<i>Edwardsiella ictaluri</i>: A systemic review and future perspectives on disease management
Bibliographic record
Abstract
Abstract Edwardsiella ictaluri , a non‐zoonotic Gram‐negative bacterium, has been known to science for more than 4 decades. It was reported for the first time in 1979 in Ictalurus punctatus in the USA and later in Pangasianodon hypophthalmus and Pelteobagrus fulvidraco in Asia. Even though catfish species are more susceptible to E. ictaluri , other fish species are also affected, and up to 44 fish species in four continents are known to be susceptible. The diseases caused by E. ictaluri are known as enteric septicaemia of catfish (ESC) in channel catfish, bacillary necrosis of pangasius (BNP) in striped catfish, red head disease in yellow catfish and edwardsiellosis in tilapia. Outbreaks caused by E. ictaluri can cause up to 100% mortality resulting in substantive economic losses to the industry, threatening food security and undermining sustainability. Although efforts have been made to prevent and control this pathogen using vaccines, antibiotics, disease resistance selective breeding, functional feed ingredients, prebiotics and probiotics, and biosecurity measures, E. ictaluri is still causing health issues in different countries. Here, we provided with a comprehensive review that addressed the current knowledge of E. ictaluri bacteriological characteristics, epidemiology, pathogenesis, diagnosis, control and management. Furthermore, we also provided the future perspectives based on advanced technologies and biosecurity management in aquaculture to assist pathogen control and/or eradication.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 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".