Non‐antibiotic approaches to combat motile <i>Aeromonas</i> infections in aquaculture: Current state of knowledge and future perspectives
Bibliographic record
Abstract
Abstract Inland aquaculture contributed by three major fish groups, including carps, tilapias, and catfish plays a vital role in global food security and nutrition, particularly in low and middle‐income countries. However, the sustainable development of this sector is hampered by disease epidemics, especially those caused by bacteria such as Aeromonas species. At least eight pathogenic motile Aeromonas species ( A. hydrophila , A. veronii , A. jandaei , A. caviae , A. sobria , A. bestiarum , A. dhakensis and A. schubertii ) have been reported in aquaculture with some causing up to 100% mortality during disease outbreaks. Simultaneously, emerging multidrug‐resistant Aeromonas due to a long‐inappropriate use of antibiotics is alarming and highlights a global public health concern and negative socioeconomic impacts. Here, we provide a comprehensive overview of motile Aeromonas infections, antibiotic use and antimicrobial resistance of Aeromonas species . This contribution also highlights the non‐antibiotic approaches (the solutions for preventing or treating of bacterial diseases without resorting to antibiotic use) to control motile Aeromonas infections. In addition to the current state of knowledge and limitations of each prophylaxis/therapy, perspectives for future research are discussed critically, including oral/immersion multivalent vaccines, microencapsulated synbiotics, exogenous metabolites, and novel lytic bacteriophage cocktails. Some emerging applicable nanotechnology themes such as nanovaccines, nanobioactive compounds, and nanobubbles are also included in this review. In summary, combating motile Aeromonas infections in aquaculture, including multidrug‐resistant aeromonads, as well as other bacterial diseases, is a lengthy battle that requires a strategic combination of multiple non‐antibiotic approaches coherent with the One Health philosophy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".