Ozone nanobubble treatments improve survivability of Nile tilapia ( <i>Oreochromis niloticus</i> ) challenged with a pathogenic multi‐drug‐resistant <i>Aeromonas hydrophila</i>
Bibliographic record
Abstract
Abstract A rapid increase in multi‐drug‐resistant (MDR) bacteria in aquaculture highlights the risk of production losses due to diseases and potential public health concerns. Previously, we reported that ozone nanobubbles (NB‐O 3 ) were effective at reducing concentrations of pathogenic bacteria in water and modulating fish immunity against pathogens; however, multiple treatments with direct NB‐O 3 exposures caused alterations to the gills of exposed fish. Here, we set up a modified recirculation system (MRS) assembled with an NB‐O 3 device (MRS‐NB‐O 3 ) to investigate whether MRS‐NB‐O 3 (a) were safe for tilapia ( Oreochromis niloticus ), (b) were effective at reducing bacterial load in rearing water and (c) improved survivability of Nile tilapia following an immersion challenge with a lethal dose of MDR Aeromonas hydrophila . The results showed no behavioural abnormalities or mortality of Nile tilapia during the 14‐day study using the MRS‐NB‐O 3 system. In the immersion challenge, although high bacterial concentration (~2 × 10 7 CFU/ml) was used, multiple NB‐O 3 treatments in the first two days reduced the bacteria between 15.9% and 35.6% of bacterial load in water, while bacterial concentration increased from 13.1% to 27.9% in the untreated control. There was slight up‐regulation of non‐specific immune‐related genes in the gills of the fish receiving NB‐O 3 treatments. Most importantly, this treatment significantly improved survivability of Nile tilapia with relative percentage survival (RPS) of 64.7% – 66.7% in treated fish and surviving fish developed specific antibody against MDR A. hydrophila . In summary, the result suggests that NB‐O 3 is a promising non‐antibiotic approach to control bacterial diseases, including MDR bacteria, and has high potential for application in recirculation aquaculture system (RAS).
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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 | 0.000 | 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 | 0.000 |
| 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 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".