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K P, Aswathi, Gopika M, Mohammed Afkar, Ajith K, and Manoj M. "Fish Disease Detection to Sustain Hatchery and Pond Production System." June 2023 5, no. 2 (2023): 144–53. http://dx.doi.org/10.36548/jaicn.2023.2.005.
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Diagnosis of fish disease in aquaculture is a necessary process and needs an exceptionally high level of competency to sustain hatchery and pond production systems. Developing an system to overcome the challenges faced by fish farmers in stopping the spreading the disease that leads to economic loss, is a crucial task. A crucial initial phase in preventing the spread of disease is early identification of diseases in fish. The fish disease usually propagates quickly through the water, affecting large numbers of fish and causing financial loss to the farmers. Since tilapia aquaculture is one of the methods for producing food that is expanding the quickest and has the highest export value, we’d like to know more about the fish disease that affects this sector. The research uses the pathogen-infected fish. System is developed by working perfect image processing and machine learning techniques together. The proposed work has two phase. Image pre-processing has been used in the first phase to, respectively, reduce distortion and magnify the image. In the second section, the system extracts the relevant information and uses machine learning approaches for recognising the diseases. A trained machine learning model has been deployed to the first fraction's processed images. Then, using the chosen fish image dataset to study the fish disease, the research integrates an extensive experiment combining different methods.
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Afridiansyah, Rahmanda, and De Rosal Ignatius Moses Setiadi. "Comparison of EfficientNetB1 Model Effectiveness in Identifying Fish Diseases in South Asian Fish Diseases and Salmon Fish Diseases." Journal of Applied Informatics and Computing 8, no. 2 (2024): 453–62. https://doi.org/10.30871/jaic.v8i2.8677.
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The purpose of this study is to evaluate the effectiveness of the EfficientNetB1 model in identifying fish diseases across two distinct datasets: South Asian Fish Diseases and Salmon Fish Diseases. The South Asian Fish Diseases dataset includes seven categories: red bacterial disease, aeromoniasis, gill bacterial disease, fungal saprolegniasis, parasitic disease, and white tail viral disease. The Salmon dataset is divided into two parts: FreshFish and InfectedFish. Using the EfficientNetB1 algorithm, each dataset was separately trained and tested to predict species and disease. Results showed an accuracy of 98.14% for the South Asian Fish Diseases dataset and 99.18% for the Salmon Diseases dataset. These findings support the argument that the model possesses sufficient capability to detect diseases affecting various fish species. This suggests that the model could be a valuable tool in the aquaculture industry for disease management and detection strategies.
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Son, Hyun Seung, Han-kyu Lim, and Han Suk Choi. "A Study on Disease Prediction of Paralichthys Olivaceus using Deep Learning Technique." Korean Institute of Smart Media 11, no. 4 (2022): 62–68. http://dx.doi.org/10.30693/smj.2022.11.4.62.
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To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.
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Tiwari, Ayush. "Aqua Health Vision." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46963.
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Abstract— The "Aqua Health Vision" is aimed at forecasting fish disease diagnosis and to overcome various challenges in it has still not achieved any success. Conventional techniques for detecting diseases in fish, such as visual observations and laboratory analysis, are frequently less efficient and labor-intensive. With the evolution gained through technology, Artificial Intelligence (AI) and Machine Learning (ML) are becoming powerful tools to enhance detection and management of fish diseases.This paper discusses how these technologies are used to detect the fish diseases and presents various means of recovering from these diseases. In aquaculture, AI system can be used to analyze vast amounts of data, identify patterns and make decisions or predictions. Keywords— Fish Disease ,Deep Learning ,Aquaculture ,YOLO-V9.
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Tamut, Hayin, Robin Ghosh, Kamal Gosh, and Md Abdus Salam Siddique. "Enhancing Disease Detection in the Aquaculture Sector Using Convolutional Neural Networks Analysis." Aquaculture Journal 5, no. 1 (2025): 6. https://doi.org/10.3390/aquacj5010006.
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The expansion of aquaculture necessitates innovative disease detection methods to ensure sustainable production. Fish diseases caused by bacteria, viruses, fungi, and parasites result in significant economic losses and threaten food security. Traditional detection methods are labor-intensive and time-consuming, emphasizing the need for automated approaches. This study investigates the application of convolutional neural networks (CNNs) for classifying freshwater fish diseases. Such CNNs offer an efficient and automated solution for fish disease detection, reducing the burden on aquatic health experts and enabling timely interventions to mitigate economic losses. A dataset of 2444 images was used across seven classes—bacterial red disease, bacterial Aeromoniasis disease, bacterial gill disease, fungal disease, parasitic diseases, white tail disease, and healthy fish. The CNNs model incorporates convolutional layers for feature extraction, max-pooling for down-sampling, dense layers for classification, and dropout for regularization. Categorical cross-entropy loss and the Adam optimizer were used over 50 epochs, with continuous training and validation performance monitoring. The results indicated that the model achieved an accuracy of 99.71% and a test loss of 0.0119. This study highlights the transformative potential of artificial intelligence in aquaculture for enhancing food security.
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Li, Daoliang, Xin Li, Qi Wang, and Yinfeng Hao. "Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review." Animals 12, no. 21 (2022): 2938. http://dx.doi.org/10.3390/ani12212938.
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Aquatic products, as essential sources of protein, have attracted considerable concern by producers and consumers. Precise fish disease prevention and treatment may provide not only healthy fish protein but also ecological and economic benefits. However, unlike intelligent two-dimensional diagnoses of plants and crops, one of the most serious challenges confronted in intelligent aquaculture diagnosis is its three-dimensional space. Expert systems have been applied to diagnose fish diseases in recent decades, allowing for restricted diagnosis of certain aquaculture. However, this method needs aquaculture professionals and specialists. In addition, diagnosis speed and efficiency are limited. Therefore, developing a new quick, automatic, and real-time diagnosis approach is very critical. The integration of image-processing and computer vision technology intelligently allows the diagnosis of fish diseases. This study comprehensively reviews image-processing technology and image-based fish disease detection methods, and analyzes the benefits and drawbacks of each diagnostic approach in different environments. Although it is widely acknowledged that there are many approaches for disease diagnosis and pathogen identification, some improvements in detection accuracy and speed are still needed. Constructing AR 3D images of fish diseases, standard and shared datasets, deep learning, and data fusion techniques will be helpful in improving the accuracy and speed of fish disease diagnosis.
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Coutinho, Catarina D., Charlotte E. Ford, Joseph D. Trafford, Ana Duarte, Rui Rebelo, and Gonçalo M. Rosa. "Non-Lethal Detection of Ranavirus in Fish." Viruses 15, no. 2 (2023): 471. http://dx.doi.org/10.3390/v15020471.
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Emergent infectious diseases have an increasing impact on both farmed animals and wildlife. The ability to screen for pathogens is critical for understanding host–pathogen dynamics and informing better management. Ranavirus is a pathogen of concern, associated with disease outbreaks worldwide, affecting a broad range of fish, amphibian, and reptile hosts, but research has been limited. The traditional screening of internal tissues, such as the liver, has been regarded as the most effective for detecting and quantifying Ranavirus. However, such methodology imposes several limitations from ethical and conservation standpoints. Non-lethal sampling methods of viral detection were explored by comparing the efficacy of both buccal swabbing and fin clipping. The study was conducted on two Iberian, threatened freshwater fish (Iberochondrostoma lusitanicum and Cobitis paludica), and all samples were screened using qPCR. While for C. paludica both methods were reliable in detecting Ranavirus, on I. lusitanicum, there was a significantly higher detection rate in buccal swabs than in fin tissue. This study, therefore, reports that fin clipping may yield false Ranavirus negatives when in small-bodied freshwater fish. Overall, buccal swabbing is found to be good as an alternative to more invasive procedures, which is of extreme relevance, particularly when dealing with a threatened species.
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Nadja, K. K., I. Jaya, M. Iqbal, and A. Santika. "Detection and Quantification of Motile Aeromonas Septicemia (MAS) Disease in Common carp Using Deep Learning." IOP Conference Series: Earth and Environmental Science 1359, no. 1 (2024): 012079. http://dx.doi.org/10.1088/1755-1315/1359/1/012079.
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Abstract Common carp (Cyprinus carpio) is one of the freshwater fisheries commodities that has important economic value, so it is widely cultured. However, common carp farming is susceptible to diseases, such as Motile Aeromonas Septicemia (MAS), which can lead to mortality. MAS disease is visually detectable, but continuous monitoring challenges arise for farmers. This research employs the YOLOv8 and DeepSORT algorithms to automatically detect common carp and wounds caused by Aeromonas hydrophila bacterial infection, obtaining wound area and swimming speed data. Training was conducted on a dataset with two different labels: infected fish wound areas and the entire fish body, using consecutive epochs of 3000 and 1000. The training results show wound label accuracy reached 91,49% for disease concentration of 107 cfu/mL and 88,68% for disease concentration of 108 cfu/mL, respectively, while the accuracy for fish label reached 96,61% for disease concentration of 107 cfu/mL and 93,44% for disease concentration of 108 cfu/mL. The coefficient value of predicted wound area against actual wound area approximates 1, indicating a close match between the two variables. The obtained fish swimming speed estimate reflects the lethargic behavior of fish due to MAS. The results demonstrate that the model is effective in detecting fish, wound areas, and swimming speed.
