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  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2013
    In:  Human Reproduction Vol. 28, No. suppl 1 ( 2013-06-01), p. i311-i356
    In: Human Reproduction, Oxford University Press (OUP), Vol. 28, No. suppl 1 ( 2013-06-01), p. i311-i356
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2013
    detail.hit.zdb_id: 632776-X
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  • 2
    In: Human Reproduction, Oxford University Press (OUP), Vol. 37, No. Supplement_1 ( 2022-06-29)
    Abstract: Which is the most predictive parameter when machine learning (ML) is applied to a known implantation database (KID) of day 5 embryo transfer database in an egg donation program? Summary answer Time to hatching (tiHB) is the most predictive embryonic parameter when machine learning algorithms were used on reproductive data in an oocyte donation program. What is known already Artificial intelligence is becoming an encouraging tool in medicine, also in ART, where the amount of data generated in the IVF lab has dramatically increase, favored by time-lapse technology. Numerous embryo selections algorithms based on logistic regressions have been developed for predicting blastocyst formation and implantation potential, but with machine learning, we can train algorithms and connect different morphological and morphokinetic embryo parameters with implantation or even live birth embryo potential. The aim of this study was to test machine learning algorithms and to identify predictive embryonic morphokinetic parameters when comparing the different models generated after machine learning analysis. Study design, size, duration Retrospective analysis of 405 embryos in a KID obtained after 392 embryo-transfers (13 double and 379 single-ET) performed in an oocyte donation program in 4 fertility clinics (year 2021). Recipientś average age: 42.2±4.2 years. The embryos were cultured in Global® Total® culture medium in Geri® (Genea Biomedx) time-lapse incubators after ICSI until embryo transfer at blastocyst stage. Only sperm samples & gt;1x106 spermatozoa/ml were included. All parameters were registered by one single trained senior embryologist. Participants/materials, setting, methods Thirty-five variables were initially analyzed: classic morphokinetic markers, time intervals (including total thinning time before hatching: tiHB-tFB and total blastulation time before hatching: tiHB-Tcav) and morphological measurements (blastocyst and inner cell mass diameter 110h post-injection). Eighty percent of the data was used for model training and 20% was reserved for model validation. Twelve supervised and unsupervised predictive machine learning models were developed. The software used to carry out the analysis was SPSS (v20.0) R (4.0.5). Main results and the role of chance The basic characteristics of the embryo population were similar. From the 405 embryos transferred, 216 blastocysts came from vitrified oocytes (53.3%). The implantation rate was 57.03% (231 gestational sacs) and the miscarriage rate was 16.8%. The classification-supervised algorithms applied included binary logistic regression, neural networks, support vector machines, neighborhood-based methods, classification trees, boosting and bagging methods. The algorithms were optimized by minimizing the AUC. Cluster analysis (unsupervised) was also performed. In the 6 best predictive models, the variables with the highest relevance were tiHB (hatching initiation time), tiHB-tFB and tiHB-tcav: variables related to hatching initiation. Furthermore, in the cluster analysis, these three variables appeared grouped in the same cluster. From the blastocysts population that implanted, 57.6% (133/231) were initiating hatching, while from those embryos that did not, only 42.3% (74/174) began the hatching process. Other variables such as the diameter of the transferred blastocyst, which we assumed to be valuable as an objective morphological parameter, did not show a high predictive capacity in the models obtained. Blastocyst average diameter of implanting blastocysts was 157.9±24.9 µm and non-implanting was 153.9±26.1 µm. Limitations, reasons for caution Morphology and morphokinetic parameters require subjective annotation and thus might have intrinsic intra-reader variability. Our findings need to be validated prospectively. Wider implications of the findings Time to blastocyst hatching appears to have significative impact in most ML predictive models. Hatching-related variables seems to have predictive power. Despite numerous variables influencing IVF outcome (intrinsic and extrinsic to embryo development) ML and AI approaches may improve the prioritization of the most viable embryo favoring single embryo transfer. Trial registration number 2021ibmad001
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 632776-X
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  • 3
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-04-26)
