Coinfection takes its toll: Sea lice override the protective effects of vaccination against a bacterial pathogen in Atlantic salmon
Bibliographic record
Abstract
Vaccination is considered crucial for disease prevention and fish health in the global salmon farming industry. Nevertheless, some aspects, such as the efficacy of vaccines, can be largely circumvented during natural coinfections. Sea lice are ectoparasitic copepods that can occur with a high prevalence in the field, are frequently found in co-infection with other pathogens, and are highly detrimental to fish health. The aim of this case-control study was to evaluate the interaction between the detrimental effects of coinfection and the protective effects of vaccination in fish. We used the interaction between the sea louse Caligus rogercresseyi, the bacterial pathogen Piscirickettsia salmonis, and their host, the Atlantic salmon Salmo salar, as a study model. Our results showed that coinfection decreased the accumulated survival (AS) and specific growth rate (SGR) of vaccinated fish (AS = 5.2 ± 0.6%; SGR = -0.05 ± 0.39%) compared to a single infection of P. salmonis (AS = 42.7 ± 1.3%; SGR = 0.21 ± 0.22%). Concomitantly, the bacterial load and clinical signs of disease were significantly increased in coinfected fish. Coinfection may explain the reduced efficacy of vaccines in sea cages and highlights the need to test fish vaccines in more diverse conditions rather than with a single infection.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".