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Nguyen, Quang Hoan, Hong Quang Doan, Van Hung Tran, Vu Thi Tuyet Nhung, and Duc Anh Duong. "An Improved YOLOv8 Model for Fish Classification and Disease Detection." Journal of Measurement, Control, and Automation 29, no. 2 (2025): 64–72. https://doi.org/10.64032/mca.v29i2.279.
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Fish classification and disease detection are crucial for sustainable aquaculture, necessitating accurate and efficient vision models. This study introduces FISH-YOLOV8, an enhanced YOLOv8 variant, incorporating: (1) SPD-Conv for optimized feature extraction and reduced computational load; (2) BiFormer Attention for enhanced small object detection and occlusion management; (3) dynamic IoU-threshold NMS to minimize false positives. This Article states that, evaluated on 15,162 images, FISH-YOLOV8 attains a mAP@50 of 0.990 and a mAP@50:95 of 0.859, outperforming baseline YOLOv8 and advanced models such as YOLOv11, at 45 fps, supports effective real-time aquaculture monitoring.
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Tarasova, A. S., A. V. Perchun, and V. P. Melnikov. "Polymerase chain reaction for detection of some highly dangerous viral fish disease agents." Veterinary Science Today, no. 1 (March 30, 2020): 11–16. http://dx.doi.org/10.29326/2304-196x-2020-1-32-11-16.
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Today viral fish diseases cause major losses in the world aquaculture. Pathogen spread often occurs during the transportation of fish from infected farms to the disease-free ones. Therefore, the import of fish stocking material to Russia from countries with a different epidemic situation requires risk-based monitoring and forecasting. Diagnostics is of primary importance in the complex of measures to prevent the spread of viral infections in fish. To date, laboratory diagnostics of viral fish diseases is based on pathogen isolation and its identification using serological methods which require a lot of time and are performed only in large research institutes with specialized laboratories. Molecular diagnostic methods are more sensitive and high-performance. The article presents the results of using reverse transcription polymerase chain reaction to detect a number of highly dangerous viral diseases of fish (Salmonidae). As a result of this work, primers were selected and the temperature and time conditions of the reaction were optimized for the identification of infectious hematopoietic necrosis, viral hemorrhagic septicemia and infectious salmon anemia. The results obtained during the research allowed us to establish that this diagnostic method is highly specific with analytical sensitivity to infectious salmon anemia virus of 2.5 lg TCD50/сm3, to infectious hematopoietic necrosis – 2.9 lg TCD50/сm3 and to viral hemorrhagic septicemia – 4.2 lg TCD50/ сm3. The described method was used to identify reference and field strains available at the FGBI ARRIAH Reference Laboratory for Aquaculture Diseases and isolated in different years in fish farms in the territory of the Russian Federation. The research data correlated with the results obtained from virus neutralization in cell culture and ELISA performed using commercial kits. The proposed method of RT-PCR allows to detect pathogens both in fish with pronounced clinical signs and in latent virus carriers.
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Ilyasu, Umar, Zaharaddeen Sani, and Tasiu Suleiman. "Internet of things-based smart fish farming: Application of smart sensors and computer vision to provide real-time monitoring and diagnosis in aquaculture." Journal of Basics and Applied Sciences Research 3, no. 2 (2025): 70–77. https://doi.org/10.4314/jobasr.v3i2.8.
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ABSTRACT The frequent occurrence of disease outbreaks in fish farming presents a significant challenge, leading to substantial economic losses and threatening food security, thus hindering the progress toward sustainable development goals (SDGs). In aquaculture, disease prevention relies on early detection of changes in water quality, abnormal fish behavior, and physical deformities, tasks typically handled by skilled fisheries experts, who are in short supply in Nigeria. Traditional manual disease detection methods are often costly and unreliable. This study proposes a computer vision-based solution utilizing Faster Region-based Convolutional Neural Network (FasterR-CNN) with Detectron2 for improved disease detection in fish farming. A dataset of 500 images was collected, pre-processed, and divided into training (70%), validation (15%), and testing (15%) sets. Three Faster R-CNN models (X101, R100, and R50) were trained and evaluated, with the X101 model achieving the highest accuracy of 98%. The results underscore the potential of deep learning techniques for accurate and efficient disease detection, offering a scalable solution to enhance fish health management. This approach provides a reliable and cost-effective alternative to traditional methods, contributing to the sustainability and growth of the aquaculture industry while addressing the need for timely interventions in fish disease control.
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Nesi, Maria Yunita, Yampi R. Kaesmetan, and Meliana O. Meo. "SISTEM PAKAR DIAGNOSA PENYAKIT IKAN GURAME DENGAN MENGGUNAKAN FIS MAMDANI." High Education of Organization Archive Quality: Jurnal Teknologi Informasi 11, no. 2 (2021): 73–80. http://dx.doi.org/10.52972/hoaq.vol11no2.p73-80.
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The carp (Osphronemus Goramy) including fish that was seeded in cultivation. In addition to the price of carp that are relatively more expensive than other fish and it has been easy to carp also has a higher value compared to other freshwater fish. But in the cultivation of carp diseases is one of the serious problems encountered by the fish farmers because it could potentially cause harm. Diseases that attack the carp both are still in the larval or adult forms of which are caused by parasitic infections in the form of fungi, protozoa, worms as well as bacterial infection of Aeromonas hydrophylla, Flexybacter colomnaris, and Mycobacterium sp. The multiplicity of types of disease that can attack the carp and the difficult process of detection because of the similarity of the symptoms caused fish farmers making it difficult to determine the methods of prevention and control of the right to address the disease. Detection of disease of carp is seen on the surface of the body of the fish. Therefore, it takes expert system to detect disease carp by involving technology. One of the methods used in the expert system of fuzzy inference system Mamdani. Fuzzy inference system Mamdani reasoning used in this study because of the handling of the value and accuisition of knowledge representation experts can directly representation in the form of rules, which can be understood when placed on the machine inference. The result of this reasoning is to detect diseases of the carp while delivering the right solution to tackle the disease of carp.
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Slovak, Marilyn L., Elmon L. Enriquez, Victoria Bedell, et al. "Antibody-Targeted FISH Analysis Improves Detection of Residual Disease in “High Risk” B-Cell Acute Lymphoblastic Leukemia." Blood 110, no. 11 (2007): 3500. http://dx.doi.org/10.1182/blood.v110.11.3500.3500.
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Abstract A number of prognostically important genetic subtypes of acute lymphoblastic leukemia (ALL) has been identified. Most notably, the presence of t(9;22)/BCR-ABL1 or 11q23/MLL rearrangements in B-cell ALL is a poor prognostic indicator and patients with these subtypes of ALL are candidates for allogeneic stem cell transplantation. Consequently, early detection of minimal residual disease (MRD) is critical for appropriate diagnostic and therapeutic decisions. Current methods for MRD measurements are based on morphologic examinations, flow cytometry, quantitative reverse-transcription polymerase chain reaction (Q-RT-PCR) and cytogenetics/fluorescence in situ hybridization (FISH). While high abnormality rates ease disease detection through standard morphologic and cytogenetic analyses, the presence of cytogenetically-aberrant lymphoblasts at low levels post treatment hampers residual disease detection. In this study, we analyzed 126 samples collected from 51 patients using a sequential immunohistochemistry (phenotype)/FISH (genotype) approach to detect B-cells with rearrangements of BCR-ABL1 or 11q23/MLL post treatment in 44 and 7 patients, respectively. Cytospin slides, made from residual bone marrow, were stained with a monoclonal CD19 (clone HD37) antibody and scanned on an image analysis system (BioView Duet™) to target the CD19 positive B-cell population. During the scan, the location of each CD19 positive cell was recorded. The slides were subsequently destained and hybridized with FISH probes specific for the genotypic rearrangements mentioned above, with only antibody-targeted cells analyzed (target FISH or T-FISH). Disease was detected by T-FISH and at least one other method in 50 (39.7%) of the 126 samples tested, a finding comparable to the percentage identified by Q-RT-PCR (39.0%) and/or flow cytometry (23.2%). In samples with positive FISH results, T-FISH outperformed or was comparable to standard FISH in detecting disease in 47 (94%) samples. Importantly, T-FISH detected an abnormal cell population in 14/50 (28%) that standard FISH did not detect (p = 0.0064). Eight (57.1%) of these 14 samples had concurrent positive Q-RT-PCR results. The remaining six samples had MLL rearrangements and PCR studies were not performed. In three (6%) samples, abnormal cells were not CD19 positive and thus not detected until a followup area scan of the entire slide revealed low-level positivity in an apparent subset of CD19 negative progenitor B-cells. This latter finding was not observed in the concurrent negative controls. Only two samples (1.6%) with low level BCR-ABL1 positivity by Q-RT-PCR (>10−5) were negative by T-FISH. Serial dilution experiments of CD19-positive/t(9;22)-positive ALL-1 and CD19-negative/t(9;22)-negative Kasumi-1 cell lines demonstrated that T-FISH identified abnormalities at dilutions as low as 10−5, with consistent and reliable detection at 10−3. These observations suggest that antibody-targeted FISH is an effective way to increase the sensitivity of a slide-based assay in detecting residual “high risk” ALL.
