our results highlight the challenges the large genetic variability of VSV pose for virus detection by mRRT-PCR and show the importance of frequent re-evaluation and validation of diagnostic assays for VSV to ensure high sensitivity and specificity.
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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) | 0.002 | 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 | 0.001 |
| 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".