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Dr. Marcos [email protected]
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To achieve high implantation rates we need “healthy embryos”
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…the oocyte is a very large cell and the cytoplasm is perhaps more important than it’s nucleus…
…the oocyte is a very large cell and the cytoplasm is perhaps more important than it’s nucleus…
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We must consider redefining what are “healthy embryos”
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To identify healthy embryos with healthy cells we need objetivity
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Dark Field – Pictures taken every 5 minutes EEVA Bright Field – Pictures taken every 10 minutes -GERI
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Three main things we can do with time lapse technology
Learn & Write a New Manual on Embryo Assessment
Support the Diagnosis ofBest Embryos to Transfer
Discover, Research & Develop
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414 Cryo‐transfers
435 KID Blastocysts
Initial ZP Thickness
(µm)
Initial area (µm2)
Initiation
expansion (min)
Minimum ZP Thickness
(µm)
Maximumarea (µm2)
Morphokinetics of warmed blastocysts
Coello, Meseguer, Galan, Cobo ESHRE 2016
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Morphokinetics of warmed blastocysts
10
15
20
25
30
35
>9900 <9900Implantation rate (%)
Area (µm2)
Initial area
*
10
15
20
25
30
35
<18 um >18 um
Implantationrate
(%)
ZP thickness (µm)
Maximun ZP thickness
*
10
20
30
40
50
>19140 <19140Implantation rate (%)
Area (µm2)
Final Area
*
10
15
20
25
30
35
40
<12 um >12 um
Implantationrate
(%)
ZP thickness (µm)
Minimum ZP thickness
*
Coello, Meseguer, Galan, Cobo ESHRE 2016
PÁG.10Athayde Wirka et al. Fertil & Steril, 2014
Identify atypical phenotypes; Abnormal-cleavage
DC 1-3 Not DC 1-3
Implantationrate
2,9%
28,7%
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• AC1 and AC2 embryos are often selected for Day 3 transfer (28.6%)
• AC embryos are often good quality (46.9% 6-10 cells, ≤10% frag)
• Morphology is unable to detect AC embryos
• Implantation Rate: 3.7%
Abnormal Cleavage = Direct Cleavageconfirmation by automatic time-lapse
Blast Rate
ImplRate
Control (n=524) 43% 18%
With AC (n=115) 12% 4%
p-value <0.0001 0.05
Athayde Wirka et al. Fertil & Steril, In Press
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Identify atypical phenotypes; Blastocyst Contraction
Incidence; 19,1% of blastocysts contraction
*
J Marcos and M Meseguer 2015 Human Reporduction in Press
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Multinucleation
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1126 KID embryos analyzed
Nucleation status n %
NO MN 647 57.47
MN 479 42.53
Nucleation status n %
NO MN 963 85.6
MN 163 14,4
MN at 2 cells 127 77,9%
De novo MN 36 22,1%
reversibilityof =
multinucleation
(479-127)
479 = 73.4%
Multinucleation
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Multinucleation
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tEB
(107,94-112.99h)
tEB
(107,94-112.99h)
S3 R.O (0-3h)S3 R.O (0-3h)
A
72,2 %
A
72,2 %
B
66,2%
B
66,2%
S3 R.O (0-3h)S3 R.O (0-3h)
C
55,8%
C
55,8%
D
39,7%
D
39,7%
S3 = t8-t5
tEB
0
10
20
30
40
50
60
70
80
A B C D
72.266.2
55.8
39.7
Implantation rate
Tasas de implantación basadas en las diferentes categorías
Motato and Meseguer, ESHRE (2014)
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Blastocyst Selection Morphology vs Morphokinetics
tEB
(≤112.99h)
A
S3 R.O (≤ 3.0-5.67h)
A
72,2 %
B
66,2%
B
S3 R.O (≤ 3.0-5.67h)
C
55,8%
D
39,7%
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Blastocyst Selection Morphology vs Morphokinetics
Morphokinetics is superior than Morphology for blastocyst selection, when both combined morphology does not relate
with implantation
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Eeva Model; Bastocyst prediction
Eeva Morpho
HIgh High-Med Med-High Low
yes no
yes nono yes
cc2
9.33-11.47
s2
0-1.73h
s2
0-1.73h
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High
n= 158
• 46,8%*p<0,05
Medium
n= 114
• 36,8%
Low
n= 249
• 27,7%
Implantation rates according to Eeva categories: All transferred KID embryos n= 521
Implantation rates andEeva
B Aparicio-Ruiz and M Meseguer 2016 ESHRE
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Implantation rates according to Eeva categories: Logistic Regression analysis
Aparicio-Ruiz and Meseguer 2016 ESHRE
Model effect values OR p value
EEVA HIGH versus LOW 2.24 (1.44-3.47) >0.001
Day of Transfer Day 5 versus Day 3 2.17 (1.43-3.28) >0.001
Oocyte source Autologous versus Donation
3.17 (1.51-5,97) <0.001
Embryo morphology ASEBIR A vs. C 1.45 (0.79-2.65) ns