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Yu, Gangyi, Junbo Zhang, Ao Chen, and Rong Wan. "Detection and Identification of Fish Skin Health Status Referring to Four Common Diseases Based on Improved YOLOv4 Model." Fishes 8, no. 4 (2023): 186. http://dx.doi.org/10.3390/fishes8040186.
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A primary problem affecting the sustainable development of aquaculture is fish skin diseases. In order to prevent the outbreak of fish diseases and to provide prompt treatment to avoid mass mortality of fish, it is essential to detect and identify skin diseases immediately. Based on the YOLOv4 model, coupled with lightweight depthwise separable convolution and optimized feature extraction network and activation function, the detection and identification model of fish skin disease is constructed in this study. The developed model is tested for the diseases hemorrhagic septicemia, saprolegniasis, benedeniasis, and scuticociliatosis, and applied to monitor the health condition of fish skin in deep-sea cage culture. Results show that the MobileNet3-GELU-YOLOv4 model proposed in this study has an improved learning ability, and the number of model parameters is reduced. Compared to the original YOLOv4 model, its mAP and detection speed increased by 12.39% and 19.31 FPS, respectively. The advantages of the model are its intra-species classification capability, lightweight deployment, detection accuracy, and speed, making the model more applicable to the real-time monitoring of fish skin health in a deep-sea aquaculture environment.
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Pauzi, S. N., M. G. Hassan, N. Yusoff, N. H. Harun, A. H. Abu Bakar, and B. C. Kua. "A review on image processing for fish disease detection." Journal of Physics: Conference Series 1997, no. 1 (2021): 012042. http://dx.doi.org/10.1088/1742-6596/1997/1/012042.
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Mamun, Md Rashedul Islam, Umma Saima Rahman, Tahmina Akter, and Muhammad Anwarul Azim. "Fish Disease Detection using Deep Learning and Machine Learning." International Journal of Computer Applications 185, no. 36 (2023): 1–9. http://dx.doi.org/10.5120/ijca2023923079.
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Wilson, Alphus Dan. "Applications of Electronic-Nose Technologies for Noninvasive Early Detection of Plant, Animal and Human Diseases." Chemosensors 6, no. 4 (2018): 45. http://dx.doi.org/10.3390/chemosensors6040045.
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The development of electronic-nose (e-nose) technologies for disease diagnostics was initiated in the biomedical field for detection of biotic (microbial) causes of human diseases during the mid-1980s. The use of e-nose devices for disease-diagnostic applications subsequently was extended to plant and animal hosts through the invention of new gas-sensing instrument types and disease-detection methods with sensor arrays developed and adapted for additional host types and chemical classes of volatile organic compounds (VOCs) closely associated with individual diseases. Considerable progress in animal disease detection using e-noses in combination with metabolomics has been accomplished in the field of veterinary medicine with new important discoveries of biomarker metabolites and aroma profiles for major infectious diseases of livestock, wildlife, and fish from both terrestrial and aquaculture pathology research. Progress in the discovery of new e-nose technologies developed for biomedical applications has exploded with new information and methods for diagnostic sampling and disease detection, identification of key chemical disease biomarkers, improvements in sensor designs, algorithms for discriminant analysis, and greater, more widespread testing of efficacy in clinical trials. This review summarizes progressive advancements in utilizing these specialized gas-sensing devices for numerous diagnostic applications involving noninvasive early detections of plant, animal, and human diseases.
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Jeremic, Svetlana, Miroslav Cirkovic, Dobrila Jakic-Dimic, and Vladimir Radosavljevic. "Fish diseases in carp fish ponds and implementation of health care measures." Veterinarski glasnik 59, no. 1-2 (2005): 59–69. http://dx.doi.org/10.2298/vetgl0502059j.
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Fish health protection is a complex and permanent measure veterinary specialists are taking in order to preserve and improve breeding and production of particular fish species and categories. The system of monitoring fish health should ensure early detection of disorders in fish health and the presence of causing agents. In order for the monitoring to be efficient it should be practiced in accordance with the specific conditions of each system and breeding venues, as well as to the specific health problem needs of different farmed fish species. The most important issue in fish diseases diagnostics is the systematic monitoring of the condition offish health. Only in such way it is possible to detect a disease on time and then determine the adequate therapeutic and other necessary measures. In dealing with the problems offish pathology in carp fishponds, the epizootical situation of disease spreading caused by different agents (viruses, bacteria, fungi and parasites) has been examined. The most frequent diseases among the farmed carps in the examined fish ponds in Vojvodina area, described in this paper were: carp pox, spring viremia of carp, carp erythrodermatitis, aeromonas and pseudomonas infections, bacterial gill disease, diseases caused by ecto- and endoparasites and gill necrosis. Based on the obtained results, modern diagnostic methods were implemented and proper prevention and successful therapy of the diseases causing the greatest loss in farmed fish populations was taken.
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Bhoi, Sourav Kumar, Sanjaya Kumar Panda, Kalyan Kumar Jena, Chittaranjan Mallick, and Akhtar Khan. "A fuzzy approach to identify fish red spot disease." Grey Systems: Theory and Application 10, no. 3 (2020): 249–63. http://dx.doi.org/10.1108/gs-11-2019-0051.
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PurposeFish are considered as one of the important aquatic animals in the planet. They play a vital role in the nutrient cycle. They can be considered as one of the healthy food for human beings. They can also act as a solution for some of the human health problems. If fish are affected by several diseases, they in turn provide an adverse effect on human health. Therefore, it is very much essential to protect fish from being affected by any diseases.Design/methodology/approachThis paper is mainly focused on the identification of the red spot diseased area in fish. In this work, a fuzzy rule based method (FRBAM) and triangular membership function (TMFN) is used to identify the red spot disease (RSD) in the fish by analyzing several red spot diseased fish (RSDF) images. The canny edge detector is used for intermediate processing of RSDF images.FindingsThe proposed method is able to identify the red pixels over the fish by marking the affected area with red color by using a standard RGB model.Originality/valueThe proposed method follows FRBAM and TMFN in order to detect the RSD and canny edge detector for processing of RSDF images. Finally, it is tested using ten different image sizes and the results show its better performance in terms of detection of RSD affected regions of fish and execution time.
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Hassan, Ahmad, and Shahzad Ali. "Epidemiology and Molecular Confirmation of E. coli Isolated from Diseased Fish in Muzaffargarh, Punjab, Pakistan." BioScientific Review 6, no. 1 (2024): 1–15. http://dx.doi.org/10.32350/bsr.61.01.
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Background Fish is an important source of protein and vitamins, such as vitamin D and B2 (riboflavin) for human beings. However, they are plagued with a variety of disease-causing pathogens, resulting in significant economic losses. Among these pathogens, Escherichia (E.) coli are prominent, worldwide. This study aimed to conduct epidemiological surveillance and identification of E. coli strains isolated from diseased fish in District Muzaffargarh, Punjab, Pakistan. Methodology A total of fifty (50) diseased fish samples were collected from various fish farms in the district. The isolation process involved enriching the samples in nutrient broth and incubating them at 37°C for 24 hours. After enrichment, the samples were inoculated on MacConkey agar and incubated again at 37°C for 24 hours. Following incubation, Gram staining was performed to identify E. coli and confirm its presence. These isolates were subjected to PCR using the uspA gene for confirmation. Results Among fish diseases, Hemorrhagic septicemia was reported to have the highest prevalence (22%), while 12% of fish samples were infected with abdominal dropsy and fin rot. In total, six (06) E. coli isolates were obtained from five different diseased fish samples and confirmed by PCR-based detection of uspA gene. Conclusion The current study found a link between disease-affected fish and naturally occurring E. coli, with molecular confirmation using the uspA gene. Effective management of soil, stock, water, nutrition, and environment is crucial to control losses caused by E. coli as opportunistic fish pathogens and spoilage agents.
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Wang, Zhen, Haolu Liu, Guangyue Zhang, Xiao Yang, Lingmei Wen, and Wei Zhao. "Diseased Fish Detection in the Underwater Environment Using an Improved YOLOV5 Network for Intensive Aquaculture." Fishes 8, no. 3 (2023): 169. http://dx.doi.org/10.3390/fishes8030169.
