Detection of Foot-and-Mouth Disease Virus in Swine Meat Juice
Bibliographic record
Abstract
Foot-and-mouth disease virus (FMDV) is a highly contagious agent that impacts livestock industries worldwide, leading to significant financial loss. Its impact can be avoided or minimized if the virus is detected early. FMDV detection relies on vesicular fluid, epithelial tags, swabs, serum, and other sample types from live animals. These samples might not always be available, necessitating the use of alternative sample types. Meat juice (MJ), collected after freeze-thaw cycles of skeletal muscle, is a potential sample type for FMDV detection, especially when meat is illegally imported. We have performed experiments to evaluate the suitability of MJ for FMDV detection. MJ was collected from pigs that were experimentally infected with FMDV. Ribonucleic acid (RNA) was extracted from MJ, sera, oral swabs, and lymph nodes from the same animals and tested for FMDV by real-time reverse transcription polymerase chain reaction (rRT-PCR). MJ was also tested for FMDV antigen by Lateral Flow Immunoassay (LFI). FMDV RNA was detected in MJ by rRT-PCR starting at one day post infection (DPI) and as late as 21 DPI. In contrast, FMDV RNA was detected in sera at 1-7 DPI. Antigen was also detected in MJ at 1-9 DPI by LFI. Live virus was not isolated directly from MJ, but was recovered from the viral genome by transfection into susceptible cells. The data show that MJ is a good sample type for FMDV detection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".