Aquaculture Management Research Articles

Deep Vision Computing-Driven Realtime Multitarget Tracking for Intelligent Industrial Aquaculture Management

Nowadays, intelligent industrial aquaculture management has been a more universal demand in digital society. Deep learning-based vision computing technique can provide much potential for such applications. As a result, this paper proposes a deep vision computation-driven realtime multitarget tracking approach for this purpose. It is a two-stage method, in which object detection is used in the first stage and target tracking is used in the second stage. First, the improved YOLOv7 model is utilized to train and identify the preprocessed data set to achieve accurate detection of fish targets. Then, a tracking algorithm, named SORT, is utilized to conduct an in-depth analysis of fish images to achieve continuous tracking of fish targets. Thus, further management affairs can be realized upon the basis of such conditions. Experimental results show that the improved YOLOv7 model achieves a high accuracy of 92% on the target detection task, and the Sort algorithm maintains a high degree of tracking accuracy and low tracking errors between consecutive frames. By combining these two methods, the daily behavior of fishes can be accurately detected and tracked in real time. In addition, the research also explores how to combine tracking results with breeding decisions to promote the development of breeding management in an intelligent direction.

Unravelling the ontogeny and tissue-specific expression profiles of immune-related genes in the near-threatened endemic catfish, Clarias dussumieri

Clarias dussumieri, an air breathing catfish endemic to the Western Ghats in India, is categorized as 'Near Threatened' by the IUCN. This species is of high regional consumer demand and is one of the prioritized candidate species for aquaculture diversification and conservation. Despite its ecological and commercial significance, comprehensive studies on its immune system are lacking. This study elucidates the ontogenetic development and tissue-specific (brain, anterior kidney, gill, spleen, muscle, liver, hindgut and skin) expression profiles of key immune-related genes in C. dussumieri. Larvae were sampled at various developmental stages post-fertilization, and tissues from adult fish were analyzed for expression patterns of genes associated with inflammation (IL-1β, TNF-α, iNOS), antimicrobial defense (LYS, HAMP), stress response (HSP70), complement system (C3), cell-mediated immunity (MHC-IIβ, CD4-1), and adaptive immunity (IgM). The expression of IL-1β was highest at 60 days post-fertilization (60D), while TNF-α expression peaked at 25D before dropping notably by 60D. iNOS showed a peak at 7D, underscoring its importance in early immune defence. LYS exhibited a high expression at 10D, while HAMP peaked at 60D, highlighting their roles in antimicrobial defence. Stress marker HSP70 increased from 15D onwards and complement component C3 was consistently expressed at low levels throughout development. MHC-IIβ and CD4-1 showed significant increase since 10D and 7D respectively, suggesting the establishment of cell-mediated immunity. IgM expression increased notably from 15 days, indicating the development of adaptive immunity. In adult fish tissues, IL-1β, TNF-α, LYS and HSP70 showed highest expression in the hindgut, while C3 was predominantly expressed in the liver. MHC-IIβ and CD4-1 were highly expressed in the spleen and anterior kidney respectively. IgM was abundant in the anterior kidney and spleen. This study provides crucial baseline data on the immune competence of different developmental stages of C. dussumieri, informing strategies for effective vaccination and disease management in aquaculture, and enhancing our understanding of fish immunology for conservation efforts.

Behavioral, haematological, and physiological effects of oxygen-rich spray after simulated catch and release in rainbow trout (Oncorhynchus mykiss).

This study investigated the impact of an oxygen-rich spray on rainbow trout subjected to short-term air exposure episodes. These episodes can be due to sampling procedures or catch-and-release (C&R) practices, focusing on behavioral, haematological, and physiological responses. For this, 12 rainbow trout were divided into two groups: control and oxygen-rich sprayed fish. The fish were chased for 3 min and exposed to air for 6 s, and blood and behavior parameters were assessed. Simulated C&R resulted in partial physiological reflex impairment across groups. However, sprayed fish exhibited significant differences during recovery in head complex reflex and faster tendencies to regain orientation compared to control. Haematological analyses revealed that the treated group displayed reduced erythrocyte swelling and maintenance of red blood cell (RBC) counts, indicating reduced need for compensatory responses to hypoxia. Meanwhile, stress-related indicators, including cortisol, glucose, and lactate, remained unaffected by the treatment, suggesting no interference of spraying with the fish ability to launch a healthy stress response. No adverse effects were observed on skin surfaces or gills after an exploratory analysis. Overall, the oxygen-rich spray exhibited favorable effects, indicating potential benefits in protecting rainbow trout during C&R practices or sampling episodes. Moreover, there is a possible wider application of this methodology to benefit other species of fish subjected to stressors such as aquaculture, research, and fisheries management.

