Notes

A manuscript B*07 version allele, B*07:416, recognized by next-generation sequencing.
Embryo evaluation and selection embody the aggregate manifestation of the entire in vitro fertilization (IVF) process. It aims to choose the "best" embryos from the larger cohort of fertilized oocytes, the majority of which will be determined to be not viable either as a result of abnormal development or due to chromosomal imbalances. Indeed, it is generally acknowledged that even after embryo selection based on morphology, time-lapse microscopic photography, or embryo biopsy with preimplantation genetic testing, implantation rates in the human are difficult to predict. Our pursuit of enhancing embryo evaluation and selection, as well as increasing live birth rates, will require the adoption of novel technologies. Recently, several artificial intelligence (AI)-based methods have emerged as objective, standardized, and efficient tools for evaluating human embryos. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze "big" data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. In this chapter, we present an overview of existing AI technologies in reproductive medicine and envision their potential future applications in the field.Traditionally, new treatments have been developed for the population at large. Recently, large-scale genomic sequencing analyses have revealed tremendous genetic diversity between individuals. In diseases driven by genetic events such as cancer, genomic sequencing can unravel all the mutations that drive individual tumors. The ability to capture the genetic makeup of individual patients has led to the concept of precision medicine, a modern, technology-driven form of personalized medicine. Precision medicine matches each individual to the best treatment in a way that is tailored to his or her genetic uniqueness. To further personalize medicine, precision medicine increasingly incorporates and integrates data beyond genomics, such as epigenomics and metabolomics, as well as imaging. Increasingly, the robust use and integration of these modalities in precision medicine require the use of artificial intelligence and machine learning. This modern view of precision medicine, adopted early in certain areas of medicine such as cancer, has started to impact the field of reproductive medicine. Here we review the concepts and history of precision medicine and artificial intelligence, highlight their growing impact on reproductive medicine, and outline some of the challenges and limitations that these new fields have encountered in medicine.As the world becomes increasingly reliant on computers, it is not surprising that medicine has embraced the computer age with enthusiasm. This is also true in the field of reproductive medicine, where we are witnessing exciting applications of digital technologies and artificial intelligence (AI). It is anticipated that AI-guided approaches will become more objective, more accurate, and more rapid, resulting in greater precision, standardization, and automatization in our field. This month's Views and Reviews contains five thought-provoking contributions addressing the contemporary and futuristic applications of AI in reproductive medicine and the assisted reproductive technologies.This article has been retracted please see Elsevier Policy on Article Withdrawal (https//www.elsevier.com/about/our-business/policies/article-withdrawal). ABT199 This article has been retracted at the request of the Authors. The entire group of investigators contributing to this study requests to withdraw this article. We conducted the study in good faith and according to correct methodology. We believe that our findings have been partly misinterpreted, but at the same time realize that the article may have caused distress to some people. Women's respect is a priority for us and we are extremely sorry for the discontent the publication originated.Autoinflammatory diseases are conditions in which pathogenic inflammation arises primarily through antigen-independent hyperactivation of immune pathways. First recognized just over 2 decades ago, the autoinflammatory disease spectrum has expanded rapidly to include more than 40 distinct monogenic conditions. Related mechanisms contribute to common conditions such as gout and cardiovascular disease. Here, we review the basic concepts underlying the "autoinflammatory revolution" in the understanding of immune-mediated disease and introduce major categories of monogenic autoinflammatory disorders recognized to date, including inflammasomopathies and other IL-1-related conditions, interferonopathies, and disorders of nuclear factor kappa B and/or aberrant TNF activity. We highlight phenotypic presentation as a reflection of pathogenesis and outline a practical approach to the evaluation of patients with suspected autoinflammation.Disability advisers work in companies and training centres. They are real resource people for learners with a disability. They are there to answer all their questions, favour their integration and ensure their training is a success.The integration of people with a disability has been a statutory obligation in hospitals since 2005. However, the reality of these integrations, in the experience of the healthcare facilities and staff concerned, is not simply a case of complying with legislation. It is the story of work groups, the provision of tools and resources, managerial support and above all, it is the story of human encounters and mutual enrichment.The integration of a person with a disability is challenging on a legal, financial and managerial level. Many doubts and fears persist, preventing disabled employees from becoming fully integrated. It is essential to work on deconstructing prejudices and improving managers' training.Accessibility to training is necessary to improve the inclusion of people with a disability. Beyond the current or future legal obligations, the RHF (training disability resource) scheme helps training providers improve their understanding of these students' specific needs and to support them in their learning.
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