Eeva Algorithm is related with implantation potential independently of day of transfer, oocyte quality or
conventional embryo morphology which is not related with outcome when eeva is used
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Hypothesis
More Observations Better Selection
Less DisturbanceBetter Development
IndependentHEPA
particle filter
Accurate Temp Control (4 indep sensors)
Independent IncubatorsTrigasmixing
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Randomized Control Trial
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No NW data retrieved 1 4Time-lapse(N=216)*
Standard Incubator(N=162)
P
NewBorn delivery Rate 216/438 (49.3%) 162/405 (40.0%) 0.011Twins delivery 67/216 (31.0%) 40/162 (24.7%) NS
Female neonates 114 (45.2%) 51 (63.0%) <0.006
Birth weight (g) 3163 (3035-3292) 3074 (2913-3236) NS
Low birth weight (<2500g) 19 (12.8%) 15 (12.3%) NS
Very low birth weight (<1500g)
3 (2.0%) 3 (2.4%) NS
Neonatal height (cm) 50.3 (49.6-50.9) 49.7 (48.9-50.4) NS
Birth defects 1 (0.6%) 0 NS
F Insua and M Meseguer 2015 ESHRE
No comparable study on single point morphology observation
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=128)Mean CI95% Mean CI95% p-value[h] [h] [h] [h]
t4 38.8 37.9-39.6 40.5 39.6-41.4 0.009
t8 59.1 57.4-60.8 62.4 60.7-.64.2 0.008
tM 85.4 83.4-87.4 91.3 89.5-93.2 0.001
tSB 100.5 98.3-102.5 104.9 102.9-106.7 <0.001
tB 113.1 110.5-115.6 117.2 114.8-119.6<0.001
s2 1.14 0.8-1.5 2.15 1.6-2.70.001
Rienzi and M Meseguer 2016 Fertility (Submitted)
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tM
s2= t4-t3
Rienzi and M Meseguer 2016 Fertility (Submitted)
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Implantation in the categories of the hierarchical classification tree model applied in the
time lapse monitoring system, results are only referred to those euploid embryos with
known implantation (KID embryos). Xi Square p value=0.046
Blastocyst Category n Total n implanted % and CI95%
A 53 26 49.1 (35.7-61.6)
B 17 5 29.4 (7.7-51.1)
C 23 4 17.4 (1.9-32.9)
D 11 3 27.3 (1.0-53.6)
Distribution of euploid transferred embryos according to morphological categories
(ASEBIR). Xi Square p value=0.201
Blastocyst Category n Total n Implanted % and CI95%
A 55 23 41.8 (28.8-54.84)
B 40 14 35.0 (20.2-49.8)
C 9 1 11.1 (0.0-31.62)
D - - -
ROC CURVE VALUE AUC= 0.653
ROC CURVE VALUE AUC= 0.577
Rienzi and M Meseguer 2016 Fertility (Submitted)
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Implantation rates according to morphology and morphokinetics: Logistic Regression analysis
Model effect values OR p value
Morphokinetics A versus D 4.12 (1.39-12.24) >0.001
Maternal Age 1 year old 1.03 (1.43-3.28) ns
Embryo morphology ASEBIR A vs. C 1.19 (0.23-6.19) ns
Morphokinetics is superior than Morphology for blastocyst selection, when both combined morphology
does not relate with implantation
Rienzi and M Meseguer 2016 Fertility (Submitted)
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Fragmentation pattern
Bioanalytics on embryo development dynamics
Development of sub-cellular structures
Cellular shape dynamics
Computer vision can help us “see beyond”
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Computer vision can help us “see beyond”
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It is not easy to call attention of many embryologist like in this picture….
The evolution of TL incubators
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Water tank
• Six cameras with independentincubators
The evolution of TL incubators
LED- Orange led
Camera+Dish position
Second Dish Position Camera+Dish position
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One microscope per chamberOne microscope per chamber
11 focal planes of 96 micro-wells every 5 min11 focal planes of 96 micro-wells every 5 min
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Patient Name
The evolution of TL incubators
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4.9 MP Cameras (2560 x 1928)
4.9 MP Cameras (2560 x 1928)
Geri
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The evolution of Software analysis in TL
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The evolution of Software analysis in TL
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Question
If you have a time-lapse system available for clinicaluse in your lab, which type of patients/strategy will bebenefited/associated:
a) Patients with a previous IVF failure
b) Normal or high responder patients with more than 8embryos available to improve embryo selection
c) By using its blastocyst prediction abilities I will use it asa strategy to reach similar outcome than blastocysttransfer.
d) All patients would be benefitted due to the reducedembryo manipulation and improved culture conditions.