Improved detection of a fish virus with a segmented genome by Real-Time RT-qPCR
Bibliographic record
Abstract
Piscine orthoreovirus (PRV) is a virus that infects farmed and wild salmonids in Norway, United\nKingdom, Ireland, Chile, United States and Canada. The virus infection is associated with heart\nand skeletal muscle inflammation (HSMI), an important disease in salmon aquaculture reported\nin Norway, Scotland, and Chile. The pathogenesis of PRV infection is still not well understood,\nand there is still controversy about its role in HSMI since some studies report high viral loads by\nRT-qPCR in both fish with and without lesions. The PRV genome comprises 10 double stranded\nRNA segments, and most diagnostic tests only target a few specific segments. Nevertheless, the\namplification of transcripts by RT-qPCR is likely to be different since the copy number of RNA\ntranscripts from each segment is in direct relation to the protein abundance. Therefore, this\nstudy aims to develop new RT-qPCR assays targeting the first five PRV genome segments and\ndetermine which transcript cycle threshold (CT) best reflects virus load in a fish tissue sample.\nThe PRV cRNA standards were created by in-vitro transcription, and the copy numbers were\nquantified. Standard curves were generated making 10-fold serial dilutions of the PRV cRNA\nover a range of 1 x 108\n to 1 x 104\n copies/ul. The standard curves had a reaction efficiency of 95%,\n92%, 91%, 99%, 90% for the L1, L2, L3, M1, M2 segments, respectively. These assays exhibited\nhigh specificity since no cross reaction with infectious salmon anemia virus (ISAV) and salmonid\nalphavirus (SAV) was observed. This study reports a highly significant difference (P<0.001) in the\nviral concentration obtained by targeting L1, L2, L3, M1, and M2 segments. Besides, a greater\nsensitivity was obtained for L2, L3 and M1 RT-qPCR assays when archived fish tissue samples\nwere tested. Therefore, this approach may improve the detection of PRV in fish tissue samples\nand suggests additional PRV-segments to the current diagnostic criteria.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".