    Abstract: The factors that cause a preterm birth (PTB) are not completely understood up to date. Moreover, PTB is more common in pregnancies achieved by in-vitro fertilization (IVF) than in spontaneous pregnancies. Our aim was to compare the composition of vaginal microbiome at 12 weeks of gestation between women who conceived naturally or through IVF in order to study whether IVF PTB-risk could be related to vaginal microbiome composition. We performed an observational, prospective and multicentre study among two public hospitals and a fertility private clinic in Spain. Vaginal swabs from 64 pregnant women at 12 weeks of gestation were collected to analyse the microbiome composition by sequencing the V3–V4 region of the 16S rRNA. Our results showed that the vaginal microbiome signature at 12 weeks of pregnancy was different from women who conceived naturally or through IVF. The beta diversity and the genus composition were different between both cohorts. Gardnerella , Neisseria , Prevotella , and Staphylococcus genus were enriched genus in the vaginal microbiome from the IVF group, allowing us to create a balance model to predict both cohorts. Moreover, at species level the L. iners abundance was higher and L. gasseri was lower in the IVF group. As a conclusion, our findings were consistent with a proposed framework in which IVF pregnancy are related to risk for preterm birth (PTB) suggesting vaginal microbiome could be the reason to the relation between IVF pregnancy and risk for PTB.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 4
    In: Human Reproduction, Oxford University Press (OUP), Vol. 37, No. Supplement_1 ( 2022-06-29)
    Abstract: Are there differences in the vaginal microbiome of pregnant women who had a spontaneous pregnancy compared to those who required IVF? Summary answer The composition of the vaginal microbiome at 12 week's gestation is different in women who achieve the pregnancy spontaneously or by IVF. What is known already The vaginal microbiome plays an important role in women's reproductive health, finding associations between different microbiome patterns and the presence of infertility and embryo implantation failure in IVF. Additionally, recent studies show a correlation between obstetrics and perinatal outcomes and the composition of vaginal microbiota in pregnant women, as well as an increased risk of obstetrics complications in pregnant women after IVF. Study design, size, duration Observational, prospective and multicentre study. A total of 64 women were enrolled between January 2020 and June 2021. Spontaneous pregnancies n = 30; and IVF pregnancies n = 34. Participants/materials, setting, methods Vaginal swabs were obtained by speculum exam at 12 weeks of gestation in two public hospitals and a fertility private clinic in Spain, to evaluate the differences in vaginal microbiome between both cohorts. The microbiome composition was analyzed by sequencing the V3-V4 region of the 16S rRNA on the Illumina MiSeq platform. Main results and the role of chance There were no significant differences in socio-demographic characteristics between groups, except for an expected higher maternal age in the IVF cohort. Lactobacillus was the most prevalent genus in both groups. When we compared the beta diversity of vaginal microbial by cohort a significant difference was obtained (p = 0.001). Gardenella, Neisseria, Prevotella and Staphyloccocus were significantly enriched in the IVF group (p = 0.01). A further evaluation of the four most abundant Lactobacillus species showed that Lactobacillus iners was dominant in IVF pregnancies (15.2%) compared to spontaneous (9.8%) (p = 0.002). On the other hand, Lactobacillus  gasseri showed a lower abundance in vaginal microbiome from women belonged to IVF (9.2%) vs spontaneous pregnant group (13.8%) (p = 0.005). These findings allowed us to create a model to identify a microbial signature. This model is able to discriminate between IVF and spontaneous pregnancies. Limitations, reasons for caution The main limitation of our study is the small sample size. Larger studies are needed to corroborate our findings and their relationship with important aspects such as obstetric and perinatal complications. Wider implications of the findings The microbiome composition is different between both cohorts. The microbiome found in our IVF cohort has been also associated with obstetric complications as preterm delivery in previous studies. This suggest that the microbiome composition could be a plausible etiology for a higher risk of adverse pregnancy outcomes in IVF patients. Trial registration number Not applicable
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 632776-X
    detail.hit.zdb_id: 1484864-8
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  • 5
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2021
    In:  Human Reproduction Vol. 36, No. Supplement_1 ( 2021-08-06)
