Co-Infection by LF-89-Like and EM-90-Like Genogroups of Piscirickettsia Salmonis in Farmed Atlantic Salmon in Chile: Implications for Surveillance and Control of Piscirickettsiosis
Bibliographic record
Abstract
Piscirickettsiosis (SRS), caused by Piscirickettsia salmonis, is the main infectious disease that affects farmed Atlantic salmon in Chile. Currently, the official surveillance and control plan for SRS in Chile is based only on the detection of P. salmonis, but neither of its genogroups (LF-89-like and EM-90-like) are included. Surveillance at the genogroup level is essential not only for defining and evaluating the vaccination strategy against SRS, but it is also of utmost importance for early diagnosis, clinical prognosis in the field, treatment, and control of the disease. The objectives of this study were to characterize the spatio-temporal distribution of P. salmonis genogroups using genogroup-specific real-time probe-based polymerase chain reaction (qPCR) to discriminate between LF-89-like and EM-90-like within and between seawater farms, individual fish, and tissues/organs during early infection in Atlantic salmon under field conditions. The spatio-temporal distribution of LF-89-like and EM-90-like was shown to be highly variable within and between seawater farms. P. salmonis infection was also proven to be caused by both genogroups at farm, fish, and tissue levels. Our study demonstrated for the first time a complex co-infection by P. salmonis LF-89-like and EM-90-like in Atlantic salmon. Liver nodules (moderate and severe) were strongly associated with EM-90-like infection, but this phenotype was not detected by infection with LF-89-like or co-infection of both genogroups. The detection rate of P. salmonis LF-89-like increased significantly between 2017 and 2021 and was the most prevalent genogroup in Chilean salmon aquaculture during this period. Lastly, a novel strategy to identify P. salmonis genogroups based on novel genogroup-specific qPCR for LF-89-like and EM-90-like genogroups is suggested.
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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 | 0.000 | 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) | 0.001 | 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".