Serology in Finfish for Diagnosis, Surveillance, and Research: A Systematic Review
Bibliographic record
Abstract
Historically, serological tests for finfish diseases have been underused when compared with their use in terrestrial animal health. For years the nonspecific immune response in fish was judged to make serology unreliable and inferior to the direct measurement of agent analytes. We conducted a systematic review of peer-reviewed publications that reported on the development, validation, or application of serological tests for finfish diseases. A total of 168 articles met the screening criteria; most of them were focused on salmonid pathogens (e.g., Aeromonas spp. and viral hemorrhagic septicemia virus). Before the 1980s, most publications reported the use of agglutination tests, but our review indicates that enzyme-linked immunosorbent assay (ELISA) has more recently become the dominant serological test. The main application of serological tests has been in the assessment of vaccine efficacy, with few applications for surveillance or demonstration of freedom from disease, despite the advantages of serological tests over direct detection at the population level. Nonlethal sampling, low cost, and postinfection persistence of antibodies make serological assays the test of choice in surveillance, especially of valuable broodstock. However, their adoption has been constrained by poor characterization and validation. The number of publications in our review reporting diagnostic sensitivity and specificity of serological tests in finfish was small (n = 7). Foreseeing a wider use of serological tests in the future for diagnostic end purposes, we offer recommendations for mitigating deficiencies in the development and evaluation of serological tests, including optimization, control of nonspecific reactions, informed cutoff points, diagnostic accuracy, and serological baseline studies. Achieving these goals will facilitate greater international recognition of serological testing in programs supporting aquatic animal health. Received March 21, 2016; accepted September 24, 2016.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".