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In intensive aquaculture, the real-time detection and monitoring of common infectious disease is an important basis for scientific fish epidemic prevention strategies that can effectively reduce fish mortality and economic loss. However, low-quality underwater images and low-identification targets present great challenges to diseased fish detection. To overcome these challenges, this paper proposes a diseased fish detection model, using an improved YOLOV5 network for aquaculture (DFYOLO). The specific implementation methods are as follows: (1) the C3 structure is used instead of the CSPNet structure of the YOLOV5 model to facilitate the industrial deployment of the algorithm; (2) all the 3 × 3 convolutional kernels in the backbone network are replaced by a convolutional kernel group consisting of parallel 3 × 3, 1 × 3 and 3 × 1 convolutional kernels; and (3) the convolutional block attention module is added to the YOLOV5 algorithm. Experimental results in a fishing ground showed that the DFYOLO is better than that of the original YOLOV5 network, and the average precision was improved from 94.52% to 99.38% (when the intersection over union is 0.5), for an increase of 4.86%. Therefore, the DFYOLO network can effectively detect diseased fish and is applicable in intensive aquaculture.
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Senani, B.R.K, Samarawickrama Naduni, B.K Tharuka, Lokuliyana Shashika, P.M Ranawaka, and Wijesiri M.P.M. "Fish Guard: A Holistic Approach to Automated Fish Farming with IoT and Image Processing." International Journal of Engineering and Management Research 14, no. 3 (2024): 6–14. https://doi.org/10.5281/zenodo.11450760.
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The ornamental fish industry, which is important to the global economy, faces challenges that hinder its productivity and sustainability, particularly feeding practices, water quality management and disease control. This research introduces an integrated system designed to automate and optimize these aspects using the ESP32 microcontroller. Offers a lower cost and more effective, low-power solution with real-time processing capabilities. The proposed system has developed along four main lines. An automated feeding system that adapts to the food needs of fish, a water quality management system that monitors and controls critical parameters through machine learning algorithms, and a disease management system that uses image processing techniques for early detection. The systems were tested in controlled environments, showing significant improvements in nutrient efficiency, water quality stability and disease prevention. Our findings suggest that the integration of these technologies can significantly enhance the operational efficiency and sustainability of ornamental fish farms.
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Fox, J. L., P. H. Hsu, M. S. Legator, L. E. Morrison, and S. A. Seelig. "Fluorescence in situ hybridization: powerful molecular tool for cancer prognosis." Clinical Chemistry 41, no. 11 (1995): 1554–59. http://dx.doi.org/10.1093/clinchem/41.11.1554.
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Abstract We review several aspects of fluorescence in situ hybridization (FISH) technology that demonstrate its breadth and power in detecting and monitoring genetic abnormalities associated with cancers. The clinical utility of FISH in disease management is demonstrated in several examples, including trisomy 8 detection with high specificity and sensitivity in patients with myeloid leukemias; trisomy 12 detection with higher efficiency than conventional cytogenetics in patients with chronic lymphocytic leukemia; assessment of engraftment success, chimerism, and relapse in opposite sex bone marrow transplantation; and correlation of trisomy 7 with survival time in patients with prostate tumors. Advances in FISH technology include multicolor analyses, which permit the simultaneous detection of several genetic abnormalities by using cohybridization of probes labeled with several fluorescent labels or label combinations, and comparative genomic hybridization, a relatively new method whereby a single hybridization can reveal aberrations across the entire genome.
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Bae, Myeong-Hun, Jun Park, Se-Hoon Jung, and Chun-Bo Sim. "Fish Species and Disease Detection System Using Deep Learning-Based Object Detection Model." Journal of Korea Multimedia Society 26, no. 8 (2023): 898–910. http://dx.doi.org/10.9717/kmms.2023.26.8.898.
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OLECH, MONIKA, and EWA PAŹDZIOR. "Detection of bacterial fish pathogens by MALDI-TOF MS- state of the art." Medycyna Weterynaryjna 80, no. 4 (2024): 257–63. http://dx.doi.org/10.21521/mw.6889.
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A key element in the diagnosis of bacterial infections in fish is the rapid identification of the etiological agent of the disease and drug resistance testing. Traditional methods of identifying the bacteria that cause fish diseases are often slow and/or expensive. MALDI-TOF MS is a well-established, rapid, accurate and cost-effective method based on protein profile analysis for the identification of bacterial pathogens in human and veterinary laboratories, and has now found application in the diagnosis of bacterial infections in fish. The method identifies microorganisms based on their protein profile, which is then compared with reference spectra stored in databases. Since not all bacterial fish pathogens are included in commercial databases, libraries are usually created locally for the relevant reference strains/species. As studies have shown, MALDI-TOF MS is suitable for the specific identification of many bacterial fish pathogens enabling rapid diagnosis of infections.
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Rolph, Matthew J., Pompei Bolfa, Sarah M. Cavanaugh, and Kerry E. Rolph. "Fluorescent In Situ Hybridization for the Detection of Intracellular Bacteria in Companion Animals." Veterinary Sciences 11, no. 1 (2024): 52. http://dx.doi.org/10.3390/vetsci11010052.
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FISH techniques have been applied for the visualization and identification of intracellular bacteria in companion animal species. Most frequently, these techniques have focused on the identification of adhesive-invasive Escherichia coli in gastrointestinal disease, although various other organisms have been identified in inflammatory or neoplastic gastrointestinal disease. Previous studies have investigated a potential role of Helicobacter spp. in inflammatory gastrointestinal and hepatic conditions. Other studies evaluating the role of infectious organisms in hepatopathies have received some attention with mixed results. FISH techniques using both eubacterial and species-specific probes have been applied in inflammatory cardiovascular, urinary, and cutaneous diseases to screen for intracellular bacteria. This review summarizes the results of these studies.
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Gu, Junjie, Huayu Wang, Mengye Zhang, et al. "Application of Fluorescence In Situ Hybridization (FISH) in Oral Microbial Detection." Pathogens 11, no. 12 (2022): 1450. http://dx.doi.org/10.3390/pathogens11121450.
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Varieties of microorganisms reside in the oral cavity contributing to the occurrence and development of microbes associated with oral diseases; however, the distribution and in situ abundance in the biofilm are still unclear. In order to promote the understanding of the ecosystem of oral microbiota and the diagnosis of oral diseases, it is necessary to monitor and compare the oral microorganisms from different niches of the oral cavity in situ. The fluorescence in situ hybridization (FISH) has proven to be a powerful tool for representing the status of oral microorganisms in the oral cavity. FISH is one of the most routinely used cytochemical techniques for genetic detection, identification, and localization by a fluorescently labeled nucleic acid probe, which can hybridize with targeted nucleic acid sequences. It has the advantages of rapidity, safety, high sensitivity, and specificity. FISH allows the identification and quantification of different oral microorganisms simultaneously. It can also visualize microorganisms by combining with other molecular biology technologies to represent the distribution of each microbial community in the oral biofilm. In this review, we summarized and discussed the development of FISH technology and the application of FISH in oral disease diagnosis and oral ecosystem research, highlighted its advantages in oral microbiology, listed the existing problems, and provided suggestions for future development..
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Dishon, Arnon, Ayana Perelberg, Janette Bishara-Shieban, et al. "Detection of Carp Interstitial Nephritis and Gill Necrosis Virus in Fish Droppings." Applied and Environmental Microbiology 71, no. 11 (2005): 7285–91. http://dx.doi.org/10.1128/aem.71.11.7285-7291.2005.
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ABSTRACT Carp interstitial nephritis and gill necrosis virus (CNGV) is an unclassified large DNA virus that morphologically resembles members of the Herpesviridae but contains a large (ca. ∼280-kbp) linear double-stranded DNA. This virus has also been named koi herpesvirus, koi herpes-like virus, and cyprinid herpesvirus 3. CNGV is the cause of a lethal disease that afflicts common carp and koi. By using immunohistochemistry, molecular analysis, and electron microscopy we previously demonstrated that this virus is present mainly in the intestine and kidney of infected fish. Based on these observations, we postulated that viruses and/or viral components may appear in droppings of infected carp. Here we report that (i) by using PCR we demonstrated that fish droppings contain viral DNA, (ii) fish droppings contain viral antigens which are useful for CNGV diagnosis, and (iii) fish droppings contain active virus which can infect cultured common carp brain cells and induce the disease in naïve fish following inoculation. Thus, our findings show that CNGV can be identified by using droppings without taking biopsies or killing fish and that infectious CNGV is present in the stools of sick fish. The possibility that fish droppings preserve viable CNGV during the nonpermissive seasons is discussed.
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Benedicenti, Ottavia, Marit Måsøy Amundsen, Saima Nasrin Mohammad, et al. "A refinement to eRNA and eDNA-based detection methods for reliable and cost-efficient screening of pathogens in Atlantic salmon aquaculture." PLOS ONE 19, no. 10 (2024): e0312337. http://dx.doi.org/10.1371/journal.pone.0312337.
