European Medical Journal. Reproductive health最新文献

Ultrasound is one of the most ubiquitous imaging modalities in clinical practice. It is cheap, does not require ionizing radiation and can be performed at the bedside, making it the most commonly utilized imaging technique in pregnancy. Despite these advantages, it does have some drawbacks such as relatively low imaging quality, low contrast, and high variability. With these constraints, automating the interpretation of ultrasound images is challenging. However, successful automated identification of structures within 3D ultrasound volumes has the potential to revolutionize clinical practice. For example, a small placental volume in the first trimester has been shown to be correlated to adverse outcome later in pregnancy. If the placenta could be segmented reliably and automatically from a static 3D ultrasound volume, it would facilitate the use of its estimated volume, and other morphological metrics, as part of a screening test for increased risk of pregnancy complications potentially improving clinical outcomes. Recently, deep learning has emerged, achieving state-of-the-art performance in various research fields, notably medical image analysis involving classification, segmentation, object detection, and tracking tasks. Due to its increased performance with large datasets, it has gained great interest in medical imaging applications. In this review, we present an overview of deep learning methods applied to ultrasound in pregnancy, introducing their architectures and analyzing their strategies. We then present some common problems and provide some perspectives into potential future research.

The immature oocytes within primordial follicles are arrested at Prophase I of meiosis and remain dormant until awakened by an increase in intracellular levels of phosphatidylinositol (3,4,5)-trisphosphate (PIP3). Oocyte PIP3 level is determined by the balance between the activity of phosphoinositide 3-kinase (PI3K) and phosphatase and tensin homologue (PTEN). When this balance favours PI3K, PIP3 levels elevate and trigger the cascade of PI3K/protein kinase B (AKT)/the mammalian target of rapamycin (mTOR) pathway, leading to activation of primordial follicles. This short review aims to provide new insights into the physiological functions of PI3K and PTEN in immature oocytes by summarising recent findings from murine model studies, including oocyte-specific transgenic mice with constitutively-active mutant PI3K.

The multitude of research clarifying critical factors in embryonic organ development has been instrumental in human stem cell research. Mammalian organogenesis serves as the archetype for directed differentiation protocols, subdividing the process into a series of distinct intermediate stages that can be chemically induced and monitored for the expression of stage-specific markers. Significant advances over the past few years include established directed differentiation protocols of human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) into human kidney organoids in vitro. Human kidney tissue in vitro simulate the in vivo response when subject to nephrotoxins, providing a novel screening platform during drug discovery to facilitate identification of lead candidates, reduce developmental expenditures, and reduce future rates of drug-induced acute kidney injury. Patient-derived hiPSCs, which bear naturally occurring DNA mutations, may allow for modeling of human genetic diseases to determine pathologic mechanisms and screen for novel therapeutics. In addition, recent advances in genome editing with CRISPR/Cas9 enable to generate specific mutations to study genetic disease with non-mutated lines serving as an ideal isogenic control. The growing population of patients with end-stage kidney disease (ESKD) is a world-wide healthcare problem with higher morbidity and mortality that warrants the discovery of novel forms of renal replacement therapy. Coupling the outlined advances in hiPSC research with innovative bioengineering techniques, such as decellularized kidney and 3D printed scaffolds, may contribute to the development of bioengineered transplantable human kidney tissue as a means of renal replacement therapy.