Impact of co‐infection with <i>Lepeophtheirus salmonis</i> and <i>Moritella viscosa</i> on inflammatory and immune responses of Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
Abstract This study was conducted to determine the effects of a co‐infection with Moritella viscosa at different exposure levels of sea lice Lepeophtheirus salmonis in Atlantic salmon ( Salmo salar ). M. viscosa (1.14 × 10 6 cfu/ml) was introduced to all experimental tanks at 10 days post‐lice infection (dpLs). Mean lice counts decreased over time in both the medium lice co‐infection (31.5 ± 19.0 at 7 dpLs; 16.9 ± 9.3 at 46 dpLs) and high lice co‐infection (62.0 ± 10.8 at 7 dpLs; 37.6 ± 11.3 at 46 dpLs). There were significantly higher mortalities and more severe skin lesions in the high lice co‐infected group compared to medium lice co‐infected group or M. viscosa ‐only infection. Quantitative gene expression analysis detected a significant upregulation of genes in skin from the high lice co‐infection group consistent with severe inflammation ( il‐8 , mmp‐9 , hep, saa ). Skin lesions retrieved throughout the study were positive for M. viscosa growth, but these were rarely located in regions associated with lice. These results suggest that while M. viscosa infection itself may induce skin lesion development in salmon, co‐infection with high numbers of lice can enhance this impact and significantly reduce the ability of these lesions to resolve, resulting in increased mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".