    In: Human Reproduction, Oxford University Press (OUP), Vol. 36, No. Supplement_1 ( 2021-08-06)
    Abstract: Could patient suffering unexplained recurrent fetal malformations be benefit of PGT-M by exome sequencing mutations identification? Summary answer Patients suffering unexplained recurrent fetal malformations could be benefit of the use of exome sequencing in combination to PGT-M to have a healthy live birth. What is known already Fetal malformations account for approximately 3% of live births and causes include: chromosomal abnormalities, exposure to toxic substances or teratogens and infections. Recently, studies have shown that several monogenic diseases are linked to fetal abnormalities. However, because of the large number of potential genes, genetic testing is challenging. Exome sequencing is widely used to detect genetic mutations and has emerged as a useful tool for finding the genetic cause of fetal abnormalities. The aim of this study was to show how exome sequencing in patients suffering unexplained recurrent fetal malformations in combination to PGT-M could lead to successful healthy newborn. Study design, size, duration Case report of a non-consanguineous couple with unexplained, recurrent fetal malformations. Couple were recruited during clinical consultation for unexplained recurrent fetal malformations at a private reproductive medicine clinic. The couple had two malformed fetus with the same congenital abnormalities: hydrocephalus, cerebellar vermis agenesis, cerebellar hypoplasia and enlarged cisterna magna. Patients signed written informed consent regarding to exome testing. For fetal sample, informed consent was obtained from parents. Participants/materials, setting, methods Sample of the affected fetus were provided. Parental genomic DNA was extracted from peripheral blood. Exome sequencing was performed using TrusightOne (Illumina®). FASTAQ data were processed through BWA and GATK algorithm. VCF files were analysed using Variant Interpreter software. After genetic counselling, PGT-M was performed using linkage polymorphic markers analysis and mutation sequencing. Embryo biopsy was carried at blastocyst stage. Embryos were vitrified and one healthy embryo was thaw and transfer in a subsequent cycle. Main results and the role of chance An homozygous novel pathogenic mutation c.641 C & gt;T (p.Ala214Val) in FVLCR2 gene was found. The parents were heterozygous carriers revealing that the detected variant follow an autosomal recessive pattern. The FLVCR2 (14q24.3) gene encodes a transmembrane protein that belongs to the major facilitator superfamily of secondary carriers that transport small solutes in response to chemiosmosis ion gradients, such as calcium. Mutations in this gene are related to fetal central nervous system defects. This disorder is diagnosed prenatally and is lethal. PGT-M was recommended during genetic counselling. After control ovarian stimulation 14 oocytes were retrieved and finally 4 embryos were suitable for embryo biopsy at blastocyst stage. One embryo was diagnosed as healthy, two affected and one heterozygous carrier. The healthy embryo was thaw and transferred and a healthy male baby was born. Limitations, reasons for caution Exome sequencing has technical limitations: only covers mutations in coding regions and does not cover noncoding regions of the genome. It also cannot reliably detect copy-number variants at single gene level. Wider implications of the findings: This study offers strong evidence of exome-sequencing as a new diagnostic strategy and powerful tool discovering the underlying etiology of recurrent fetal malformations and identifying new genes important for human development. Using this strategy in combination with PGT-M, clinicians can help couples with recurrent fetal malformations to have healthy newborns. Trial registration number Not applicable
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 632776-X
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  • 6
    In: Human Reproduction, Oxford University Press (OUP), Vol. 39, No. Supplement_1 ( 2024-07-03)
    Abstract: Do luteal phase (LF) embryos have the same outcomes in laboratory and morphokinetics parameters and clinical and genetic rates that follicular phase (FF) embryos? Summary answer Laboratory outcomes, ploidy and clinical rates result similar between two phases. In morphokinetic analysis we obtained significant differences in the duration of visible pronuclei. What is known already Dual stimulation (DS) protocols are being proposed as a useful strategy in patients with poor prognosis, particularly in cases of low response or low ovarian reserve. Nevertheless, there is still a lack of solid evidence for their use. In this context, it is interesting to analyse if the embryos from the LF achieve the same results as those from the FF. Our previous studies have shown comparable data, although they included vitrified oocytes. To avoid the impact of vitrification, we only review those