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Finfish aquaculture is one of the fastest-growing food production sectors in the world, and numerous infectious diseases are a constant challenge to the fish farming industry, causing decreased fish health and, consequently, economic losses. Specific and sensitive tools for pathogen detection are crucial for the surveillance of environmental samples to prevent the spread of fish pathogens in farms. Monitoring of waterborne pathogens through filtration of water and subsequent molecular detection of target-specific DNA or RNA sequence motifs is an animal-friendly method. This approach could reduce or even replace the sacrifice of fish for monitoring purposes in aquaculture and allow earlier implementation of disease control measures. Sampling methods might be a bottleneck, and there is a need for simple sampling methods that still ensure the best detection probability. In this study, we tested different filtration methods with spiked freshwater and seawater for a panel of fish pathogens to discern a suitable procedure that can be easily applied on-site by farm personnel without compromising detection probability. Specifically, we tested combinations of different filtration flow rates, lysis buffers, and filters for the detection of some of the pathogens relevant to the aquaculture industry. The results showed that a “sandwich” filtration method using two different filters and a flow rate of up to 4.0 L/min ensured good pathogen detection. The filters, consisting of a hydrophilic glass fibre filter with binder resin on the top and a hydrophilic mixed cellulose esters membrane at the bottom, achieved the best concentration and qPCR detection of both viral and bacterial fish pathogens. This up-and-coming tool allows the detection of very different fish pathogens during a single filtration step, and it can be combined with one single automated total nucleic acid extraction step for all the investigated pathogens, reducing both analysis costs and time.
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Sergeenko, NV, EA Ustimenko, MG Eliseikina, AD Kuhlevskiy, EV Bochkova, and TV Ryazanova. "First report of bacterial kidney disease in coho salmon Oncorhynchus kisutch in Russia." Diseases of Aquatic Organisms 140 (June 18, 2020): 31–36. http://dx.doi.org/10.3354/dao03486.
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This paper describes the first case of bacterial kidney disease (BKD) to be identified in coho salmon Oncorhynchus kisutch in Russia. The fish in question was caught in Lake Bolshoi Vilyui on the Kamchatka Peninsula. The diseased fish had foci of granulomatous inflammation in the kidneys. The diagnosis was confirmed by isolating the bacterium Renibacterium salmoninarum from kidney tissue in pure culture, and by determining the partial 16S RNA gene sequence of the isolate. This is the first detection of this pathogen in the genus Oncorhynchus in Russia, and detection of BKD in coho salmon indicates that the pathogen is present in the natural fish populations of Kamchatka. Therefore, it will be necessary to conduct screening studies of mature salmon selected for artificial reproduction, for the presence of BKD signs and asymptomatic infection with R. salmoninarum, which will allow us to estimate the prevalence of the pathogen.
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Daru, A. F., F. W. Christanto, and V. Vydia. "Internet of Things-based Water pH Level Monitoring for Arowana Cultivation." IOP Conference Series: Earth and Environmental Science 1177, no. 1 (2023): 012004. http://dx.doi.org/10.1088/1755-1315/1177/1/012004.
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Abstract The Arowana fish cultivation is a special cultivation that grows decorative fish instead of consumption. Unlike consumption fish, Arowana fish require a strict water pH level to live healthily. Hence, the cultivator must carefully maintain the water to prevent high acidity levels in the aquarium. A high acid level in the water may cause severe disease or death to Arowana. However, detecting acidity levels in the water required a particular tool, such as litmus paper. Litmus paper provided accurate acid detection by color indication. A strip of litmus paper is required for every aquarium to detect the acidity level. Buying many litmus papers may increase the unnecessary cost of cultivation. This research proposed acidity level monitoring by implementing Internet of Things technology to allow automation and remote monitoring. According to the evaluation result, the proposed model is capable of detecting the state of the acidity level with an accuracy of up to 100%. Besides that, the detection range of the proposed model is almost identical to a pH meter with a percentage error of less than 1% in many acidity levels.
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Gomaa, Aya, Samy Khaliel, Adel Elgamal, and Rasha Tawfik. "Molecular Characterization of Vibrio Spp Isolated from Fresh Water Fish in Kafer Elsheikh Government." Alexandria Journal of Veterinary Sciences 82 (2024): 44. http://dx.doi.org/10.5455/ajvs.203996.
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The most important threatening disease problem facing freshwater aquaculture is Vibriosis that cause water-borne diseases and foodborne gastroenteritis resulted from ingesting of uncooked or undercooked fish products. Also, it is one of the most zoonotic disease which transmitted to human from fish and another aquaculture. This study aimed to isolate different vibrio species from freshwater fish. Microbiological identification of Vibrio species were made using colonial morphology, biochemical characteristics and Identification system for Gram negative bacteria (GN 24) (Diagnostic sro kits) from total of 100 clinically diseased fish samples (Oreochromis niloticus) were collected alive from two various fish farms ( each 50 fish from one farm) from Kafrelsheik Governorate., the prevalence of Vibrio species were noticed in samples tilabia (23/100) 23% which V. parahaemolyticus was found in 6/100 (6%) and V. alginolyticus was detected in 16/100 (16%) and in V. cholera 1/100 (1%), Also we used PCR using 16S rRNA gene-specific for the genus Vibrio , detection of virulence and antibacterial resistance associated genes in v. parahaemolyticus and V. alginolyticus which the most pathogenic vibrio species .The present study also focused on the emergence of Multiple Antibiotic Resistance (MAR) index with the aim of developing strategies to mitigate the potential for infection and ensure effective treatment in the near future.
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Rao, Koteshwar. "Deep Learning and AI Tools for Monitoring and Detecting Diseases in Freshwater Fish Populations." International Journal of Forest, Animal And Fisheries Research 9, no. 2 (2025): 22–24. https://doi.org/10.22161/ijfaf.9.2.3.
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Freshwater fish populations sustainability and well-being are essential to aquaculture biodiversity and food security conventional approaches to fish disease diagnosis are frequently labor-intensive time-consuming and necessitate professional intervention which causes treatment delays and large financial losses recent developments in deep learning dl a subfield of artificial intelligence AI present viable substitutes for automated quick and precise fish disease detection this study investigates how to use AI and deep learning tools to monitor and diagnose illnesses that impact freshwater fish predictive modeling pattern recognition and image recognition techniques are used by these systems to accurately identify visual symptoms like lesions discoloration or abnormal behavior along with their datasets training procedures and performance metrics the paper examines a variety of machine learning models used in fish health assessment such as convolutional neural networks CNNS support vector machines SVMS and hybrid architectures real-time monitoring systems made possible by internet of things IOT gadgets and AI-powered image processing frameworks are also covered the results show how deep learning can transform aquaculture disease management by improving fish welfare enabling early detection and lowering manual labor the development of robust scalable and economical solutions is one of the future directions
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Afzali, Fatemeh, Hassan Hj Mohd Daud, Shiv Shankar, M. Shuaib Khan, and Samanesadat Afzali. "Detecting Aphanomyces Invadans in Pure Cultures and EUS-infected Fish Lesions by Applying PCR." Malaysian Journal of Medical and Biological Research 3, no. 2 (2016): 75–84. http://dx.doi.org/10.18034/mjmbr.v3i2.410.
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Aphanomyces invadans is an oomycete fungus which causes Epizootic Ulcerative Syndrome (EUS) disease in wide range of fresh and brackish water fish worldwide imposing serious economic losses. A diagnostic procedure, based on a polymerase chain reaction method (PCR) was developed to detect infection of fish with the A.invadans. A set of primers (1APM 1F and 1APM 6R) was used to specifically amplify A. invadans DNA. The PCR amplifies a 400 bp amplicon. A protocol for the extraction of A. invadans DNA from infected fish tissue and pure fungal cultures was developed. The method was tested on seven EUS-susceptible fish species (snakehead, snakeskin gourami, moonlight gourami, koi carp, catfish, gold fish, climbing perch) and one EUS-resistant fish (tilapia), artificially infected with A. invadans and pure cultures of Aphanomyces spp., Saprolegnia spp., Achlya spp., and Allomyces sp. Detection of A. invadans was possible at the early stage of sampling, which was 24 hours post injection in both EUS-susceptible and resistant fish. Resistant fish was found to be PCR-negative after 6 days of inoculation but in susceptible fish PCR-positive results obtained even after day 28 or in dead fish. Therefore PCR may be a useful method for detection EUS infection in fish from early stage of disease onset.
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Mahamuni, Chaitanya Vijaykumar, and Chalamala Srinivas Goud. "Unveiling the Internet of Things (IoT) Applications in Aquaculture: A Survey and Prototype Design with Thing Speak Analytics." Journal of Ubiquitous Computing and Communication Technologies 5, no. 2 (2023): 152–74. http://dx.doi.org/10.36548/jucct.2023.2.004.
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The study examines the potential impact of IoT in aquaculture, and its role in enhancing water quality monitoring as well as the disease prevention. It highlights the transformative power of IoT technology in providing real-time data on water parameters and enabling proactive measures against diseases. The study emphasizes the significance of adopting IoT solutions to optimize water conditions, mitigate disease risks, and enhance fish health. It also explores recent advancements, key challenges, and future directions in IoT applications for aquaculture, including water quality monitoring, feed automation systems, environmental control systems, fish tracking and monitoring systems, remote monitoring and control systems, smart harvesting systems, and disease detection and prevention systems. Based on a comprehensive literature survey, this paper introduces a research proposal focusing on water quality monitoring and disease prevention in fish. The progress thus far encompasses the selection of hardware components, sensor testing, and ongoing activities in programming and debugging.
