A pilot study on the effects of in-feed probiotic <i>Lactobacillus rhamnosus</i> ATCC 53103 (LGG) on vaccinated Atlantic salmon (<i>Salmo salar</i>): microbiomes and <i>Aeromonas salmonicida</i> challenge resilience
Bibliographic record
Abstract
The use of probiotics is an alternative approach to mitigate the proliferation of antimicrobial resistance in aquaculture. In our study, we examined the effects of Lactobacillus rhamnosus GG (ATCC 53103, LGG) delivered in-feed on the weight, length, skin mucus, and faecal microbiomes of Atlantic salmon. We also challenged the salmon with Aeromonas salmonicida 2004-05MF26 (Asal2004) and assessed the mortality. Our results showed no significant change ( P > 0.05) in weight or length of Atlantic salmon or their resilience to Asal2004 infection after LGG feeding. Infection changed significantly the skin mucus and faecal microbiomes: Clostridium sensu stricto increased from 3.14% to 9.20% in skin mucus and 1.39% to 3.74% in faeces ( P < 0.05). Aeromonas increased from 0.02% to 0.60% in faeces ( P < 0.05). Photobacterium increased from not detected (0%) to 52.16% ( P < 0.01) and Aliivibrio decreased from 67.21% to 0.71% in faeces ( P < 0.01). After infection, Lactococcus (9.93%) and Lactobacillus (2.11%) in skin mucus of the LGG group were significantly higher ( P < 0.05) than in the skin mucus from the rest of the groups (4.14% and 1.08%, respectively). In conclusion, LGG feeding did not further increase the resilience of vaccinated Atlantic salmon. Asal2004 infection had much greater impact on skin mucus and faecal microbiomes than LGG feeding.
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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) | 0.001 | 0.001 |
| 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".