cycles with fresh oocytes. A morphokinetic analysis is also added to compare their development. Study design, size, duration This multicentre retrospective evaluated 81 cycles of DS and PGT-A in patients with some poor prognostic feature (Antimüllerian hormone & lt;1ng/ml, antral follicle count & lt;6, poor response, & gt;39 years) with 161 pick-ups between January 2022-August 2023 (81 in FF and 80 in LF). A total of 398 embryos were biopsied (195 from FF and 203 from LF) and 59 frozen embryo transfers were performed (33 in FF and 26 in FL) between January 2022-December 2023. Participants/materials, setting, methods We compared several parameters between the two groups including gamete age, oocyte number, MII number, fertilization rate, blastocyst quality, biopsy rates, and genetic results. Additionally, clinical outcomes after embryo cryotransfer were also studied. We compared classical morphokinetic parameters as well. We performed this preliminary analysis on 226 embryos (99 FF, 127 LF) in two ways: comparing FF and LF embryos as two groups and comparing by patient within the same cycle in a paired analysis. Main results and the role of chance The average age of the patients was 40.5 (±2.3). No significant differences were found in terms of oocytes retrieved per patient (8.7 ±4.9 vs 9.2±5.9), maturity (7.2 ±4 vs 7.7±4.3) or number of embryo biopsied (6.54 ±3.9 vs 6.9±4.4). After genetic analysis, euploidy rate was found to be lower in LF compared to FF although not statistically significant (23.6% and 28.6%). Aneuploidy (67.4% vs 71.4%) and mosaicism rates (22.3% vs 19.1%) were also comparable between the two groups. In the same way, we did not find any statistically significant differences in the day of transfer, quality of the embryo transfer, neither pregnancy rates (54.5% vs 50%) nor life birth rate (54.5% vs 38.5%). In the preliminary morphokinetic analysis we only obtained significant differences in the time in which pronuclei were visible, being greater in the FF (16±3h vs 15±3h, p = 0.005/p=0.004 after adjusting for confounding variables). In addition, it appears that the pronuclei of the FF embryos tend to take longer to disappear. Also, these FF embryos complete the third cell cycle in a slower way than LF embryos, although none of these comparisons are statistically significant (p = 0.065 and p = 0.068). In the paired analysis we didn’t find any statistically significant difference. Limitations, reasons for caution This study is limited by its retrospective nature. Validation by a prospective study is recommended. Regarding morphokinetics, it is necessary to increase the number of embryos analysed to increase the statistical power of the study. Wider implications of the findings It is interesting to analyse LF embryos’ morphokinetics since there are few studies in this regard. Moreover, it is still unclear whether the oocytes obtained from the LF cycles are the result of the second stimulation or a persist effect of the first one, which could somehow affect embryo development. Trial registration number Not applicable
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
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  • 7
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2023
    In:  Human Reproduction Vol. 38, No. Supplement_1 ( 2023-06-22)
    In: Human Reproduction, Oxford University Press (OUP), Vol. 38, No. Supplement_1 ( 2023-06-22)
    Abstract: Can an artificial intelligence (AI)-based model predict the risk of suboptimal ovarian response using patient genetic data? Summary answer AI-based predictive models can identify relevant predicting factors for suboptimal ovarian response, including several genetic variants. What is known already A substantial proportion of women classified as “normo-responders” may not reach the optimal range of oocytes retrieved after COS. Polyzos and Sunkara (2015) defined a new category of women (15-36%) with a worse prognosis and a “suboptimal” level of ovarian response in which the number of oocytes retrieved ranges between 4 and 9 oocytes. The explanation for this behavior has been suggested to be genetic and indeed variants in FSH receptor and LH subunit-B have been identified that predispose to lower ovarian sensitivity to stimulation. Therefore, these women might require higher doses of gonadotropins or longer stimulations. Study design, size, duration This observational study included a retrospective analysis of 1370 ovarian stimulations from egg donors performed between March 2018 and April 2022. The oocyte donor candidates were selected according to our clinic donation program requirements and ASRM and ESHRE guidelines for oocyte donation. They were stimulated following a progesterone-primed ovarian stimulation protocol. All donors started stimulation with 150-300 IU/day of FSH according to AFC and BMI. A GnRH agonist was used for final oocyte maturation. Participants/materials, setting, methods Oocyte donors were healthy