Monitoring stress in fish by applying image analysis to their skin mucous cells
AbstractSeveral authors have previously demonstrated that the number of the skin mucous cells of fish is affected by many stressors. In the present study, two experiments were conducted in order to examine the effects of two common environmental conditions on the morphology of skin of sea bass and particularly on the number and diameter of skin mucous cells. In the first experiment, two groups of sea bass (mean weight 155.6±10.3 g SD) were maintained in two different concentrations of nitrate, 100 and 700 ppm respectively, for 48 h, while a third group was used as control. In the second experiment, sea bass (initial mean weight 78.9±3.1 g SD) were divided into four groups and each group was maintained in a different level of oxygen for 9 weeks. The oxygen concentration in each group was: 3.6±0.2 ppm, 4.7±0.2 ppm, 6.2±0.2 ppm and 8.2±0.2 ppm. In both experiments the effects of the two environmental factors on the morphology of the fish skin were examined histologically and a software containing a visual basic script macro, allowing quantification of the skin mucous cells, was used to analyze the skin tissue sections. Concerning the overall morphology of the skin and the diameter of the skin mucous cells, no differences were noted in both experiments (P>0.05). It was demonstrated however, that fish maintained in the lowest oxygen level and fish maintained in the highest concentration of nitrate exhibited significantly increased number of mucous cells per skin area (mm2). There is evidence that the enumeration of the skin mucous cells of fish can be used to monitor stress in fish.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2010 I. N. Vatsos, Y. Kotzamanis, M. Henry, P. Angelidis, M. Alexis
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.