Methods for Assessing the Functional Capacity of Human Spermatozoa; Their Role in the Selection of Patients for In Vitro Fertilization

Author(s):  
R. J. Aitken ◽  
A. Templeton ◽  
R. Schats ◽  
F. Best ◽  
D. Richardson ◽  
...  
2007 ◽  
Vol 87 (6) ◽  
pp. 1314-1321 ◽  
Author(s):  
Claudine C. Hunault ◽  
Egbert R. te Velde ◽  
Sjerp M. Weima ◽  
Nicholas S. Macklon ◽  
Marinus J.C. Eijkemans ◽  
...  

1993 ◽  
Vol 8 (2) ◽  
pp. 253-257 ◽  
Author(s):  
C.J. De Jonge ◽  
S.M. Tarchala ◽  
R.G. Rawlins ◽  
Z. Binor ◽  
E. Radwanska

2021 ◽  
Author(s):  
Itay Erlich ◽  
Assaf Ben-Meir ◽  
Iris Har-Vardi ◽  
James A Grifo ◽  
Assaf Zaritsky

Automated live embryo imaging has transformed in-vitro fertilization (IVF) into a data-intensive field. Unlike clinicians who rank embryos from the same IVF cycle cohort based on the embryos visual quality and determine how many embryos to transfer based on clinical factors, machine learning solutions usually combine these steps by optimizing for implantation prediction and using the same model for ranking the embryos within a cohort. Here we establish that this strategy can lead to sub-optimal selection of embryos. We reveal that despite enhancing implantation prediction, inclusion of clinical properties hampers ranking. Moreover, we find that ambiguous labels of failed implantations, due to either low quality embryos or poor clinical factors, confound both the optimal ranking and even implantation prediction. To overcome these limitations, we propose conceptual and practical steps to enhance machine-learning driven IVF solutions. These consist of separating the optimizing of implantation from ranking by focusing on visual properties for ranking, and reducing label ambiguity.


2017 ◽  
Vol 52 (3) ◽  
pp. 209
Author(s):  
Reny I’tishom ◽  
Doddy M Soebadi ◽  
Aucky Hinting ◽  
Hamdani Lunardhi ◽  
Rina Yudiwati

One of the materials as potential candidates immunocontraception material is spermatozoa. Fertilin beta is spermatozoa membrane protein and is found only in mature spermatozoa and ejaculate, which serves as an adhesion molecule. Spermatozoa membrane protein that is used as an ingredient immunocontraception candidate, must have specific criteria that the specificity of spermatozoa, the role of antigen in the fertilization process, which includes the formation of immunogenicity sufficient antibody response has the potential to block fertilization. Antibodies against spermatozoa affect the stages before fertilization of the reproductive process and can hinder the development of the embryo after fertilization. Until now very little research data spermatozoa membrane protein as an ingredient immunocontraception are up to the test of experimental animals. The research objective is to prove the role of the resulting antibody induction of antibodies fertilin beta protein in the membrane of human spermatozoa induce agglutination and reduce motility thus reducing the number of in vitro fertilization. Research conducted at the IVF Laboratory, Department of Biology of Medicine, Faculty of Medicine, University of Airlangga. This research includes: Test the potential of antibody protein beta fertilin membrane of human spermatozoa and inhibit the role of antibodies in vitro fertilization in mice (Mus musculus Balb/c). In vitro studies have resulted in fertilization figure of 25% is smaller than the number that is equal to control fertilization of 58.7%, whereas previously the spermatozoa were incubated first with a beta membrane protein antibody fertilin human spermatozoa. While the percentage of inhibition of sperm to fertilize an oocyte by 33.75%. Potential imunokontraseptif considered effective if it decreased significantly (P <0.05) than the numbers fertilization in the treatment group compared with the control group. This shows fertilin beta membrane protein antibody has the ability to inhibit human spermatozoa to fertilize oocytes that reduce the number of fertilization.


2019 ◽  
Vol 21 (4) ◽  
pp. 200-209 ◽  
Author(s):  
Swati Viviyan Lagah ◽  
Tanushri Jerath Sood ◽  
Prabhat Palta ◽  
Manishi Mukesh ◽  
Manmohan Singh Chauhan ◽  
...  

2007 ◽  
Vol 88 ◽  
pp. S152
Author(s):  
E.B. Johnston-MacAnanny ◽  
A.J. DiLuigi ◽  
L.L. Engmann ◽  
D.B. Maier ◽  
C.A. Benadiva ◽  
...  

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