The Development of a Multiplex Reverse Transcription Polymerase Chain Reaction for Detection of Porcine Reproductive and Respiratory
Bibliographic record
Abstract
A multiplex reverse transcription polymerase chain reaction (multiplex RT-PCR) was developed for the detection of porcine reproductive and respiratory syndrome virus (PRRSV). A set of three pairs of primer were designed based on the sequence of high conservative region of PRRSV. The diagnostic accuracy of the multiplex RT-PCR assay was evaluated using 25 field clinical samples, 10 reference strains and 6 true-negative samples. Simultaneously, the specificity and sensitivity of this method were evaluated. The result indicated that this assay could reliably differentiate between PRRSV and other swine viral disease, such as classical swine fever virus (CSFV), swine vesicular disease virus (SVDV) and vesicular exanthema of swine virus (VESV). Meanwhile, this assay was shown to be 10-fold more sensitive than the conventional single RT-PCR (conventional sRT-PCR) method. The results indicated that the multiplex RT-PCR developed in this study has high sensitivity, strong specificity and easy operation,it is not only suitable for diagnosis of samples of insignifiance content,but also it has important applied value in getting rid of animals with recessive virus and veterinary quarantine .The method may provide a new avenue to the rapid detection of this important pathogen in one reaction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".