Detection and virulence of <i>Lactococcus garvieae</i> and <i>L. petauri</i> from four lakes in southern California
Bibliographic record
Abstract
OBJECTIVE: The first objective of the study aimed to detect the presence of Lactococcus petauri, L. garvieae, and L. formosensis in fish (n = 359) and environmental (n = 161) samples from four lakes near an affected fish farm in California during an outbreak in 2020. The second objective was to compare the virulence of the Lactococcus spp. in Rainbow Trout Oncorhynchus mykiss and Largemouth Bass Micropterus salmoides. METHODS: Standard bacterial culture methods were used to isolate Lactococcus spp. from brain and posterior kidney of sampled fish from the four lakes. Quantitative PCR (qPCR) was utilized to detect Lactococcus spp. DNA in fish tissues and environmental samples from the four lakes. Laboratory controlled challenges were conducted by injecting fish intracoelomically with representative isolates of L. petauri (n = 17), L. garvieae (n = 2), or L. formosensis (n = 4), and monitored for 14 days postchallenge (dpc). RESULT: Lactococcus garvieae was isolated from the brains of two Largemouth Bass in one of the lakes. Lactococcus spp. were detected in 14 fish (8 Bluegills Lepomis macrochirus and 6 Largemouth Bass) from 3 out of the 4 lakes using a qPCR assay. Of the collected environmental samples, all 4 lakes tested positive for Lactococcus spp. in the soil samples, while 2 of the 4 lakes tested positive in the water samples through qPCR. Challenged Largemouth Bass did not show any signs of infection postinjection throughout the challenge period. Rainbow Trout infected with L. petauri showed clinical signs within 3 dpc and presented a significantly higher cumulative mortality (62.4%; p < 0.0001) at 14 dpc when compared to L. garvieae (0%) and L. formosensis (7.5%) treatments. CONCLUSION: The study suggests that qPCR can be used for environmental DNA monitoring of Lactococcus spp. and demonstrates virulence diversity between the etiological agents of piscine lactococcosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".