Pre-treatment of Nile tilapia (Oreochromis niloticus) with ozone nanobubbles improve efficacy of heat-killed Streptococcus agalactiae immersion vaccine
Bibliographic record
Abstract
Nanobubble technology has shown appealing technical benefits and potential applications in aquaculture. We recently found that treatment with ozone nanobubbles (NB–O3) activated expression of several immune-related genes leading to effective response to subsequent exposure to fish pathogens. In this study, we investigated whether pre-treatment of Nile tilapia (Oreochromis niloticus) with NB-O3 can enhance specific immune responses and improve efficacy of immersion vaccination against Streptococcus agalactiae. Spleen and head kidney of fish in the vaccinated groups showed a substantial upregulation in expression levels of pro-inflammatory cytokine genes (IL-1β, TNF-α, IL-6) and immunoglobulin classes (IgM, IgD, IgT) compared with the unvaccinated control groups. The mRNA transcript of pro-inflammatory cytokine genes was greatest (approx. 2.8–3.3 folds) on day 7 post-vaccination, whereas the relative expression of immunoglobulin genes was greatest (approx. 3.2–4.1 folds) on day 21 post-immunization. Both systemic and mucosal IgM antibodies were elicited in vaccinated groups. As the result, the cumulative survival rate of the vaccinated groups was found to be higher than that of the unvaccinated groups, with a relative percent survival (RPS) ranging from 52.9 to 70.5%. However, fish in the vaccinated groups that received pre-treatment with NB-O3, bacterial antigen uptakes, expression levels of IL-1β, TNF-α, IL-6, IgM, IgD, and IgT, as well as the specific-IgM antibody levels and percent survival, were all slightly or significantly higher than that of the vaccinated group without pre-treatment with NB-O3. Taken together, our findings suggest that utilizing pre-treatment with NB-O3 may improve the immune response and efficacy of immersion vaccination in Nile tilapia.
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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) | 0.000 | 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) | 0.001 | 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".