Related Experiment Video
Updated: Feb 12, 2026

Standardizing a Non-Lethal Method for Characterizing the Reproductive Status and Larval Development of Freshwater Mussels Bivalvia: Unionida
Published on: October 4, 2019
First molecular data for Lernaea cyprinacea (Copepoda: Cyclopoida) infesting Odontesthes bonariensis, a commercially
Iris Aparecida Soares1, Víctor Salinas2, Omar Del Ponti3
1Programa de Pós-graduação em Ciências Veterinárias, Universidade Federal Rural do Rio de Janeiro - UFRRJ, Seropédica, RJ, Brasil.
Abstract:
Parasitic copepods of the family Lernaeidae are often found infesting freshwater fishes worldwide. They cause lernaeosis, a disease that can lead to serious pathogenic effects on their fish hosts. The most common lernaeid is the Lernaea cyprinacea, which has been widely introduced through importation of tropical fishes, e.g. cyprinids. In South America, it is one of the most common parasites both in wild and in farmed fish in the central region of Argentina. The silverside Odontesthes bonariensis is the most important fish of the sport fisheries of Argentina and one of the fish most affected by lernaeosis. Six specimens of copepods were collected from 30 specimens of O. bonariensis collected in a Pampean shallow lake (33°25'28"S 62°53'56"W) of Córdoba (Argentina). The 28S rRNA gene of L. cyprinacea was amplified by means of PCR to obtain the 28S rDNA sequence. The sequence obtained of this parasite from Argentina showed high genetic similarity with those from various geographical origins. The present study provided molecular characterization of L. cyprinacea in South America for the first time.
More Related Videos
Related Concept Videos
Osmoregulation in Fishes
Molecular Models
Molecular Orbital Theory II
How Data are Classified: Categorical Data
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Molecular Orbital Theory I
Predicting Molecular Geometry