Conceptual Validation of High-Precision Fish Feeding Behavior Recognition Using Semantic Segmentation and Real-Time Temporal Variance Analysis for Aquaculture

Aquaculture plays an important role in the global economy. However, unscientific feeding methods often lead to problems such as feed waste and water pollution. This study aims to address this issue by accurately recognizing fish feeding behaviors to provide automatic bait casting machines with scientific feeding strategies, thereby reducing farming costs. We propose a fish feeding behavior recognition method based on semantic segmentation, which overcomes the limitations of existing methods in dealing with complex backgrounds, water splash interference, fish target overlapping, and real-time performance. In this method, we first accurately segment fish targets in the images using a semantic segmentation model. Then, these segmented images are input into our proposed fish feeding behavior recognition model. By analyzing the aggregation characteristics during the feeding process, we can identify fish feeding behaviors. Experiments show that the proposed method has excellent robustness and real-time performance, and it performs well in the case of complex water background and occlusion of fish targets. We provide the aquaculture industry with an efficient and reliable method for recognizing fish feeding behavior, offering new scientific support for intelligent aquaculture and delivering powerful solutions to improve aquaculture management and production efficiency. Although the algorithm proposed in this study has shown good performance in fish feeding behavior recognition, it requires certain lighting conditions and fish density, which may affect its adaptability in different environments. Future research could explore integrating multimodal data, such as sound information, to assist in judgment, thereby enhancing the robustness of the model and promoting the development of intelligent aquaculture.

한국 수산 양식 생산-에너지-경영 정보 상관관계 분석

The aquaculture industry in South Korea has developed advanced breeding technologies and high-quality farming techniques, contributing to the steady growth of aquaculture production since 2013. However, domestic aquaculture operations still rely on traditional farm environments, with farm managers making decisions based on experiential judgment. Despite the industry's growth, aquaculture operators face challenges in predicting market trends due to the biological complexities of the aquaculture environment and fluctuating fish prices driven by market demand. While recent research has primarily focused on the volatility of fish prices themselves, there is a lack of studies exploring other factors influencing these price fluctuations. This paper serves as a foundational study for sustainable aquaculture management, aiming to identify factors that impact fish prices through data analysis. By collecting and analyzing data on production, energy, and management within Korea's aquaculture industry, this research seeks to provide aquaculture managers with insights that will support efficient decision-making in their operations.

A Diverse Vibrio Community in Ría de Vigo (Northwestern Spain)

The genus Vibrio is genetically and ecologically diverse, resulting in severe economic losses in aquacultural macroalgae and animals. Studies on vibrios will contribute to the conservation of aquatic aquaculture in Ría de Vigo (Spain), which is famous for its shellfish farming activity. However, limited research focused on the diversity and distribution of Vibrio spp. in Ría de Vigo has been reported. Here, a slight increase in the abundance of Vibrio spp. was recorded from inshore to the open sea by quantitative PCR (qPCR). Vibrios were more abundant in the free-living (FL) than in the particle-associated (PA) fraction, showing that FL might be their preferential lifestyle. Photobacterium piscicola, Vibrio japonicus and Vibrio harveyi, which are serious pathogens for fish and invertebrates in mariculture, were found to be the dominant species across all samples by high-throughput sequencing and analyses and were mainly affected by stochastic processes. More colony-forming units and species of vibrios were recorded at 16 °C (18 species) than at 28 °C (6 species), and five isolates (5.05% of the total isolates) showed low 16S rRNA gene similarity (<98.65%) to the known strains. The results indicate that culture conditions closer to the in situ temperature may help to isolate more Vibrio species. Our findings may contribute to local aquatic aquaculture research, providing background information and novel insights for management and pathogen prevention in aquaculture, and more studies should focus on Ría de Vigo to acquire novel or pathogenic microbes in the future.

