Interpreting dual ELISA and qPCR data for bacterial kidney disease of salmonids
Bibliographic record
Abstract
Although there are a variety of methods available for the detection of Renibacterium salmoninarum, the causative agent of bacterial kidney disease in salmon and trout, the enzyme-linked immunosorbent assay (ELISA) is probably the most widely used method. However, ELISA measures bacterial antigen, which does not necessarily reflect the number of cells present. We hypothesized that dual analysis of kidney tissue by ELISA and a quantitative real-time polymerase chain reaction assay (qPCR) would provide complementary information about antigen level and the number of bacterial genomes. We found that DNA extracted from the insoluble fraction of the ELISA tissue preparation produced the same qPCR result as DNA extracted directly from frozen tissue, permitting true dual analysis of the same tissue sample. We examined kidney tissue in this manner from individual free-ranging juvenile Chinook salmon and antibiotic-treated captive subadult Chinook salmon and observed 3 different patterns of results. Among the majority of fish, there was a strong correlation between the ELISA value and the qPCR value. However, subsets of fish exhibited either low ELISA values with elevated qPCR values or higher ELISA values with very low qPCR values. These observations suggest a conceptual model that allows inferences about the state of infection of individual fish based on dual ELISA/qPCR results. Although this model requires further assessment through experimental infections and treatments, it may have utility in broodstock selection programs that currently apply egg-culling practices based on ELISA alone.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".