Use of real-time polymerase chain reaction for investigation of Senecavirus infection occurrence in Russia
Bibliographic record
Abstract
Senecavirus, previously known as Seneca valley virus, is an emerging virus belonging to Senecavirus genus, Picornaviridae family, that can cause idiopathic vesicular disease clinically indistinguishable from foot-and-mouth disease, vesicular stomatitis and swine vesicular disease and thereby posing a great threat for pig holdings. Recently, evidence of Senecavirus A occurrence in pig herds in such countries as Brazil, the USA, Colombia, China and Thailand has been provided in foreign literature. Accurate diagnosis is crucial for of Senecavirus infection control. Results of studying the disease situation with genodiagnostic methods in the Russian Federation are presented in the paper. Primers and probe for real-time RT-PCR described by V. L. Fowler et al. in 2017 were used but the reaction conditions were optimized. Analysis of the method for its sensitivity showed absence of cross-reactivity with other tested viruses. The developed method for virus RNA detection was used to investigate senecavirus occurrence in pig holdings in the Russian Federation. A total of 1,577 samples of biological materials collected form pigs of different ages in 112 holdings located in 37 regions of the country were tested during 2018–2020. Senecavirus was detected in one holding located in the Urals Federal Okrug. It was supposed that the infectious agent had entered the said pig holding at the time of putting of the said holding into operation in 2015 and introduction of young breeding animals imported from Canada. This is the first report on Senecavirus detection in the Russian Federation. The threat of the pathogen introduction from other countries requires further Senecavirus infection investigation and control. The developed method can be used as a potential sensitive method for the said infectious disease diagnosis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".