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Kinetic Energy and the Free Energy Principle in the Birth of Human Life.
- Source :
- Reproductive Medicine; Jun2024, Vol. 5 Issue 2, p65-80, 16p
- Publication Year :
- 2024
-
Abstract
- The retrospective noninterventional study investigated the kinetic energy of video images of 18 fertilized eggs (7 were normal and 11 were abnormal) recorded by a time-lapse device leading up to the beginning of the first cleavage. The norm values of cytoplasmic particles were measured by the optical flow method. Three phase profiles for normal cases were found regarding the kinetic energy: 2.199 × 10<superscript>−24</superscript> ± 2.076 × 10<superscript>−24</superscript>, 2.369 × 10<superscript>−24</superscript> ± 1.255 × 10<superscript>−24</superscript>, and 1.078 × 10<superscript>−24</superscript> ± 4.720 × 10<superscript>−25</superscript> (J) for phases 1, 2, and 3, respectively. In phase 2, the energies were 2.369 × 10<superscript>−24</superscript> ± 1.255 × 10<superscript>−24</superscript> and 4.694 × 10<superscript>−24</superscript> ± 2.996 × 10<superscript>−24</superscript> (J) (mean ± SD, p = 0.0372), and the time required was 8.114 ± 2.937 and 6.018 ± 5.685 (H) (p = 0.0413) for the normal and abnormal cases, respectively. The kinetic energy change was considered a condition for applying the free energy principle, which states that for any self-organized system to be in equilibrium in its environment, it must minimize its informational free energy. The kinetic energy, while interpreting it in terms of the free energy principle suggesting clinical usefulness, would further our understanding of the phenomenon of fertilized egg development with respect to the birth of human life. [ABSTRACT FROM AUTHOR]
- Subjects :
- T-test (Statistics)
DYNAMICS
HUMAN beings
INFERTILITY
KRUSKAL-Wallis Test
RETROSPECTIVE studies
DESCRIPTIVE statistics
CHI-squared test
MANN Whitney U Test
ENERGY metabolism
HUMAN reproductive technology
HUMAN reproduction
FERTILIZATION in vitro
MEDICAL records
ACQUISITION of data
ANALYSIS of variance
MICROSCOPY
DATA analysis software
REGRESSION analysis
Subjects
Details
- Language :
- English
- ISSN :
- 26733897
- Volume :
- 5
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Reproductive Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 178183453
- Full Text :
- https://doi.org/10.3390/reprodmed5020008