Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization

Deep learning dives: Predicting anxiety in zebrafish through novel tank assay analysis

Behavior is fundamental to neuroscience research, providing insights into the mechanisms underlying thoughts, actions and responses. Various model organisms, including mice, flies, and fish, are employed to understand these mechanisms. Zebrafish, in particular, serve as a valuable model for studying anxiety-like behavior, typically measured through the novel tank diving (NTD) assay. Traditional methods for analyzing NTD assays are either manually intensive or costly when using specialized software. To address these limitations, it is useful to develop methods for the automated analysis of zebrafish NTD assays using deep-learning models. In this study, we classified zebrafish based on their anxiety levels using DeepLabCut. Subsequently, based on a training dataset of image frames, we compared deep-learning models to identify the model best suited to classify zebrafish as anxious or non anxious and found that specific architectures, such as InceptionV3, are able to effectively perform this classification task. Our findings suggest that these deep learning models hold promise for automated behavioral analysis in zebrafish, offering an efficient and cost-effective alternative to traditional methods.

Transfer from ciliate to zebrafish: Unveiling mechanisms and combined effects of microplastics and heavy metals

The impacts and toxicological mechanisms of microplastics (MPs) or heavy metals on aquatic ecosystems have been the subject of extensive research and initial understanding. However, the combined toxicity of co-pollutants on organisms and cumulative toxic effects along the food chain are still underexplored. In this study, the ciliate protozoan Paramecium caudatum and zebrafish Danio rerio were used to represent the microbial loop and the higher trophic level, respectively, to illustrate the progressive exposure of MPs and cadmium (Cd2+). The findings indicate that MPs (ca. 1 ×105 items/L) containing with Cd2+ (below 0.1 µg/L) could permeate the bodies of zebrafish through trophic levels after primary ingestion by ciliates. This could cause adverse effects on zebrafish, including alterations in bioindicators (total sugar, triglycerides, lactate, and glycogen) associated with metabolism, delayed hepatic development, disruption of intestinal microbiota, DNA damage, inflammatory responses, and abnormal cellular apoptosis. In addition, the potential risks associated with the transfer of composite pollutants through the microbial loop into traditional food chain were examined, offering novel insights on the evaluation of the ecological risks associated with MPs. As observed, understanding the bioaccumulation and toxic effects of combined pollutants in zebrafish holds crucial implications for food safety and human health.

Exploring the use of deep learning models for accurate tracking of 3D zebrafish trajectories

Zebrafish are ideal model organisms for various fields of biological research, including genetics, neural transmission patterns, disease and drug testing, and heart disease studies, because of their unique ability to regenerate cardiac muscle. Tracking zebrafish trajectories is essential for understanding their behavior, physiological states, and disease associations. While 2D tracking methods are limited, 3D tracking provides more accurate descriptions of their movements, leading to a comprehensive understanding of their behavior. In this study, we used deep learning models to track the 3D movements of zebrafish. Videos were captured by two custom-made cameras, and 21,360 images were labeled for the dataset. The YOLOv7 model was trained using hyperparameter tuning, with the top- and side-view camera models trained using the v7x.pt and v7.pt weights, respectively, over 300 iterations with 10,680 data points each. The models achieved impressive results, with an accuracy of 98.7% and a recall of 98.1% based on the test set. The collected data were also used to generate dynamic 3D trajectories. Based on a test set with 3,632 3D coordinates, the final model detected 173.11% more coordinates than the initial model. Compared to the ground truth, the maximum and minimum errors decreased by 97.39% and 86.36%, respectively, and the average error decreased by 90.5%.This study presents a feasible 3D tracking method for zebrafish trajectories. The results can be used for further analysis of movement-related behavioral data, contributing to experimental research utilizing zebrafish.

Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species

Detecting skeletal or bone-related deformities in model and aquaculture fish is vital for numerous biomedical studies. In biomedical research, model fish with bone-related disorders are potential indicators of various chemically induced toxins in their environment or poor dietary conditions. In aquaculture, skeletal deformities are affecting fish health, and economic losses are incurred by fish farmers. This survey paper focuses on showcasing the cutting-edge image analysis tools and techniques based on artificial intelligence that are currently applied in the analysis of bone-related deformities in aquaculture and model fish. These methods and tools play a significant role in improving research by automating various aspects of the analysis. This paper also sheds light on some of the hurdles faced when dealing with high-content bioimages and explores potential solutions to overcome these challenges.