es

Authors

  • es es Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica. Córdoba, Argentina; Embryoxite.
  • es es Embryoxite
  • es es Embryoxite
  • es es Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Consejo Nacional de Investigaciones Científicas y Técnicas. Fundación Para el Progreso de la Medicina.
  • es es Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación y Desarrollo en Ingeniería de Procesos y Química Aplicada (IPQA).

Keywords:

Reproducción, Inteligencia artificial, Técnicas de reproducción

Abstract

Millones de personas alrededor del mundo padecen infertilidad. Actualmente, los tratamientos de reproducción asistida cuentan con métodos de selección embrionaria que están lejos de ser perfectos. Las metodologías "ómicas" y la inteligencia artificial, podrían mejorar la precisión y aumentar la tasa de éxito, optimizando los tratamientos de infertilidad.

References

World Health Organization. [Online]. Available from: https://www.who.int/es/health-topics/infertility#tab=tab_1.

Reproducción Asistida ORG. [Online]. Available from: https://www.reproduccionasistida.org/reproduccion-asistida/#que-es-la-reproduccion-asistida.

Reproducción Asistida ORG. [Online]. Available from: https://www.reproduccionasistida.org/fecundacion-in-vitro-fiv/.

Molt Petersen B, Boel , Montag , Gardner K. Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3. Human Reproduction. 2016 September; 31(10).

Desai NN, Goldstein , Rowland Y, Goldfarb JM. Morphological evaluation of human embryos and derivation of an embryo quality scoring system specific for day 3 embryos: a preliminary study. Human Reproduction. 2000 October; 15(10).

Holte J, Berglund L, Milton K, Garello C, Gennarelli G, Revelli A, et al. Construction of an evidence-based integrated morphology cleavage embryo score for implantation potential of embryos scored and transferred on day 2 after oocyte retrieval. Human Reproduction. 2007 February; 22(2).

Fisch JD, Rodriguez H, Ross R, Overby G, Sher. The Graduated Embryo Score (GES) predicts blastocyst formation and pregnancy rate from cleavage-stage embryos. Human Reproduction. 2001 September; 16(9).

Anduaga Marchetti , Pené , Martínez G. Embriología Clínica Casiva E, editor. Córdoba : Universidad Nacional de Córdoba ; 2022.

D. BB. Culture of preimplantation embryos: facts and artifacts. Human Reproduction Update. 1995 January; 1(2).

Rehman KS, Bukulmez , Langley , Carr BR, Nackley AC, Doody KM, et al. Late stages of embryo progression are a much better predictor of clinical pregnancy than early cleavage in intracytoplasmic sperm injection and in vitro fertilization cycles with blastocyst-stage transfer. Fertility and Sterility. 2007 May; 87(5).

Gardner DK, Balaban. Assessment of human embryo development using morphological criteria in an era of time-lapse, algorithms and ‘OMICS’: is looking good still important? Molecular Human Reproduction. 2016 October; 22(10).

Minasi MG, Colasante , Riccio T, Ruberti , Casciani , Scarselli , et al. Correlation between aneuploidy, standard morphology evaluation and morphokinetic development in 1730 biopsied blastocysts: a consecutive case series study. Human Reproduction. 2016 October; 31(10).

Lou , Li , Guan , Zhang , Hao , Cui. Association between morphologic grading and implantation rate of Euploid blastocyst. Journal of Ovarian Research. 2021 January; 14(18).

Chen , Xu , Fu , Wang , Liu Y, Ding , et al. Clinical application of next generation sequencing-based haplotype linkage analysis in the preimplantation genetic testing for germline mosaicisms. Orphanet Journal of Rare Diseases. 2023 January; 18(137).

Hornak , Bezdekova , Kubicek D, Navratil , Hola , Balcova , et al. OneGene PGT: comprehensive preimplantation genetic testing method utilizing next-generation sequencing. Genetics. 2023 December; 41.

Chen HF, Chen , Ho HN. An overview of the current and emerging platforms for preimplantation genetic testing for aneuploidies (PGT-A) in in vitro fertilization programs. Taiwanese Journal of Obstetrics and Gynecology. 2020 July; 59(4).

