Application of computer-aided sperm analysis (CASA) for detecting sperm-immobilizing antibody

2018 ◽  
Vol 79 (3) ◽  
pp. e12814 ◽  
Author(s):  
Yu Wakimoto ◽  
Atsushi Fukui ◽  
Teruhito Kojima ◽  
Akiko Hasegawa ◽  
Minoru Shigeta ◽  
...  
2017 ◽  
Vol 124 ◽  
pp. 75
Author(s):  
Yu Wakimoto ◽  
Teruhito Kojima ◽  
Akiko Hasegawa ◽  
Atushi Fukui ◽  
Minoru Shigeta ◽  
...  

Author(s):  
Sharon T Mortimer ◽  
Christopher J De Jonge

2018 ◽  
Vol 30 (6) ◽  
pp. 867 ◽  
Author(s):  
M. T. Gallagher ◽  
D. J. Smith ◽  
J. C. Kirkman-Brown

The human semen sample carries a wealth of information of varying degrees of accessibility ranging from the traditional visual measures of count and motility to those that need a more computational approach, such as tracking the flagellar waveform. Although computer-aided sperm analysis (CASA) options are becoming more widespread, the gold standard for clinical semen analysis requires trained laboratory staff. In this review we characterise the key attitudes towards the use of CASA and set out areas in which CASA should, and should not, be used and improved. We provide an overview of the current CASA landscape, discussing clinical uses as well as potential areas for the clinical translation of existing research technologies. Finally, we discuss where we see potential for the future of CASA, and how the integration of mathematical modelling and new technologies, such as automated flagellar tracking, may open new doors in clinical semen analysis.


Andrologia ◽  
2018 ◽  
Vol 50 (10) ◽  
pp. e13141 ◽  
Author(s):  
Farren Hardneck ◽  
Gadieja Israel ◽  
Edmund Pool ◽  
Liana Maree

2018 ◽  
Vol 30 (1) ◽  
pp. 149
Author(s):  
M. L. Mphaphathi ◽  
M. M. Seshoka ◽  
T. R. Netshirovha ◽  
Z. C. Raphalalani ◽  
N. Bovula ◽  
...  

Subjective semen evaluation using standard optical microscopy is the most common practice. Semen parameters routinely assessed are volume, concentration, progressive motility, and morphology. However, computer-aided sperm analysis (CASA) represents an objective evaluation, sperm assessment that are reproducible and reliable. Such semen parameters have not been evaluated in Afrikaner, Brahman, and Bonsmara bulls’ semen. The present study evaluated the sperm motion and kinematics characteristics of semen from stud Afrikaner, Brahman, Bonsmara, and Nguni bulls using CASA technology. The electro-ejaculator was used for semen collection from Afrikaner (n = 11), Brahman (n = 7), Bonsmara (n = 10) and Nguni (n = 16) bulls of known and proven fertility. Semen was collected following 4 days of resting period. The bulls ranged between 5 and 6 years of age. After collection, the semen samples were immediately transferred to a thermo-flask and maintained at 37°C for further evaluation in the mobile laboratory (Nedambale, 2014). The CASA-Sperm Class Analyzer® system (Microptic, Barcelona, Spain) was used to evaluate sperm motion, velocity, and kinematic parameters or characteristics of raw/fresh semen from 4 cattle breeds. Data were analysed using GenStat® statistical programme (VSN International, Hemel Hempstead, United Kingdom). Treatment means were compared using one-way ANOVA. The total sperm motility rate was similar for all breeds: Afrikaner (92.2 ± 4.2), Brahman (90.7 ± 9.0), Bonsmara (93.9 ± 4.0), and Nguni (96.0 ± 2.7). However, Brahman and Afrikaner bull semen had higher sperm cells moving in a progressive motility of 57.3 and 45.6%, respectively, compared with other breeds (P < 0.05). Nguni, Afrikaner, and Bonsmara had the highest sperm cells moving in a rapid movement of 73.7, 72.4, and 67.4% (P > 0.05), respectively. The bulls sperm trajectories had a variation, as they were recorded to be irregular and not linear (P < 0.05). The straight-line sperm velocity (µm s−1), wobbling %, and amplitude of lateral head displacement % was similar for the 4 breeds (P > 0.05). In conclusion, CASA technology was a useful technique for assessing differences in sperm motion and kinematic (motility and velocity characteristics) among different bull breeds.


1993 ◽  
Vol 59 (5) ◽  
pp. 953-955 ◽  
Author(s):  
Russell O. Davis ◽  
David F. Katz

Cryobiology ◽  
2016 ◽  
Vol 72 (3) ◽  
pp. 232-238 ◽  
Author(s):  
Mokgadi Magdelin Seshoka ◽  
Masindi L. Mphaphathi ◽  
Tshimangadzo L. Nedambale

Chemosphere ◽  
2019 ◽  
Vol 214 ◽  
pp. 791-800 ◽  
Author(s):  
Zhen-Zhen Wan ◽  
Heng-Gui Chen ◽  
Wen-Qing Lu ◽  
Yi-Xin Wang ◽  
An Pan

Sign in / Sign up

Export Citation Format

Share Document