women between 18 and 35 years old (n = 504). In order to establish the predictive machine learning models, patient characteristics and controlled ovarian stimulation data were recorded in a data frame. In addition, donors were genotyped for 31 variants corresponding to 16 genes associated with ovarian response. The association between the different variables and risk of suboptimal ovarian response was analysed using SPSS (v23.0) and R (v. 4.2.0) statistical software. Main results and the role of chance The oocyte donors had a mean age of 25.4±4.0 and a high ovarian reserve (AFC: 18.4±7). The mean number of oocytes retrieved after ovarian stimulation was 16.3±7.5, of which 13.1±6.4 were mature. Despite being young women with good ovarian reserve, 16.4% of the stimulations were suboptimal (4-9 oocytes retrieved). Classical statistical method (binary logistic regression) and 5 different supervised classification machine learning algorithms (multi-layer perceptron, support vector-machines, k-nearst neighbors, random forest and eXtreme Gradient Boosting (XGBoost)) were used to establish a prediction model for suboptimal ovarian response. The model with the highest AUC value was the XGBoost (0.876). The accuracy, sensitivity and specificity were 0.798, 0.809 and 0.787 respectively. The variables that had the greatest predictive power in the best machine learning algorithm (XGBoost) were: age, BMI, AFC and total gonadotrophin dose. In addition, four genetic variants were identified that modified the risk of a suboptimal ovarian response: -ESR2 (oestrogen receptor; c.*39G & gt;A). -LHB (LH beta subunit; c.82T & gt;C/p.Trp28Arg). -SOD2 (superoxide dismutase; c.47T & gt;C/p.Val16Ala). A mitochondrial enzyme that catalyses the detoxification and protection against redox damage that can occur in COS. -and TP53 (tumour suppressor protein; c.215C & gt;T/p.Pro72Leu) that exerts a protective effect on DNA damage in folliculogenesis. Limitations, reasons for caution Our investigation was performed with retrospectively collected data, and hence it will be of importance to collect data prospectively to confirm that the new identified variants are associated with suboptimal ovarian response also in the IVF population. Wider implications of the findings The combination of AI and pharmacogenetics has led to the identification of genetic variants that might predispose to suboptimal ovarian response. Women who carry these variants (erroneously considered as normo-responders) could be candidates for personalization of their ovarian stimulation treatment with higher doses of gonadotrophins and/or longer stimulations. Trial registration number Not applicable
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 632776-X
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  • 8
    In: Human Reproduction, Oxford University Press (OUP), Vol. 36, No. Supplement_1 ( 2021-08-06)
    Abstract: What is the effect of artificial laser-assisted collapse before vitrification on pregnancy and implantation rates after transfer of vitrified-warmed blastocysts? Summary answer The artificial shrinkage by laser-induced collapse before vitrification significantly increased the implantation and clinical pregnancy rates after single thawed embryo transfer. What is known already Freeze all, cycle segmentation and, in general, single embryo transfer (SET) strategies (for example trophectoderm biopsy-based aneuploidy testing) have targeted blastocysts vitrification as the best option for reproductive practice worldwide. Artificial shrinkage seems to be a pre-vitrification parameter associated with an increased embryo survival after warming and implantation rate. However, the available medical evidence shows controversial results with only a limited number of prospective studies assessing the subject. Study design, size, duration This prospective cohort study evaluated 394 women who underwent a frozen blastocyst transfer at Instituto Bernabeu between July and December 2020. All patients were prepared with substitutive cycle and received single blastocyst embryo transfers. Participants/materials, setting, methods Before embryo vitrification on day 5 of development, some expanded and/or early hatching blastocysts (A/B ASEBIR categories) were artificial laser-assisted collapsed. (n = 83, study group). 