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Nooraie, Farzad, J. Dianne Keen-Kim, Derek Denton Lyle, et al. "Cell Enrichment for FISH: A Novel Diagnostic Tool in Challenging Cases of Low Bone Marrow Involvement By Mature B-Cell Neoplasms." Blood 124, no. 21 (2014): 2992. http://dx.doi.org/10.1182/blood.v124.21.2992.2992.
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Abstract Cytogenetic studies are useful tools which can provide diagnostic, prognostic and management information for mature B-cell neoplasms. Mature B-cells do not grow well in cytogenetic cultures. Therefore, detection, characterization and differentiation of scant mature B-cell neoplasms or minimal residual disease can be difficult in bone marrow or peripheral blood specimens. FISH provides more sensitive information than G-band chromosome analysis. However, in cases with low-level involvement of the marrow, abnormal FISH results may be missed or not reported when abnormalities do not exceed experimentally determined cut-off values. To increase the sensitivity of abnormal mature B-cell detection, we developed an enrichment method utilizing pan B-cell antibodies. This method separates mature B-cells from the remaining bone marrow and/or peripheral blood cells. We selected 59 bone marrow and peripheral blood specimens from patients referred for mature B-cell neoplasms (except for myeloma) for use in the validation. These specimens had 1% to 10% monoclonal mature B-cells detected by flow cytometry and were enriched using cell-separating technologies. Each specimen was divided into four equal portions, with three of four portions undergoing enrichment using different pan B-cell antibodies. The fourth portion was reserved for standard non-enriched testing and was used for comparison to results obtained in the enriched portions. A variety of corresponding FISH analyses were performed in each of the four portions, based upon the disease state. FISH results were obtained by two independent scoring technologists. Enrichment with B-cell antibodies improved detection of FISH abnormalities that may not have otherwise been observed in the patient specimens. 42% (25/59) of samples had abnormalities detected within the enriched portion that were not detected in the standard non-enriched portion. Of these, 64% (16/25) had a FISH abnormality that was a critical finding for the final diagnosis, prognosis and/or management of the patient. Enrichment also increased the sensitivity of FISH abnormality detection. 29% (17/59) of samples had abnormalities that were detected in both the enriched and non-enriched portions. However, detection was on average 15-fold more sensitive. The average detection rate of FISH abnormalities in the non-enriched portion was 3%, which is at or near the experimentally determined cut-off value for most FISH probes. In contrast, the average detection rate of FISH abnormalities in the enriched portion was 56%. In 5% (3/59) of cases, detection of FISH aberrations in enriched specimens helped to distinguish two separate neoplastic processes in the bone marrow. These results demonstrate the increased opportunity for detecting FISH aberrations in enriched versus non-enriched specimens. Mature B-cell enrichment and subsequent FISH testing in cases of scant mature B-cell neoplastic involvement of the bone marrow and/or peripheral blood is a novel and powerful cytogenetic technique. This technique enriches bone marrow and/or peripheral blood specimens for targeted abnormal cells and increases the number of those cells analyzed by FISH testing, thus allowing for a higher detection rate of genetic abnormalities. Disclosures Nooraie: Genoptix Inc., A Novartis Company: Employment, Equity Ownership. Keen-Kim:Genoptix Inc., A Novartis Company: Employment, Equity Ownership. Lyle:Genoptix Inc., A Novartis Company: Employment, Equity Ownership. Dingivan:Genoptix Inc., A Novartis Company: Employment, Equity Ownership. Mauch:Genoptix Inc., A Novartis Company: Employment, Equity Ownership. Lynes:Genoptix Inc., A Novartis Company: Employment. Castillo:Genoptix Inc., A Novartis Company: Employment. Kolker:Genoptix Inc., A Novartis Company: Employment. Cancino:Genoptix Inc., A Novartis Company: Employment, Equity Ownership.
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Kuri, Krešimir, Marijana Sokolović, Krešimir Drašner, Juraj Petravić, Margarita Maruškić Kulaš, and Goran Jakšić. "Health Management of Endemic and Non-Endemic Fish in the Aquatika – Freshwater Aquarium Karlovac." Croatian Journal of Fisheries 80, no. 3 (2022): 141–50. http://dx.doi.org/10.2478/cjf-2022-0015.
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Abstract This study presents data on regular health monitoring of fish in the Aquatika – Freshwater Aquarium Karlovac between October 2016 and December 2019. The Aquatika Aquarium houses 85 different freshwater fish species, 31 of which are endemic in Croatia. The study included an evaluation of the results of the aquarium health monitoring programme. It determined the most common fish diseases in the aquarium (at the species and individual levels) and determined whether endemic or non-endemic freshwater fish are more susceptible to diseases and disorders. The regular health monitoring programme revealed different diseases and disorders in endemic and non-endemic fish. During the monitoring, 3104 fish specimens were analysed. The most frequent disease was ichthyophthiriasis which occurred at a similar frequency in endemic and non-endemic fish species. The results proved to be valuable for the evaluation of risks and measures to minimise the risk of the introduction and spread of pathogens in the aquarium. Preventive fish medicine is extremely important for effective aquarium management. A comprehensive health monitoring programme, including quarantine systems, control of feed and environmental parameters, along with regular fish observation, are critical for the early detection of fish diseases.
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Savan, Ram, Arisa Igarashi, Satoru Matsuoka, and Masahiro Sakai. "Sensitive and Rapid Detection of Edwardsiellosis in Fish by a Loop-Mediated Isothermal Amplification Method." Applied and Environmental Microbiology 70, no. 1 (2004): 621–24. http://dx.doi.org/10.1128/aem.70.1.621-624.2004.
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ABSTRACT Here we report a rapid and sensitive method (using loop-mediated isothermal amplification [LAMP]) for the diagnosis of edwardsiellosis, a fish disease caused by Edwardsiella tarda, in Japanese flounder. A set of four primers was designed, and conditions for the detection were optimized for the detection of E. tarda in 45 min at 65°C. No amplification of the target hemolysin gene was detected in other related bacteria. When the LAMP primers were used, detection of edwardsiellosis in infected Japanese flounder kidney, and spleen and seawater cultures was possible. We have developed a rapid and sensitive diagnostic protocol for edwardsiellosis detection in fish. This is the first report of the application of LAMP for the diagnosis of a fish pathogen.
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Durak, Beyhan, Meltem Olga Akay, Behiye Kaytaz, et al. "Detection of Chromosomal Aberrations in CLL and Correlation with Clinical Staging." Blood 108, no. 11 (2006): 4634. http://dx.doi.org/10.1182/blood.v108.11.4634.4634.
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Abstract Chromosome abnormalities detected in CLL are important markers for determination of treatment approaches. This study was aimed to evaluate usefulness of FISH panels for testing chromosome aberrations in CLL and to correlate the aberrations with clinical staging of disease and responses to chlorombucil therapy. Cytogenetics and FISH analyses from bone marrow samples of 67 CLL patients were performed according to standard unstimulated short term culture methods. CLL specific FISH panel included probes for 11q22.3 (LSIATM), CEP12, 13q14 (LSID13S19 and LSID13S25), 17p13.1 (LSIp53). FISH analysis was performed in all samples. The most commonly seen abnormality was 13q14 deletion followed by deletions of ATM, p53 and trisomy12. Of 19 cases with 13q14 deletion, 11 had only this abnormality whereas the others had trisomy12, p53 and ATM loci deletions (multiple abnormalities). Of 11 cases with single abnormality, 8 were at stage A, 3 was at stage C and all patients achieved benefits from chlorombucil therapy except one patient. Failed therapy response was seen in 9 patients with multiple abnormalities and the disease was at stage C (4 cases) and B (5 cases). Patients showing p53 and ATM deletions related with poor prognosis had progressive disease and no benefit could be achieved from chlorombucil therapy. Of 6 cases with trisomy 12, 4 were at early stage and response to the therapy was good but in two cases at later stage, therapy benefit could be achieved in one while no benefit was seen in the other one. The results showed that FISH analysis is more informative than karyotype analysis in CLL. Detection of p53 and ATM loci deletions is an important marker for administration of second level therapy.
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Freen-van Heeren, Julian J. "Flow-FISH as a Tool for Studying Bacteria, Fungi and Viruses." BioTech 10, no. 4 (2021): 21. http://dx.doi.org/10.3390/biotech10040021.
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Many techniques are currently in use to study microbes. These can be aimed at detecting, identifying, and characterizing bacterial, fungal, and viral species. One technique that is suitable for high-throughput analysis is flow cytometry-based fluorescence in situ hybridization, or Flow-FISH. This technique employs (fluorescently labeled) probes directed against DNA or (m)RNA, for instance targeting a gene or microorganism of interest and provides information on a single-cell level. Furthermore, by combining Flow-FISH with antibody-based protein detection, proteins of interest can be measured simultaneously with genetic material. Additionally, depending on the type of Flow-FISH assay, Flow-FISH can also be multiplexed, allowing for the simultaneous measurement of multiple gene targets and/or microorganisms. Together, this allows for, e.g., single-cell gene expression analysis or identification of (sub)strains in mixed cultures. Flow-FISH has been used in mammalian cells but has also been extensively employed to study diverse microbial species. Here, the use of Flow-FISH for studying microorganisms is reviewed. Specifically, the detection of (intracellular) pathogens, studying microorganism biology and disease pathogenesis, and identification of bacterial, fungal, and viral strains in mixed cultures is discussed, with a particular focus on the viruses EBV, HIV-1, and SARS-CoV-2.