Analysis of Gut Microbiota Associated with WSSV Resistance in Litopenaeus vannamei.

Microorganisms in the digestive tract regulate the metabolism of host cells as well as stimulate the immune system of the host. If the microbiota is in good balance, it will promote the good health of the host. In this study, using 16S rRNA sequencing, we analyzed the microbiota of three groups of shrimp: a group of normal shrimp (control group), shrimp that were killed by infection with the white spot syndrome virus (WSSV) (susceptible group), and shrimp that survived WSSV infection (resistant group). The results showed that although the alpha diversity of the microbiota was barely affected by the WSSV, the bacterial communities in the three groups had different prevalences. The resistant group harbored significantly more bacteria than both the other groups. Remarkably, the resistant group had the greatest prevalence of the phylum Bacterioidetes, the families Rhodobacteraceae and Flavobacteriaceae, and the genus Nautella, suggesting their potential as biomarkers for shrimp resistance to WSSV infection. In addition, analysis of functional diversity in bacterial communities showed that the abundance of bacterial metagenomes in two groups infected with WSSV was mostly linked to metabolism and cellular processes. The susceptible WSSV group exhibited a significant reduction in amino acid metabolism. This result suggested that metabolism was the principal factor affecting the alteration in the microbiota after WSSV infection. This overview of the gut microbiota of shrimp infected with the WSSV offers crucial insights for aquaculture management and simplifies the use of control strategies in the future.

Fish-Finder: A robust small target detection method for aquaculture fish in low-quality underwater images.

Underwater fish object detection serves as a pivotal research direction in marine biology, aquaculture management, and computer vision, yet it poses substantial challenges due to the complexity of underwater environments, occultations, and the small-sized and frequently moving fish in aquaculture. Addressing these challenges, we propose a novel underwater fish object detection algorithm named Fish-Finder. First, we engendered a structure titled "C2fBF," utilizing the dual-path routing attention protocol of BiFormer. The primary objective of this structure is to alleviate the perturbations induced by underwater intricacies during the phase of downsampling in the backbone network, thereby discerning and conserving finer contextual features. Subsequently, we co-opted the RepGFPN method within our neck network-a distinctive approach that adeptly merges high-level semantic constructs with low-level spatial specifics, thus fortifying its multi-scale detection prowess. Then, in an endeavor to diminish the sensitivity toward positional aberrations during the detection of diminutive aquatic creatures, we incorporated a novel bounding box regression loss function, the Wasserstein loss, to the existing CIoU. This innovative function gauges the congruity between the predicted bounding box Gaussian distribution and the reference bounding box Gaussian distribution. Finally, in regard to the dataset, we independently assembled a specific dataset termed "SmallFish." This unique dataset, meticulously designed for the detection of small-scale fish within intricate underwater settings, includes 5000 annotated images of small fish. Experimental results demonstrate that, compared to the state-of-the-art detection methods, our proposed method improves the accuracy by and , and mean average precision (mAP) increases and in public dataset Kaggle-Fish and our SmallFish dataset, respectively.

Application of AMIS-optimized vision transformer in identifying disease in Nile Tilapia

