Machine Learning Methods for Automated Quantification of Ventricular Dimensions

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening

The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

Automated Zebrafish Phenotype Pattern Recognition: 6 Years Ago, and Now

The article assesses the developments in automated phenotype pattern recognition: Potential spikes in classification performance, even when facing the common small-scale biomedical data set, and as a reader, you will find out about changes in the development effort and complexity for researchers and practitioners. After reading, you will be aware of the benefits and unreasonable effectiveness and ease of use of an automated end-to-end deep learning pipeline for classification tasks of biomedical perception systems.

KaIDA: a modular tool for assisting image annotation in deep learning

Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehensive tool for assistance which is time- consuming, burdensome, and not intuitive. Using the here presented modular Karlsruhe Image Data Annotation (KaIDA) tool, for the first time assisted annotation in various image processing tasks is possible to support users during this process. It aims to simplify annotation, increase user efficiency, enhance annotation quality, and provide additional useful annotation-related functionalities. KaIDA is available open-source at https://git.scc.kit.edu/sc1357/kaida.

High-throughput screening paradigms in ecotoxicity testing: Emerging prospects and ongoing challenges

The rapidly increasing number of new production chemicals coupled with stringent implementation of global chemical management programs necessities a paradigm shift towards boarder uses of low-cost and high-throughput ecotoxicity testing strategies as well as deeper understanding of cellular and sub-cellular mechanisms of ecotoxicity that can be used in effective risk assessment. The latter will require automated acquisition of biological data, new capabilities for big data analysis as well as computational simulations capable of translating new data into in vivo relevance. However, very few efforts have been so far devoted into the development of automated bioanalytical systems in ecotoxicology. This is in stark contrast to standardized and high-throughput chemical screening and prioritization routines found in modern drug discovery pipelines. As a result, the high-throughput and high-content data acquisition in ecotoxicology is still in its infancy with limited examples focused on cell-free and cell-based assays. In this work we outline recent developments and emerging prospects of high-throughput bioanalytical approaches in ecotoxicology that reach beyond in vitro biotests. We discuss future importance of automated quantitative data acquisition for cell-free, cell-based as well as developments in phytotoxicity and in vivo biotests utilizing small aquatic model organisms. We also discuss recent innovations such as organs-on-a-chip technologies and existing challenges for emerging high-throughput ecotoxicity testing strategies. Lastly, we provide seminal examples of the small number of successful high-throughput implementations that have been employed in prioritization of chemicals and accelerated environmental risk assessment.

Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels

Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical image recognition, as datasets are generally small-scale, challenging to obtain, expensive to label, and frequently heterogeneously labeled. Furthermore, heterogeneous labels are a challenge for supervised methods. If not all classes are labeled for an individual sample, supervised deep learning approaches can only learn on a subset of the dataset with common labels for each individual sample; consequently, biomedical image recognition engineers need to be frugal concerning their label and ground truth requirements. This paper discusses the effects of frugal labeling and proposes to train neural networks for multi-class semantic segmentation on heterogeneously labeled data based on a novel objective function. The objective function combines a class asymmetric loss with the Dice loss. The approach is demonstrated for training on the sparse ground truth of a heterogeneous labeled dataset, training within a transfer learning setting, and the use-case of merging multiple heterogeneously labeled datasets. For this purpose, a biomedical small-scale, multi-class semantic segmentation dataset is utilized. The heartSeg dataset is based on the medaka fish’s position as a cardiac model system. Automating image recognition and semantic segmentation enables high-throughput experiments and is essential for biomedical research. Our approach and analysis show competitive results in supervised training regimes and encourage frugal labeling within biomedical image recognition.

Quantitative measurements of zebrafish heartrate and heart rate variability: A survey between 1990-2020

Zebrafish is an essential model organism for studying cardiovascular diseases, given its advantages of fast proliferation and high gene homology with humans. Zebrafish embryos/larvae are valuable experimental models used in toxicology studies to analyze drug toxicity, including hepatoxicity, nephrotoxicity and cardiotoxicity, as well as for drug discovery and drug safety screening in the preclinical stage. Heart rate (HR) serves as a functional endpoint in studies of cardiotoxicity, while heart rate variability (HRV) serves as an indicator of cardiac arrhythmia. Cardiotoxicity is a major cause of early and late termination of drug trials, so a more comprehensive understanding of zebrafish HR and HRV is important. This review summarized HR and HRV in a specific range of applications and fields, focusing on zebrafish heartbeat detection procedures, signal analysis technology and well-established commercial software, such as LabVIEW, Rvlpulse, and ZebraLab. We also compared HR detection algorithms and electrocardiography (ECG)-based methods of heart signal extraction. The relationship between HR and HRV was also systematically analyzed; HR was shown to have an inverse correlation with HRV. Applications to drug testing are also highlighted in this review. Furthermore, HR and HRV were shown to be regulated by the automatic nervous system; their connections with ECG measurements are also summarized herein.

Cytotoxic Evaluation and Anti-Angiogenic Effects of Two Furano-Sesquiterpenoids from Commiphora myrrh Resin

Commiphora myrrh resin (Myrrh) has been used in traditional Arabic medicine to treat various inflammatory diseases. Two furano-sesquiterpenoids, 2-methoxyfuranodiene (CM1) and 2-acetoxyfuranodiene (CM2), were isolated from the chloroform fraction of the ethanolic extract of Arabic Commiphora myrrh resin. The cytotoxicity of the compounds was evaluated using human liver carcinoma, breast cancer cells (HepG2 and MCF-7, respectively) and normal human umbilical vein endothelial cells (HUVECs) cell lines. The development toxicity and anti-angiogenic activity of both compounds were also evaluated using zebrafish embryos. Cell survival assays demonstrated that both compounds were highly cytotoxic in HepG2 and MCF7 cells, with IC₅₀ values of 3.6 and 4.4 µM, respectively. Both compounds induced apoptosis and caused cell cycle arrest in treated HepG2 cells, which was observed using flow cytometric analysis. The development toxicity in zebrafish embryos showed the chronic toxicity of both compounds. The toxicity was only seen when the embryos remained exposed to the compounds for more than three days. The compound CM2 showed a significant level of anti-angiogenic activity in transgenic zebrafish embryos at sublethal doses. Thus, we demonstrated the cytotoxic properties of both compounds, suggesting that the molecular mechanism of these compounds should be further assessed.