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Chu, Lee Voon, Tse Hui Lim, Hein Than, et al. "Importance of Fluorescence in Situ Hybridization (FISH) Analysis on Bone Core Specimen for Detection of Relapse in Myeloid Malignancies with Fibrosis." Blood 144, Supplement 1 (2024): 6096. https://doi.org/10.1182/blood-2024-202353.
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Background and Aim:Patients with haematological disorders often require bone marrow aspirate (BMA) and trephine biopsy as part of diagnostics and assessment of disease relapse. Conventionally, marrow is assessed by morphology, immunophenotyping, cytogenetics, fluorescence in situ hybridization (FISH), and mutation analysis. However, in myeloid malignancies such as myelofibrosis or myelodysplastic syndrome (MDS) with fibrosis, BMA may be dry or haemodiluted without representative marrow fragments, leading to failure in analysis or inaccurate findings. In such instances, cut sections of bone core from trephine biopsy, besides providing histopathology, can be analysed for informative markers by FISH on the formalin-fixed paraffin-embedded blocks (FFPE-FISH). Methods:This is a retrospective study on patients followed up in Singapore General Hospital Department of Haematology who had informative results from FFPE-FISH performed on marrow bone core. FISH probes that detect chromosomal aberrations common in MDS, including chromosomes 5q, 7q, 20q, 17p, and trisomy 8, were used in the panel. In addition, FISH for X and Y chromosomes (X/Y-FISH) was done for patients with gender-mismatched haematopoietic stem cell transplant (HSCT). The result of FFPE-FISH was compared with FISH on BMA samples (asp-FISH), karyotype analysis, and a variable number of tandem repeat (VNTR) analysis. Results and Discussion:Ten HSCT patients had a total of 40 marrow samples taken at different timepoints. There was a total of 65 (39 MDS and 26 X/Y chromosome) FFPE-FISH tests performed. Fourteen BMA samples had both MDS and X/Y-FISH performed, 14 had only MDS FISH, while 12 had only X/Y-FISH done. Of 28 marrow samples with MDS FFPE-FISH done, 11 (39.3%) had results discordant from corresponding BMA tests, of which 90.9% (n=10) had disease detectable by FFPE-FISH which were missed on either asp-FISH, karyotyping or both. Conversely, FFPE-FISH failed to detect the expected complex karyotype only in 1 instance. Among these 11 discordant samples, 7 (63.6%) were dry or haemodiluted. On the other hand, for the 17 concordant samples, a significant number (n=8, 47.1%) did not have detectable MDS markers. For those with true disease presence, most (62.5%) had adequate BMA samples making asp-FISH and FFPE-FISH comparable. In the 26 FFPE-X/Y-FISH done on 7 patients with gender-mismatched HSCT, 22 (84.6%) had incomplete donor chimerism detected by FFPE-X/Y-FISH where other tests failed to. Amongst them, 11 had parallel asp-FISH done where 8 (72.7%) failed to detect incomplete chimerism (62.5% haemodiluted); 14 cases had successful karyotyping done where all missed the presence of recipient-gender metaphases (50% haemodiluted). Similarly, 100% (n=17) available VNTR analyses done (82.4% from BMA) erroneously reported complete donor chimerism. When analysed in totality, 6 of the 10 patients (60%), and 24 out of 40 unique bone marrow specimens (62.5%) would have had disease relapse or incomplete donor chimerism missed if FFPE-FISH had not been performed on the bone core. Conclusion:We have shown the usefulness of FFPE-FISH in the detection of disease relapse and falling donor chimerism post-HSCT, in the context of fibrotic or inadequate BMA samples. Our observation provides compelling evidence that FFPE-FISH should be performed in such patients with seemingly normal asp-FISH or karyotyping reports.
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Gao, Zihui, Chunhua Yang, Xiaobo Zhang, et al. "Establishment of a Rapid LAMP Assay for Aeromonas hydrophila and Comparison with the Application of qPCR." Metabolites 13, no. 7 (2023): 841. http://dx.doi.org/10.3390/metabo13070841.
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The development of an exceptionally sensitive diagnostic technique for early identification of aquaculture diseases, specifically Aeromonas hydrophila, is essential for efficient management of disease outbreaks at aquaculture locations. In this research, a swift and sensitive diagnostic assay employing Loop-mediated isothermal amplification (LAMP) of Aeromonas hydrophila was devised and compared to the conventional qPCR method documented by Rong Wang. Validation of the diagnostic assay was carried out using actual samples obtained from aquaculture fish. The findings revealed that based on the rapid detection of crude bacterial genomic DNA, the fluorescent LAMP assay possessed a lower limit of detection (LOD) of 0.559 ng/μL (0.315–1.693, 95% CI), while the LOD for qPCR stood at 4.301 ng/μL (2.084–8.876, 95% CI). Both techniques demonstrated outstanding specificity, exhibiting no cross-reactivity with bacteria from the same or closely related genera. A total of 74 fish samples suspected to be infected with the fish disease were gathered, with 26 and 23 samples testing positive for Aeromonas hydrophila via LAMP and qPCR, respectively. The concordance analysis for LAMP and qPCR methods generated a Kappa value of 0.909 (0.778–1.000, 95% CI), signifying a high degree of diagnostic consensus. This study highlights that the LAMP assay eliminates the thermal cycle temperature change process of qPCR, uses lysate to crudely extract bacterial genomic DNA, and can complete the detection within 40 min, rendering it a practical and efficient alternative for monitoring disease outbreaks at aquaculture sites.
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Duval, Eloïse, Simon Blanchet, Erwan Quéméré, et al. "An eDNA-based method for monitoring a salmonid infectious disease: Development and application." ARPHA Conference Abstracts 4 (March 4, 2021): e64797. https://doi.org/10.3897/aca.4.e64797.
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In the current context of global change, freshwater species are increasingly exposed to emerging infectious diseases (Okamura and Feist 2011). As an example, the Proliferative Kidney Disease (PKD) has emerged in salmonid fish during the last two decades, both in Europe and North America, causing important losses in aquaculture and worrying declines of several wild salmonid populations (Sudhagar et al. 2019). It is caused by <em>T</em><em>etracapsuloides bryosalmonae</em>, a myxozoan parasite with a complex life cycle involving two hosts: salmonids (intermediate host) and bryozoans (primary host). As PKD development strongly depends upon water temperature and quality, it is expected that global change could lead to more outbreaks (Okamura et al. 2011). Current monitoring of fish parasite load and infection status relies on histological observation or <em>T. bryosalmonae</em> DNA amplification out of kidney samples, involving fish euthanasia, and thus relatively small sample sizes when inferring infection prevalence. As large-scale screening of this parasite infections are required to better understand PKD dynamics, we have developed a non-lethal method for <em>T. bryosalmonae</em> detection in fish host based on the biological fact that <em>T. bryosalmonae</em> spores can be excreted from infected fish into the water through urine (Hedrick et al. 2004). This novel approach based on the detection of <em>T.</em> <em>bryosalmonae</em> DNA in fish urine was developed on wild brown trout (<em>Salmo trutta</em>)<em>, </em>a species known to be an intermediate host of <em>T. bryosalmonae </em>and for releasing infective spores (only towards bryozoan host) through urine (Okamura et al. 2011). Applying this method, we have been able to map wild brown trout infection prevalence across 50 sites at the foothill of French Pyrenees and to identify the main environmental drivers of this disease.
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Zhai, Jing. "UroVysion Multi-Target Fluorescence in situ Hybridization Assay for the Detection of Malignant Bile Duct Brushing Specimens: A Comparison with Routine Cytology." Acta Cytologica 62, no. 4 (2018): 295–301. http://dx.doi.org/10.1159/000488636.
Full textAbstract:
Objective: Routine bile duct brushing cytology is an important diagnostic tool in the evaluation of bile duct stricture. The purpose of this study was to evaluate the performance of the UroVysion fluorescence in situ hybridization (FISH) assay for the detection of malignant bile duct brushing specimens. Study Design: Thirty-five bile duct brushing specimens were included in the study. The FISH assay utilized the commercially available UroVysion probes. The indeterminate cytology results were considered as negative for statistical analysis. Results: Twenty-two of 35 patients were diagnosed as having malignancy based on tissue diagnosis or clinical progression of disease by image assessment. The sensitivity of routine cytology and FISH for the detection of malignancy was 14% (3/22) and 55% (12/22), respectively (p = 0.003). The specificity of routine cytology and FISH was 100% (13/13) and 62% (8/13), respectively (p = 0.025). The false-positive rate for routine cytology and FISH was 0% (0/13) and 38% (5/13), respectively. Conclusions: Our study shows that FISH is significantly more sensitive than routine cytology for the detection of malignancy in bile duct brushing specimens. However, in our study, the specificity of FISH was poor compared to the excellent specificity of routine cytology. The compromised specificity of FISH may limit its utility in the detection of malignant bile duct brushing specimens.
