Identification of new amoebae strains in rainbow trout (<i>Oncorhynchus mykiss</i>, Walbaum) farms affected by nodular gill disease (<scp>NGD</scp>) in Northeastern Italy
Bibliographic record
Abstract
Nodular gill disease (NGD) is an emerging condition associated with amoeba trophozoites in freshwater salmonid farms. However, unambiguous identification of the pathogens still must be achieved. This study aimed to identify the amoeba species involved in periodic NGD outbreaks in two rainbow trout (Oncorhynchus mykiss) farms in Northeastern Italy. During four episodes (February-April 2023), 88 fish were euthanized, and their gills were evaluated by macroscopic, microscopic and histopathological examination. The macroscopic and microscopic severity of the lesions and the degree of amoebae infestation were scored and statistically evaluated. One gill arch from each animal was put on non-nutrient agar (NNA) Petri dishes for amoeba isolation, cultivation and subsequent identification with SSU rDNA sequencing. Histopathology confirmed moderate to severe lesions consistent with NGD and mild to moderate amoeba infestation. The presence of amoebae was significantly correlated with lesion severity. Light microscopy of cultured amoebae strains and SSU rDNA analysis revealed the presence of a previously characterized amoeba Naegleria sp. strain GERK and several new strains: two strains from Hartmannelidae, three vannelid amoebae from the genus Ripella and cercozoan amoeba Rosculus. Despite the uncertainty in NGD etiopathogenesis and amoebae pathogenic role, identifying known and new amoebae leans towards a possible multi-aetiological origin.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".