Gualtieri MR, Ory SJ, Bazzi , Alburkat Z, Bazzi. The promise and challenges of preimplantation genetic testing for IVF. Contemporary OB/GYN Journal. 2023 February; 68

Varghese AC, Goldberg , Bhattacharyya AK, Agarwal. Emerging technologies for the molecular study of infertility, and potential clinical applications. Reproductive BioMedicine Online. 2007 August; 15(4).

Manzoni , Kia DA, Vandrovcova , Hardy , Wood NW, Lewis PA, et al. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Briefings in Bioinformatics. 2018 March; 19(2).

Vandereyken , Sifrim , Thienpont , Voet T. Methods and applications for single-cell and spatial multi-omics. Nature Reviews Genetics. 2023 March; 24.

Treff NR, Su , Tao I, Levy B, Scott RT. Accurate single cell 24 chromosome aneuploidy screening using whole genome amplification and single nucleotide polymorphism microarrays. Fertility and Sterility. 2010 Novmber; 94(6).

Rivera Egea R, Garrido Puchalt , Meseguer Escrivá , Varghese AC. OMICS: Current and future perspectives in reproductive medicine and technology. Journal of Human Reproductive Sciences. 2014 June; 7(2).

García-Herrero , Garrido , Martínez-Conejero JA, Remohí , Pellicer , Meseguer. Ontological evaluation of transcriptional differences between sperm of infertile males and fertile donors using microarray analysis. Journal Assisted Reproductive Genetics (2010) 27:111–120. 2010 February; 27.

Hernández-Vargas , Muñoz , Domínguez. Identifying biomarkers for predicting successful embryo implantation: applying single to multi-OMICs to improve reproductive outcomes. Human Reproduction Update. 2020 April; 26(2).

Cortezzi SS, Garcia JS, Ferreira CR, Braga DPAF, Figueira RCS, Iaconelli A, et al. Secretome of the preimplantation human embryo by bottom-up label-free proteomics. Analytical and Bioanalytical Chemistry. 2011 July; 401.

Singh R, Sinclai KD. Metabolomics: Approaches to assessing oocyte and embryo quality. Theriogenology. 2007 September; 68(1).

Brison DR, Hollywood K, Arnesen R, Goodacre R. Predicting human embryo viability: the road to non-invasive analysis of the secretome using metabolic footprinting. Reproductive VioMedicine Online. 2007 July; 15(3).

Chung RH, Kang CY. A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification. Gigascience. 2019 May; 8(5).

Liu , Zhang , Martin , Ma , Shen. Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine. International Journal of Molecular Sciences. 2023; 24(4).

Zaninovic , elemento O, Rosenwaks. Artificial intelligence: its applications in reproductive medicine and the assisted reproductive technologies. Fertility and Sterility. 2019 July; 112(1).

Tran , Cooke S, WIllingworth PJ, Gardner DK. Deep learning as a predictive tool for fetal heart pregnancy

following time-lapse incubation and blastocyst transfer. Human Reproduction. 2019 June; 34(6).

Fly Kragh , Karstoft. Embryo selection with artificial intelligence: how to evaluate and compare methods?

Journal of Assisted Reproductive Genetics. 2021 July; 38(7).

Diakiw SM, Hall JMM, VerMilyea MD, Amin , Aizpurua , Giardini L, et al. Development of an artificial intelligence

model for predicting the likelihood of human embryo euploidy based on blastocyst images from

multiple imaging systems during IVF. Human Reproduction. 2022 August; 37(8).

Paternot , Devroe J, Debrock S, D'Hooghe TM, Spiessens. Intra- and inter-observer analysis in the morphological

assessment of early-stage embryos. Reproduction Biology Endocrinology. 2009 September;

7(105).

Lynn Curchoe C, Flores-Saiffe Farias A, Mendizabal-Ruiz , Chavez-Badiola A. Evaluating predictive models

in reproductive medicine. Fertility and Sterility. 2020 November; 114(5).

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Published

2024-12-16