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APOSTOLIDI, EVANGELIA D., KONSTANTINA BITCHAVA, ELENI TSAVEA, et al. "Development of real-time qPCR assays for detecting and quantifying common bacterial pathogens in fish from Mediterranean aquacultures." Mediterranean Marine Science 25, no. 3 (2024): 732–39. https://doi.org/10.12681/mms.38053.
Full textAbstract:
The aquaculture industry is a rapidly developing and economically important sector for many countries, thus it requires effective solutions to overcome the key challenges that could impede its growth and sustainability. Among the most serious concerns are fish diseases caused by bacterial infections. This study introduces the development of novel rapid molecular methods employing Real-Time Polymerase Chain Reaction (qPCR) for the specific detection and quantification of five major fish pathogenic bacteria: Vibrio harveyi, V. alginolyticus, V. anguillarum, Photobacterium damselae, and Tenacibaculum maritimum. These bacteria are responsible for diseases such as vibriosis, photobacteriosis, and tenacibaculosis. The qPCR assays developed in this study are highly specific and extremely sensitive, making them suitable for early detection of these pathogens, thus aiding in the prevention of disease outbreaks in aquaculture farms.
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Chauffaille, Maria de Lourdes Lopes Ferrari, José Salvador Rodrigues Oliveira, Maura Romeo, and José Kerbauy. "Fluorescent in-situ hybridization (FISH) for BCR/ABL in chronic myeloid leukemia after bone marrow transplantation." Sao Paulo Medical Journal 119, no. 1 (2001): 16–18. http://dx.doi.org/10.1590/s1516-31802001000100005.
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CONTEXT: Identification of Philadelphia chromosome or BCR/ABL gene rearrangement in chronic myeloid leukemia is important at diagnosis as well as after treatment. OBJECTIVE: To compare the results of karyotyping using fluorescent in-situ hybridization (FISH) upon diagnosis and 1 year after bone marrow transplantation in 12 patients. TYPE OF STUDY: Diagnostic test and residual disease detection. SETTING: Hematology and Hemotherapy Department, Federal University of São Paulo/Escola Paulista de Medicina, São Paulo, Brazil. SAMPLE: 12 patients with chronic myeloid leukemia at diagnosis and 1 year after bone marrow transplantation. DIAGNOSTIC TEST: Karyotyping was done in the usual way and the BCR/ABL gene-specific probe was used for FISH. MAIN MEASUREMENTS: Disease at diagnosis and residual. RESULTS: At diagnosis, 10 patients presented t(9;22)(q34.1;q11) as well as positive FISH. Two cases did not have metaphases but FISH was positive. After bone marrow transplantation, 8 patients presented normal karyotype, 1 had persistence of identifiable Philadelphia chromosome and 3 had no metaphases. Two cases showed complete chimera and 2 had donor and host cells simultaneously. FISH was possible in all cases after bone marrow transplantation and confirmed the persistence of identifiable Philadelphia chromosome clone in one patient, and identified another that did not present metaphases for analysis. Cases that showed mixed chimera in karyotype were negative for BCR/ABL by FISH. CONCLUSION: The applicability of FISH is clear, particularly for residual disease detection. Classical and molecular cytogenetics are complementary methods.
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Latour, Robert J., David T. Gauthier, James Gartland, Christopher F. Bonzek, Kathleen A. McNamee, and Wolfgang K. Vogelbein. "Impacts of mycobacteriosis on the growth of striped bass (Morone saxatilis) in Chesapeake Bay." Canadian Journal of Fisheries and Aquatic Sciences 69, no. 2 (2012): 247–58. http://dx.doi.org/10.1139/f2011-158.
Full textAbstract:
The striped bass (Morone saxatilis) is an economically and ecologically valuable finfish species that inhabits nearshore and estuarine waters of many states along the US Atlantic coast. Chesapeake Bay provides extensive nursery and foraging habitats for striped bass, yet fish in the bay exhibit high prevalence of disease caused by bacteria in the genus Mycobacterium. Detection of population-level impacts associated with mycobacteriosis has been difficult because the disease is chronic and synoptic biological and disease data have been limited. Here, we present modeling analyses of growth data for disease-positive and -negative striped bass in Chesapeake Bay. Three growth relationships were considered, and for each, a single model was parameterized to include several covariates, most notably disease status and severity. Our results indicate that disease-positive and -negative fish have differing growth patterns and that the estimated asymptotic sizes of disease-positive fish are considerably lower than those of disease-negative fish. Compromised growth along with documentation that striped bass in Chesapeake Bay are experiencing disease-associated mortality suggests that disease may be reducing the productivity of this species.
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Rahman, Mohammad Rofi. "The Design Of Augmented Reality Media Koi Fish Literacy Using Fast Corner Algorithm." International Journal of Informatics and Computation 3, no. 1 (2021): 10. http://dx.doi.org/10.35842/ijicom.v3i1.32.
Full textAbstract:
Ornamental fish that are quite famous and in demand in the market is the koi fish. This fish has a relatively high economic value, and its demand is increasing. There are still many difficulties in maintaining this fish so that it can cause the growth of disease and even death in the fish. It is due to the lack of public attention in terms of literacy about koi fish. Researchers used augmented reality technology to design koi fish literacy media based on these problems using the FAST Corner algorithm. So it is hoped that it could help improve public literacy about koi fish by introducing real-time information. The Fast Corner detection algorithm is helpful to accelerate the computational time when detecting corners in real-time with the markerless Augmented Reality technique. In this technique, the marker used for object tracking has been replaced with pattern recognition or pattern recognition of an object. The study results showed that experiments using this algorithm could track targets with good and faster performance and a maximum level of accuracy.
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Nácher-Vázquez, Montserrat, Ana Barbosa, Inês Armelim, et al. "Development of a Novel Peptide Nucleic Acid Probe for the Detection of Legionella spp. in Water Samples." Microorganisms 10, no. 7 (2022): 1409. http://dx.doi.org/10.3390/microorganisms10071409.
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Legionella are opportunistic intracellular pathogens that are found throughout the environment. The Legionella contamination of water systems represents a serious social problem that can lead to severe diseases, which can manifest as both Pontiac fever and Legionnaires’ disease (LD) infections. Fluorescence in situ hybridization using nucleic acid mimic probes (NAM-FISH) is a powerful and versatile technique for bacterial detection. By optimizing a peptide nucleic acid (PNA) sequence based on fluorescently selective binding to specific bacterial rRNA sequences, we established a new PNA-FISH method that has been successfully designed for the specific detection of the genus Legionella. The LEG22 PNA probe has shown great theoretical performance, presenting 99.9% specificity and 96.9% sensitivity. We also demonstrated that the PNA-FISH approach presents a good signal-to-noise ratio when applied in artificially contaminated water samples directly on filtration membranes or after cells elution. For water samples with higher turbidity (from cooling tower water systems), there is still the need for further method optimization in order to detect cellular contents and to overcome interferents’ autofluorescence, which hinders probe signal visualization. Nevertheless, this work shows that the PNA-FISH approach could be a promising alternative for the rapid (3–4 h) and accurate detection of Legionella.
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Luo, Zhang, Xiaohui Bai, Shuang Hao, Mengyu Wang, Yongjiang Wu, and Hanchang Sun. "Two Genotypes of Streptococcus iniae Are the Causative Agents of Diseased Ornamental Fish, Green Terror Cichlid (Aequidens rivulatus)." Fishes 9, no. 4 (2024): 140. http://dx.doi.org/10.3390/fishes9040140.
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Green terror cichlid (Aequidens rivulatus) is a popular tropical freshwater ornamental fish. In 2021, an unknown disease was observed in cultured A. rivulatus in Tianjin, China, with a cumulative mortality rate of 25% within 7 days of onset. The main clinical signs were scale loss, skin ulceration, and slight bleeding. Histopathological observation revealed obvious damage to the liver, spleen, and kidney of diseased fish. In addition, abundant granulomas were observed in the spleen and head kidney of the diseased fish. To define the potential pathogens from A. rivulatus, bacteria were isolated from the visceral tissue of diseased fish with conventional methods. An artificial infection experiment was carried out to prove the pathogenicity of the isolated bacteria. The strains HG-2021-1 and HG-2021-3 were isolated from diseased fish and identified as being responsible for the disease. They were identified as Streptococcus iniae based on physiological and biochemical tests, lctO gene detection, and 16S rRNA gene sequence analysis. According to the result of multilocus sequence typing (MLST), HG-2021-1 and HG-2021-3 belong to different genotypes of S. iniae. Furthermore, they were found to contain the virulence genes pgmA, scpI, cpsD, and pdi, and the median lethal dose (LD50) for A. rivulatus was 1.8 × 106 Colony-Forming Units (CFU)/mL and 6.6 × 106 CFU/mL, respectively. To our knowledge, this is the first report of fish coinfected by two genotypes of S. iniae.