Efficient health monitoring in Nile tilapia aquaculture is critical due to the substantial economic losses from diseases, underlining the necessity for innovative monitoring solutions. This study introduces an advanced, automated health monitoring system known as the “Automated System for Identifying Disease in Nile Tilapia (AS-ID-NT),” which incorporates a heterogeneous ensemble deep learning model using the Artificial Multiple Intelligence System (AMIS) as the decision fusion strategy (HE-DLM-AMIS). This system enhances the accuracy and efficiency of disease detection in Nile tilapia. The research utilized two specially curated video datasets, NT-1 and NT-2, each consisting of short videos lasting between 3–10 s, showcasing various behaviors of Nile tilapia in controlled environments. These datasets were critical for training and validating the ensemble model. Comparative analysis reveals that the HE-DLM-AMIS embedded in AS-ID-NT achieves superior performance, with an accuracy of 92.48% in detecting health issues in tilapia. This system outperforms both single model configurations, such as the 3D Convolutional Neural Network and Vision Transformer (ViT-large), which recorded accuracies of 84.64% and 85.7% respectively, and homogeneous ensemble models like ViT-large-Ho and ConvLSTM-Ho, which achieved accuracies of 88.49% and 86.84% respectively. AS-ID-NT provides a non-invasive, continuous, and automated solution for timely intervention, successfully identifying both healthy and unhealthy (infected and environmentally stressed) fish. This system not only demonstrates the potential of advanced AI and machine learning techniques in enhancing aquaculture management but also promotes sustainable practices and food security by maintaining healthier fish populations and supporting the economic viability of tilapia farms.

New insights into ER stress mediated by ATF6 and IRE1-XBP1 signals in yellow catfish under hypoxia

In aquaculture, fish are often exposed to the major stressor of hypoxia. However, the mechanism of hypoxia-induced stress remains unclear. The present study aimed to investigate the physiological and molecular responses of yellow catfish (Pelteobagrus fulvidraco) to hypoxia stress and to further elucidate its stress response mechanisms. Each fish (28.14 ± 0.75 g) in the hypoxia group was individually placed in a 1 L transparent plastic tank and transferred to a closed hypoxia environment, where the oxygen and carbon dioxide levels were adjusted to 2 % and 0.5 %, respectively. The results showed that hypoxia stress induced a stress response in yellow catfish, characterized by significant changes in lactate dehydrogenase (LD) and cortisol levels in plasma and liver, as well as increased apoptosis of brain cells and liver damage. Additionally, the HPA axis was activated, playing a crucial role in stress regulation during hypoxia exposure. And under hypoxia conditions, endoplasmic reticulum (ER) stress pathways were activated in yellow catfish liver, specifically showing significant upregulation of the activating transcription factor 6 (ATF6) and inositol-requiring enzyme 1 (IRE1)-X-box binding protein 1 (XBP1) signals, while the activated transcription factor 4 (ATF4) pathway remained inactive. The expression levels of atf6, ire1 and xbp1 genes, as well as ATF6, IRE1 and XBP1 proteins in the ATF6 and IRE1-XBP1 pathways, were significantly elevated. However, there was no significant differences in the expression levels of protein kinase R-like ER kinase (perk), eukaryotic translation initiation factor 2α (eif2α) and atf4 key genes, as well as PERK, EIF2α and ATF4 proteins in the PERK-ATF4 pathway. It is concluded for the first time that hypoxia significantly activates ER stress through the ATF6 and IRE1-XBP1 signaling pathways, but not the PERK-ATF4 pathway in yellow catfish. These findings not only provide new insights into the stress response mechanisms of fish but also offer the accurate molecular target of hypoxia-induced stress for aquaculture management.

SNP Polymorphisms Are Associated with Environmental Factors in Sockeye Salmon Populations Across the Northwest Pacific: Insights from Redundancy Analysis.

The SNP variation in sockeye salmon across the Asian part of its range was studied in 23 samples from 16 lake-river systems of the West Pacific Coast to improve understanding of genetic adaptation in response to spawning watersheds conditions. Identification of candidate SNPs and environmental factors that can contribute to local adaptations in sockeye salmon populations was carried out using redundancy analysis (RDA), a powerful tool for landscape genetics proven to be effective in genotype-environment association studies. Climatic and hydrographic indices (7 indices in total), reflecting abiotic conditions in freshwater habitats of sockeye salmon and characterizing the temperature regime in the river basin, its variability during the year, the amount of precipitation, as well as the height of the maximum tide in the estuary, were used as predictor factors. Among the 45 analyzed SNPs, several loci (ALDOB-135, HGFA, and RAG3-93) correlated with predictors gradients along the northwest Pacific coast were identified. The putative candidate loci localized in genes involved in the immune and inflammatory responses, as well as genes encoding temperature-sensitive enzymes and some hormones regulating ion homeostasis in fish during the anadromous migration and smoltification, were potentially associated with environmental conditions in natal rivers. The findings could have implications for aquaculture, conservation, and resource management in the context of global climate change.

