Antimicrobial Resistance in Malaysian Shrimp Aquaculture and Strategies to Reduce Its Occurrence
Bibliographic record
Abstract
ABSTRACT Shrimp is a commercially important species in several regions and is among the key global aquaculture commodities essential for food production and security. Similar to most shrimp‐producing countries, shrimp aquaculture in Malaysia suffers from recurring disease outbreaks that consequently impact the overall economy. The use of antimicrobial agents, particularly antibiotics, in shrimp aquaculture for prophylactic treatment and growth enhancement has increased the spread of antimicrobial‐resistant bacteria in the aquatic environment. The development and dissemination of antimicrobial‐resistant bacteria and other potential sources of antimicrobial contamination in waterways are facilitated by the continuous application of antibiotics in shrimp farming, municipalities, livestock, hospitals and pharmaceutical sources. This situation contributes to the spread of the antimicrobial resistance (AMR) phenomenon, a One Health issue with detrimental effects on human and animal health as well as the environment. Addressing the risks associated with AMR dissemination remains highly challenging due to the intensification of shrimp farming trends, which heightens disease outbreaks, and the limited availability of alternatives to antibiotics for many farmers seeking to prevent crop failure. In this review, we critically examine the key issues related to AMR in shrimp aquaculture and explore emerging treatment strategies. Our analysis encompasses a comprehensive review of the existing literature on the impact of AMR on shrimp farming in Malaysia, as well as alternative mitigation strategies aimed at fostering more sustainable and resilient aquaculture practices.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".