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Thyroxine, levothyroxine, and thyroxine complexed into cyclodextrin changed animal behavior, oxygen consumption, and photopic electroretinogram of Colossoma macropomum.

Thamiris Pinheiro SantosPriscila Rafaela Leão SoaresMarília Cordeiro Galvão da SilvaStephannie Caroline Barros Lucas da SilvaAndré Lucas Correa de AndradeAmanda Rodrigues Dos SantosJadson Freitas da SilvaElyda Grazyelle da Silva OliveiraElton Hugo Lima da Silva SouzaFabrício Bezerra de SáMarilia Ribeiro Sales CadenaPabyton Gonçalves Cadena
Published in: Fish physiology and biochemistry (2019)
The toxic effects of thyroxine (T4F), levothyroxine (L-T4), and thyroxine complexed into β-cyclodextrin (β-CD-T4) on the biological parameters of tambaqui (Colossoma macropomum) were evaluated. The animals were exposed to a chronic toxicity test based on concentrations of influent (60 ng/L) for 2 months. Weight, total length, animal behavior, oxygen consumption, photopic electroretinogram (ERG), and the Flicker exam were evaluated. No significant differences were observed (p > 0.05) on the weight and total length measurements between all groups studied. Behavioral observations of the animals exposed to L-T4 and β-CD-T4 complex showed a reduction (p < 0.05) in slow swimming and an increase in staying motionless events. The animals exposed to the β-CD-T4 complex presented the highest O2 consumption. L-T4 and β-CD-T4 promoted a reduction in the ability of the animals to respond to stimuli in the photoreceptors according to the photopic ERG examination. Data from the experimental Flicker exam showed no significant differences (p > 0.05) in all groups studied. It can be concluded that the complexation of T4 into β-CD and L-T4 modified the toxicity of this hormone, promoting changes in the behavior, oxygen consumption, and electrophysiological responses of the exposed animals, suggesting that inclusion complexes should be submitted to new toxicity tests to ensure higher safety.
Keyphrases
  • nk cells
  • oxidative stress
  • body mass index
  • physical activity
  • machine learning
  • electronic health record
  • deep learning
  • body weight