Inter-species comparison of life traits related to amnesic shellfish toxin kinetic in five pectinid species

Pectinid species (scallops) hold significant economic value, but their filtration activity makes them vulnerable to harmful algal blooms, particularly Pseudo-nitzschia species producing domoic acid (DA). Domoic acid contamination can lead to amnesic shellfish poisoning in humans, causing prolonged fisheries closures and sales bans. This study aimed to compare several pectinid species to investigate if inter-specific differences in energetic traits could be linked to their ability to depurate the toxin. Using Dynamic Energy Budget (DEB) theory and the AmP multi-species estimation procedure, we analysed five species: two slow depurators (Pecten maximus and Placopecten magellanicus) and three hypothesised fast depurators (Argopecten purpuratus, Mimachlamys varia, and Nodipecten subnodosus). Results showed differences among species in assimilation rates, somatic maintenance rates, and reserve mobilisation rates but only the reserve mobilisation rates (i.e. the energy conductance parameter) consistently increased along the “slow-to-fast” depuration gradient. This research lays the groundwork for developing toxin kinetics models to quantify the processes affecting DA accumulation and depuration, and to assess the retention time of DA. Our approach and results will therefore not only be of interest to the DEB community in terms of multi-species approaches, but are likely to have applications in pectinid aquaculture and fisheries management.

Energy‐Aware Power Control Scheme For Water Quality Monitoring Systems

ABSTRACTThis research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments.

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

Aquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species.

A review of deep learning-based stereo vision techniques for phenotype feature and behavioral analysis of fish in aquaculture

The industrialization, high-density, and greener aquaculture requires a more precise and intelligent aquaculture management. Phenotypic and behavioral information of fish, which can reflect fish growth and welfare status, play a crucial role in aquaculture management. Stereo vision technology, which simulates parallax perception of the human eye, can obtain the three-dimensional phenotypic characteristics and movement trajectories of fish through different types of sensors. It can overcome the limitations in dealing with fish deformation, frequent occlusions and understanding three-dimension scenes compared to the traditional two-dimensional computer vision techniques. With the deep learning development and application in aquaculture, stereo vision has become a super computer vision technology that can provide more precise and interpretable information for intelligent aquaculture management, such as size estimation, counting and behavioral analysis of fish. Hence, it is very beneficial for researchers, managers, and entrepreneurs to possess a thorough comprehension about the fast-developing stereo vision technology for modern aquaculture. This study provides a critical review of relevant topics, including the four-layer application structure of stereo vision technology in aquaculture, various deep learning-based technologies used, and specific application scenarios. The review contributes to research development by identifying the current challenges and provide valuable suggestions for future research directions. This review can serve as a useful resource for developing future studies and applications of stereo vision technology in smart aquaculture, focusing on phenotype feature extraction and behavioral analysis of fish.

Using Machine Learning to Identify Factors of Importance in Aquaculture Management in Malaysia

Using Machine Learning to Identify Factors of Importance in Aquaculture Management in Malaysia

Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark

The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R2), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used.

Shrimp Intestinal Microbiota Homeostasis: Dynamic Interplay Between the Microbiota and Host Immunity

