Handling Techniques to Reduce Stress in Mice.

Laboratory animals are subjected to multiple manipulations by scientists or animal care providers. The stress this causes can have profound effects on animal well-being and can also be a confounding factor for experimental variables such as anxiety measures. Over the years, handling techniques that minimize handling-related stress have been developed with a particular focus on rats, and little attention to mice. However, it has been shown that mice can be habituated to manipulations using handling techniques. Habituating mice to handling reduces stress, facilitates routine handling, improves animal wellbeing, decreases data variability, and improves experimental reliability. Despite beneficial effects of handling, the tail-pick up approach, which is particularly stressful, is still widely used. This paper provides a detailed description and demonstration of a newly developed mouse-handling technique intended to minimize the stress experienced by the animal during human interaction. This manual technique is performed over 3 days (3D-handling technique) and focuses on the animal's capacity to habituate to the experimenter. This study also shows the effect of previously established tunnel handling techniques (using a polycarbonate tunnel) and the tail-pick up technique. Specifically studied are their effects on anxiety-like behaviors, using behavioral tests (Elevated-Plus Maze and Novelty Suppressed Feeding), voluntary interaction with experimenters and physiological measurement (corticosterone levels). The 3D-handling technique and the tunnel handling technique reduced anxiety-like phenotypes. In the first experiment, using 6-month-old male mice, the 3D-handling technique significantly improved experimenter interaction. In the second experiment, using 2.5-month-old female, it reduced corticosterone levels. As such, the 3D-handling is a useful approach in scenarios where interaction with the experimenter is required or preferred, or where tunnel handling may not be possible during the experiment.

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.