Female squirrel monkeys as models for research on women’s pelvic floor disorders

2021 ◽  
pp. 002367722110325
Author(s):  
Gessiane Pereira da Silva ◽  
Thyago Habner de Souza Pereira ◽  
Ana Kelen Felipe Lima ◽  
Wilter Ricardo Russiano Vicente ◽  
Thomas J. Kuehl ◽  
...  

Animal models enable research on biological phenomena with controlled interventions not possible or ethical in patients. Among species used as experimental models, squirrel monkeys ( Saimiri genus) are phylogenetically related to humans and are relatively easily managed in captivity. Quadrupedal locomotion of squirrel monkeys resembles most other quadrupedal primates in that they utilize a diagonal sequence/diagonal couplets gait when walking on small branches. However, to assume a bipedal locomotion, the human pelvis has undergone evolutionary changes. Therefore, the pelvic bone morphology is not that similar between the female squirrel monkey and woman, but pelvic floor support structures and impacts of fetal size and malpresentation are similar. Thus, this review explores the pelvic floor support structural characteristics of female squirrel monkeys, especially in relation to childbirth to demonstrate similarities to humans.

2015 ◽  
Vol 213 (5) ◽  
pp. 735.e1-735.e8 ◽  
Author(s):  
Fiona M. Lindo ◽  
Emily S. Carr ◽  
Michelle Reyes ◽  
Jilene M. Gendron ◽  
Julio C. Ruiz ◽  
...  

2019 ◽  
Vol 9 (4) ◽  
pp. 20190022 ◽  
Author(s):  
Mark T. Gordon ◽  
John O. L. DeLancey ◽  
Aaron Renfroe ◽  
Andrew Battles ◽  
Luyun Chen

To develop an anatomically based customizable finite-element (FE) model of the pelvic floor support system to simulate pelvic organ prolapse (POP): POP-SIM1.0. This new simulation platform allows for the construction of an array of models that objectively represent the key anatomical and functional variation in women with and without prolapse to test pathomechanism hypotheses of the prolapse formation. POP-SIM1.0 consists of anatomically based FE models and a suite of Python-based tools developed to rapidly construct FE models by customizing the base model with desired structural parameters. Each model consists of anatomical structures from three support subsystems which can be customized based on magnetic resonance image measurements in women with and without prolapse. The customizable structural parameters include presence of levator ani (LA) avulsion, hiatus size, anterior vaginal wall dimension, attachment fascia length and apical location in addition to the tissue material properties and intra-abdominal pressure loading. After customization, the FE model was loaded with increasing intra-abdominal pressure (0–100 cmH 2 O) and solved using ABAQUS explicit solver. We were able to rapidly construct anatomically based FE models with specific structural geometry which reflects the morphology changes often observed in women with prolapse. At maximum loading, simulated structural deformations have similar anatomical characteristics to those observed during clinical exams and stress magnetic resonance images. Simulation results showed the presence of LA muscle avulsion negatively impacts the pelvic floor support. The normal model with intact muscle had the smallest exposed vaginal length of 11 mm, while the bilateral avulsion produced the largest exposed vaginal length at 24 mm. The unilateral avulsion model had an exposed vaginal length of 18 mm and also demonstrated a tipped perineal body similar to that seen in clinical observation. Increasing the hiatus size, vaginal wall length and fascia length also resulted in worse pelvic floor support, increasing the exposed vaginal length from 18 mm in the base model to 33 mm, 54 mm and 23.5 mm, respectively. The developed POP-SIM1.0 can simulate the anatomical structure changes often observed in women with prolapse. Preliminary results showed that the presence of LA avulsion, enlarged hiatus, longer vaginal wall and fascia length can result in larger prolapse at simulated maximum Valsalva.


The Lancet ◽  
1989 ◽  
Vol 333 (8639) ◽  
pp. 674 ◽  
Author(s):  
Luc Lesaffer

2021 ◽  
Vol 25 (02) ◽  
pp. 367-380
Author(s):  
Tiago Henrique da Silva Pires ◽  
◽  
Jansen Zuanon

Direct field observation constitutes an important source of information on life history and behavior of stream fish. Unfortunately, information gathered from direct field observations are often limited and thus may fail to provide robust testing for the causation of biological phenomena. Here, we bring concepts that form the theoretical foundation necessary for raising hypotheses in Evolutionary Behavioral Ecology,a study area directly derived from natural history and ethology that directly incorporates evolutionary processes and the adaptative value of behavioral patterns observed. This perspective allows the analysis of the evolutionary context and the adaptive value of observed behavioral patterns, and serves as the basis for new hypotheses that can be applied to many groups of organisms. Using this framework, field observations and experiments encompassing freshwater stream fish have subsidized many theories on the evolution of behavioral and morphological traits that are widespread debated by scientific community. After presenting the main key concepts, we bring some examples of research programs with freshwater fish that culminated in the formulation of important theories and where scientists took advantage of the opportunities provided by stream fish to conduct observations, samplings and controlled experiments both in the field and in captivity. We also describe our own research program, which uses an Amazonian freshwater stream fish (Crenuchus spilurus) as a model organism. By doing so, we advocate for the use of several methods and analyses for the study of Evolutionary Behavioral Ecology of freshwater stream fish.


Obstetrics ◽  
2017 ◽  
Author(s):  
Yu Chye Wah ◽  
Chew Heng Hai

2017 ◽  
Vol 27 (3) ◽  
pp. 257-261 ◽  
Author(s):  
Janet M. Shaw ◽  
Ingrid E. Nygaard

2019 ◽  
Vol 31 (3) ◽  
pp. 535-543 ◽  
Author(s):  
Carolyn W. Swenson ◽  
Mariana Masteling ◽  
John O. DeLancey ◽  
Lahari Nandikanti ◽  
Payton Schmidt ◽  
...  

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