From the basics to emerging diagnostic technologies: What is on the horizon for tilapia disease diagnostics?
Bibliographic record
Abstract
Abstract Tilapia is an affordable protein source farmed in over 140 countries with the majority of production in low‐ and middle‐income countries. Intensification of tilapia farming has exacerbated losses caused by emerging and re‐emerging infectious diseases. Disease diagnostics play a crucial role in biosecurity and health management to mitigate disease loss and improve animal welfare in aquaculture. Three continuous levels of diagnostics (I, II and III) for aquatic species have been proposed by Food and Agriculture Organization of the United Nations (FAO) and the Network of Aquaculture Centers in Asia and the Pacific (NACA) to promote the integration of basic and advanced methods to achieve accurate and meaningful interpretation of diagnostic results. However, the recent proliferation of cutting‐edge molecular methods applied in the diagnosis of diseases of aquacultured animals has shifted the focus of researchers and users away from basic approaches and toward molecular diagnostics, despite the fact that many diseases can be rapidly diagnosed using inexpensive, simple microscopic examination and that most emerging diseases in aquaculture were discovered by histopathology. This review, therefore, revisits and highlights the importance of the three levels of diagnostics for diseases of tilapia, particularly the frequently overlooked basic procedures (e.g., case history records, gross pathology, presumptive diagnostic methods and histopathology). The review also covers current and emerging molecular diagnostic technologies for tilapia pathogens including polymerase chain reaction methods (conventional, quantitative, digital), isothermal amplification methods Loop‐mediated Isothermal Amplification (LAMP), recombinase polymerase amplification (RPA), clustered regularly interspaced short palindromic repeats (CRISPR)‐based detection, lateral flow immunoassays, as well as discussing what is on the horizon for tilapia disease diagnostics (next generation sequencing, artificial intelligence, environmental Deoxyribonucleic Acid (DNA) and Ribonucleic Acid (RNA) and point‐of‐care testing) providing a future vision for transferring these technologies to farmers and stakeholders for a sustainable aquatic food system transformation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".