Fully automated and integrated multiplex detection of high consequence livestock viral genomes on a microfluidic platform
Bibliographic record
Abstract
Differential diagnosis of diseases that share common clinical signs typically requires the performance of multiple independent diagnostic tests to confirm diagnosis. Diagnostic tests that can detect and discriminate between multiple differential pathogens in a single reaction may expedite, reduce costs, and streamline the diagnostic testing workflow. Livestock haemorrhagic diseases like classical swine fever (CSF), African swine fever (ASF), and vesicular diseases, such as foot-and-mouth disease (FMD), vesicular stomatitis (VS), and swine vesicular disease (SVD) can have an enormous impact on the livestock industry and economy of countries that were previously free of the diseases. Thus, rapid diagnosis of these diseases is critical for disease control. Here, we describe the development and initial laboratory validation of a novel fully automated user-developed assay for simultaneous detection and differentiation of multiple viruses of veterinary importance in a single reaction with minimal user-intervention. The user only performs sample loading, placement of consumables and reagents, selection and initiation of assay while all other processes (i.e., nucleic acid extraction, multiplex RT-PCR, reverse dot blot detection and result reporting) are performed fully automated. The current assay has a turn-around time of approximately 6 hr and can simultaneously process up to 24 samples. The automated assay accurately and specifically detected 37 laboratory amplified strains of the five target viruses, including all seven serotypes of FMD virus, three genotypes of CSF virus, and two serotypes of VS virus. The assay also detected targeted viruses in a variety of clinical samples collected from infected animals, such as oral fluid, oral swab, nasal swab, whole blood, serum, as well as tonsil, spleen, kidney, and ileum. No cross-reactivity was observed with 15 nontarget viruses that affect livestock and samples from clinically healthy animals. To our knowledge, this is the first fully automated and integrated assay for simultaneous detection of multiple high consequence veterinary pathogens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".