311 embryos of the same quality and day of development were not collapsed (control group). We compared the embryo survival rate, clinical, implantation and miscarriage rates between groups. The statistical analysis was performed using SPSS (version 20.0). Main results and the role of chance The two groups were comparable in terms of maternal age (39.79 ± 3.83, control group; 40.21 ± 4.45, study group; p = 0.341). Embryo survival rate resulted in 100% in both groups. Regarding clinical outcomes, collapsed blastocysts significantly increased the positive pregnancy test and the clinical pregnancy and implantation rate compared to the control group, respectively (positive test: 69,9% vs 43,4%, p = 0.000018, odds ratio (OR)= 3.02 [95% CI 1.80–5.08]; clinical pregnancy and implantation: 56,6% vs 35,4%, p = 0.000041, OR = 2.39 [95% CI 1.46–3.90] ). The miscarriage rate was not affected by the blastocyst collapse effect (23,6% in the control group vs 27,6% in the study group, p = 0.593, OR = 1.23 [95% CI 0.57–2.68]). Limitations, reasons for caution This is a non-randomized controlled study. Additional RCTs are warranted to corroborate our findings. Wider implications of the findings: Considering the large number of blastocyst vitrification cycles that are carried out worldwide, artificial laser-assisted collapse before vitrification has the potential to increase the clinical results in benefit of many patients. Trial registration number Not applicable
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 632776-X
    detail.hit.zdb_id: 1484864-8
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  • 9
    In: Human Reproduction, Oxford University Press (OUP), Vol. 36, No. Supplement_1 ( 2021-08-06)
    Abstract: Is it possible to predict the likelihood of an IVF embryo being aneuploid and/or mosaic using a machine learning algorithm? Summary answer There are paternal, maternal, embryonic and IVF-cycle factors that are associated with embryonic chromosomal status that can be used as predictors in machine learning models. What is known already The factors associated with embryonic aneuploidy have been extensively studied. Mostly maternal age and to a lesser extent male factor and ovarian stimulation have been related to the occurrence of chromosomal alterations in the embryo. On the other hand, the main factors that may increase the incidence of embryo mosaicism have not yet been established. The models obtained using classical statistical methods to predict embryonic aneuploidy and mosaicism are not of high reliability. As an alternative to traditional methods, different machine and deep learning algorithms are being used to generate predictive models in different areas of medicine, including human reproduction. Study design, size, duration The study design is observational and retrospective. A total of 4654 embryos from 1558 PGT-A cycles were included (January-2017 to December-2020). The trophoectoderm biopsies on D5, D6 or D7 blastocysts were analysed by NGS. Embryos with ≤25% aneuploid cells were considered euploid, between 25-50% were classified as mosaic and aneuploid with & gt;50%. The variables of the PGT-A were recorded in a database from which predictive models of embryonic aneuploidy and mosaicism were developed. Participants/materials, setting, methods The main indications for PGT-A were advanced maternal age, abnormal sperm FISH and recurrent miscarriage or implantation failure. Embryo analysis were performed using Veriseq-NGS (Illumina). The software used to carry out all the analysis was R (RStudio). The library used to implement the different algorithms was caret. In the machine learning models, 22 predictor variables were introduced, which can be classified into 4 categories: maternal, paternal, embryonic and those specific to the IVF cycle. Main results and the role of chance The different couple, embryo and stimulation cycle variables were recorded in a database (22 predictor variables). Two different predictive models were performed, one for aneuploidy and the other for mosaicism. The predictor variable was of multi-class type since it included the segmental and whole chromosome alteration categories. The dataframe were first preprocessed and the different classes to be predicted were balanced. A 80% of the data were used for training the model and 20% were reserved for further testing. The classification algorithms applied include multinomial regression, neural networks, support vector machines, neighborhood-based methods, classification trees, gradient boosting, ensemble methods, Bayesian and discriminant analysis-based methods. The algorithms were optimized by minimizing the Log_Loss that measures accuracy but penalizing misclassifications. The best predictive models were achieved with the XG-Boost and random forest algorithms. The AUC of the predictive model for aneuploidy was 80.8% (Log_Loss 1.028) and for mosaicism 84.1% (Log_Loss: 0.929). The best predictor variables of the models were maternal age, embryo quality, day of biopsy and whether or not the couple had a history of pregnancies with chromosomopathies. The male factor only played a relevant role in the mosaicism model but not in the aneuploidy model. Limitations, reasons for caution Although the predictive models obtained can be very useful to know the probabilities of achieving euploid embryos in an IVF cycle, increasing the sample size and including additional variables could improve the models and thus increase their predictive capacity. Wider implications of the findings Machine learning can be a very useful tool in reproductive medicine since it can allow the determination of factors associated with embryonic aneuploidies and mosaicism in order to establish a predictive model for both. To identify couples at risk of embryo aneuploidy/mosaicism could benefit them of the use of PGT-A. Trial registration number Not Applicable
