Nociceptive-like behavior and analgesia in silver catfish (Rhamdia quelen)

Antinociceptive Analysis of Natural Monoterpenes Eugenol, Menthol, Carvacrol and Thymol in a Zebrafish Larval Model

In the last decade, a considerable number of studies have broadened our knowledge of the nociceptive mechanisms of pain, a global health problem in both humans and animals. The use of herbal compounds such as eugenol, menthol, thymol, and carvacrol as analgesic agents has accompanied the growing interest in this area, offering a possible solution for this complex problem. Here, we aimed to explore how these natural substances-at three different concentrations (2, 5 and 10 mg/L)-affect the pain responses in zebrafish (Danio rerio) larvae exposed to 0.05% acetic acid (AA) for 1 min. By analysing the activity of acetylcholinesterase (AChE), 5'-ectonucleotidase and NTPDases, as well as aversion and exploratory behaviours, it was observed that that although all substances were effective in counteracting the pain stimulus, the concentration range within which they do so might be very limited. Eugenol, despite its acknowledged properties in fish anaesthesia, failed to alleviate the pain stimulus at low concentrations. Contrastingly, menthol exhibited the most promising results at the lowest concentrations tested. Overall, it is concluded that menthol might be a good analgesic for this species, qualifying it as a substance of interest for prospective studies.

Treatment of Pain in Fish

This chapter provides an overview of our current understanding of clinical analgesic use in fish. Recently, the efficacy and pharmacokinetics of several analgesic drugs for use in fish have been investigated, and the most important data indicates that μ-opioid agonist drugs (e.g, morphine) are consistently effective as analgesics across fish species. In addition, bath application of some analgesic drugs may be useful, which affords multiple methods for delivering analgesics to fish. Although few published studies of non-steroidal anti-inflammatory drugs administered to fish show promise, we have much to learn about the analgesic efficacy of most drugs in this class.

The Opioid System in Rainbow Trout Telencephalon Is Probably Involved in the Hedonic Regulation of Food Intake

We hypothesize that opioids are involved in the regulation of food intake in fish through homeostatic and hedonic mechanisms. Therefore, we evaluated in rainbow trout (Oncorhynchus mykiss) hypothalamus and telencephalon changes in precursors, endogenous ligands and receptors of the opioid system under different situations aimed to induce changes in the homeostatic (through fasted/fed/refed fish) and hedonic (through feeding fish a control or a palatable high-fat diet) regulation of food intake. No major changes occurred in parameters assessed related with the nutritional condition of fish (fasted/fed/refed), allowing us to suggest that the opioid system seems not to have an important role in the homeostatic regulation of food intake in rainbow trout. The responses observed in telencephalon of rainbow trout fed the palatable high-fat diet included a decrease in mRNA abundance of the opioid precursor penka, in a way similar to that known in mammals, and increased mRNA abundance of the opioid receptors oprd1 and oprk1 supporting a role for telencephalic opioid system in the hedonic regulation of food intake in fish.

DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning

Behavioral measurement and evaluation are broadly used to understand brain functions in neuroscience, especially for investigations of movement disorders, social deficits, and mental diseases. Numerous commercial software and open-source programs have been developed for tracking the movement of laboratory animals, allowing animal behavior to be analyzed digitally. In vivo optical imaging and electrophysiological recording in freely behaving animals are now widely used to understand neural functions in circuits. However, it is always a challenge to accurately track the movement of an animal under certain complex conditions due to uneven environment illumination, variations in animal models, and interference from recording devices and experimenters. To overcome these challenges, we have developed a strategy to track the movement of an animal by combining a deep learning technique, the You Only Look Once (YOLO) algorithm, with a background subtraction algorithm, a method we label DeepBhvTracking. In our method, we first train the detector using manually labeled images and a pretrained deep-learning neural network combined with YOLO, then generate bounding boxes of the targets using the trained detector, and finally track the center of the targets by calculating their centroid in the bounding box using background subtraction. Using DeepBhvTracking, the movement of animals can be tracked accurately in complex environments and can be used in different behavior paradigms and for different animal models. Therefore, DeepBhvTracking can be broadly used in studies of neuroscience, medicine, and machine learning algorithms.