Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care.

To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, 'holistically' developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.

PML/RARalpha targets promoter regions containing PU.1 consensus and RARE half sites in acute promyelocytic leukemia.

PML/RARalpha is of crucial importance in acute promyelocytic leukemia (APL) both pathologically and therapeutically. Using a genome-wide approach, we identified in vivo PML/RARalpha binding sites in a PML/RARalpha-inducible cell model. Of the 2979 targeted regions, >62% contained canonical PU.1 motifs and >84% of these PU.1 motifs coexisted with one or more RARE half (RAREh) sites in nearby regions. Promoters with such PU.1-RAREh binding sites were transactivated by PU.1. PU.1-mediated transactivation was repressed by PML/RARalpha and restored by the addition of all-trans retinoic acid (ATRA). Genes containing such promoters were significantly represented by genes transcriptionally suppressed in APL and/or reactivated upon treatment with ATRA. Thus, selective targeting of PU.1-regulated genes by PML/RARalpha is a critical mechanism for the pathogenesis of APL.