ABSTRACTThe shrimp intestine harbors a microbiota that has pivotal roles for host's physiology. Imbalance of shrimp intestinal microbiota has been shown closely related to the occurrence of diseases. The morphological and biological features of the shrimp intestine are considered suboptimal for stable microbial colonization, making the intestinal microbiota composition highly susceptible to the impact of environmental changes or stressors, and particularly unstable. Therefore, the relative unsteadiness of the microbiota composition represents a continuous threat to host survival. Shrimp intestinal microbiota homeostasis is achieved through a dynamic interplay between the microbiota and the host's innate immunity. The shrimp intestine possesses effective innate immune mechanisms that can suppress the uncontrolled proliferation of microbiota components, and simultaneously protect the microbiota from elimination. The mechanism(s) by which the microbial components and the intestinal innate immunity interact with each other to achieve homeostasis represents an interesting interplay between host and microbiota. This review summarizes the current knowledge about intestinal microbiota colonization in shrimp, as well as the intricate mechanisms employed by the intestinal immune system to regulate this microbiota. Moreover, the potential intervention strategies to promote and protect shrimp intestinal homeostasis by modulating the microbiota are also discussed. Thus, this review seeks to comprehensively analyze the current information and contribute to a deeper understanding of the complex interplay between the shrimp intestinal microbiota and innate immunity in maintaining shrimp intestinal homeostasis and overall health. This enhanced understanding may potentially open new avenues for aquaculture management and disease mitigation strategies, ultimately benefiting the shrimp farming industry.

Alginolyticus Virulence: An Overview

Introduction: Marine habitats harbor the gram-negative, halophilic Vibrio alginolyticus. Usually connected with aquatic life, it poses a serious health risk to humans, especially seafood and marine workers. Marine economic animals like prawns, fish, and shellfish can develop serious illnesses with rapid progression and significant fatality rates. Pathogenic relatives such as Vibrio cholerae and Vibrio parahaemolyticus, which carry genes for several virulence factors, have inherited its virulence, thereby increasing its clinical importance. No effective treatments exist to stop V. alginolyticus dissemination and infection; therefore, studying its virulence mechanisms is crucial to understanding its pathogenicity and improving prevention and therapy. Objectives: To analyze the virulence factors and antibiotic resistance mechanisms of Vibrio alginolyticus to improve prevention and control strategies for managing infections in aquaculture and public health settings. Methods: This study is a literature review synthesising findings from recent research on V. alginolyticus. Sources include peer-reviewed articles, clinical studies, and microbiological research focusing on virulence factors and antibiotic resistance. Data collection involved searching databases like PubMed, Scopus, and Web of Science, filtering for studies on V. alginolyticus’s virulence mechanisms, antibiotic resistance, and impacts on public health and aquaculture. We selected key studies to gain a comprehensive understanding of the pathogenic characteristics and resistance profiles of the bacterium. Results: The results show that V. alginolyticus uses many ways to be strong, like quorum sensing, making virulence proteins, secreting haemolysin, and making biofilms, which make it more dangerous to humans and marine animals. Several resistance genes and efflux systems contribute to its antibiotic resistance, thereby impeding the effectiveness of treatment. Studies also show that biofilm formation and quorum sensing facilitate survival in hostile environments, making infections more challenging to manage. Conclusions: Understanding the virulence factors and antibiotic resistance mechanisms of Vibrio alginolyticus is crucial for developing effective control strategies. This bacterium’s rapid spread and severe impact on marine life and human health necessitate new approaches in aquaculture management and clinical interventions. Enhanced monitoring and targeted research are essential to curb its spread and prevent infection, ultimately improving aquaculture productivity and reducing public health risks associated with this pathogen.

Unveiling Aquatic Organism Health Through eProteins: A Contemporary Perspective

Environmental DNA (eDNA) analysis has transformed our understanding of aquatic ecosystems, but traditional methods often lack insights into organismal health. The emerging field of environmental protein (eProtein) analysis offers a novel approach to monitoring the physiological status of aquatic organisms. Recent advancements in proteomic technologies have enabled the detection and characterization of stress-responsive proteins in water samples, providing valuable insights into organismal health and environmental stressors. This perspective paper explores the potential of eProtein analysis for monitoring aquatic organism health, disease dynamics and reproductive cycles. Methodological advancements in protein extraction and mass spectrometry have enhanced the sensitivity and specificity of eProtein analysis, facilitating comprehensive molecular profiling and biomarker identification. By integrating eProtein analysis into aquaculture management and environmental monitoring, researchers can proactively manage fish health, mitigate disease outbreaks and safeguard aquatic ecosystems. Future research directions include refining extraction methods, establishing standardized protocols and leveraging interdisciplinary collaborations to maximize the potential of eProtein analysis for aquatic research and conservation.