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 632776-X
    detail.hit.zdb_id: 1484864-8
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  • 10
    In: Human Reproduction, Oxford University Press (OUP), Vol. 36, No. Supplement_1 ( 2021-08-06)
    Abstract: Is it possible to identify a genetic cause of familial premature ovarian failure (POF) with whole-exome sequencing (WES)? Summary answer Whole-exome sequencing is the most efficient strategy to identify probably pathogenic mutations in different genes in pathologies of polygenic etiology such as premature ovarian failure. What is known already Premature ovarian failure is the loss of ovarian function before the age of 40, and it is a common cause of infertility in women. This pathology has a heterogeneous etiology. Some chromosomal and genetic alterations have been described, and could explain approximately 20% of cases. However, in most patients the origin remains unknown. Recent studies with next-generation sequencing (NGS) have identified new variants in candidate genes related with premature ovarian insufficiency (POI) or premature ovarian failure (POF). These genes are not only involved in processes such as folliculogenesis, but also with DNA damage repair, homologous recombination, and meiosis. Study design, size, duration Fourteen women, from 7 families, affected by idiopathic POF were included in the study from October 2019 to September 2020. Seven POF patients were recruited when they came to our clinic to undergo assisted reproductive treatment. In the anamnesis, it was found that they had relatives with a diagnosis of POF, who were also recruited for the study. The inclusion criteria were amenorrhea before 38 years old and analytical and ultrasound signs of ovarian failure. Participants/materials, setting, methods WES was performed using TrusightOne (Illumina®). Sequenced data were aligned through BWA tool and GATK algorithm was used for SNVs/InDel identification. VCF files were annotated using Variant Interpreter software. Only the variants shared by each family were extracted for analysis and these criteria were followed: (1) Exonic/splicing variants in genes related with POF or involved in biological ovarian functions (2) Variants with minor allele frequency (MAF) ≤0.05 and (3) having potentially moderate/strong functional effects. Main results and the role of chance Seventy-nine variants possibly related with the POF phenotype were identified in the seven families. All these variants had a minor allele frequency (MAF) ≤0.05 in the gnomAD database and 1000 genomes project. Among these candidate variants, two were nonsense, six splice region, one frameshift, two inframe deletion and 68 missense. Thirty-two of the missense variants were predicted to have deleterious effects by minimum two of the four in silico algorithms used (SIFT, PolyPhen–2, MutationTaster and PROVEAN). All variants were heterozygous, and all the families carried three or more candidate variants. Altogether, 43 probably damaging genetic variants were identified in 39 genes expressed in the ovary and related with POF/POI or linked to ovarian physiology. We have described genes that have never been associated to POF pathology, however they may be involved in key biological processes for ovarian function. Moreover, some of these genes were found in two families, for example DDX11, VWF, PIWIL3 and HSD3B1. DDX11 may function at the interface of replication-coupled DNA repair and sister chromatid cohesion. VWF gene is suggested to be associated with follicular atresia in previous studies. PIWIL3 functions in development and maintenance of germline stem cells, and HSD3B1 is implicated in ovarian steroidogenesis. Limitations, reasons for caution Whole-exome sequencing has some limitations: does not cover noncoding regions of the genome, it also cannot detect large rearrangements, copy-number variants (large deletions/duplications), mosaic mutations, mutations in repetitive or high GC rich regions and mutations in genes with corresponding pseudogenes or other highly homologous sequences. Wider implications of the findings: WES has previously shown to be an efficient tool to identify genes as cause of POF, and has demonstrated the polygenic etiology. Although some studies have focused on it, and many genes are identified, this study proposes new candidate genes and variants, having potentially moderate/strong functional effects, associated with POF. Trial registration number Not applicable
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
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    detail.hit.zdb_id: 1484864-8
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