Isolating the direct effects of adverse developmental conditions on in vivo cardiovascular function at adulthood: the avian model.

It is now well accepted that exposure to adverse environmental conditions in utero can predispose a fetus to disease later in life. Using an avian model to study the programming of disease has a unique advantage as it allows isolation of the direct effects of adverse conditions on fetal physiology, without any confounding effects via the mother or placenta. However, experiments in avian models are limited by the lack of well-established surgical protocols for the adult bird, which we have established in this study. Surgery was performed on seven young adult Bovan Brown chickens (body weight 1617±214 g, mean±s.d.) in order to instrument them with femoral arterial and venous catheters and a femoral arterial flow probe. Isoflurane and lidocaine were both found to have depressive effects on chicken cardiovascular function. Optimised methods of anaesthesia, intraoperative monitoring, surgical approach, postoperative care, and experimentation are described. Chickens recovered rapidly from surgery without significant blood gas perturbation, and basal in vivo cardiovascular studies were performed following 5 days of recovery. These techniques allow detailed investigation of avian cardiometabolic function, permitting determination of the consequences in later life of direct environmental insults to fetal physiology, isolated from additional effects on maternal physiology and/or placental endocrinology

On The Use Of Digital Twin Technology Arielle For The Development Of New Generation Perinatal Life Support Systems

Perinatal Life Support (PLS) consortium is developing an artificial womb (PLS system) to increase chance of survival of extremely preterm infants (<28 weeks of gestational age). To develop such a complex medical device, knowledge from multidisciplinary fields must integrate into one single system. Mathematical models are used to support this integration by composing a digital twin of the system, named Arielle, to allow computer simulations of the device. Arielle is connected with a manikin to support clinical implementation. For this purpose, a new model named Arielle is proposed as digital twin of the PLS system. This study presents possible applications of Arielle.First the purposes of Arielle are defined and a concept of Arielle and manikin is developed to test and analyze these purposes:1. Arielle should be able to help gaining the necessary knowledge to create an optimal environment for fetal development.2. Arielle should simulate the properties and interactions of components of the PLS system to optimize their desired effect on thestatus of the fetus.3. Arielle should be able to monitor and interpret vital signals of the fetus once integrated in the PLS system.4. Arielle should allow patient specific decision support to inform the user to optimize settings of the PLS system and support interventions.To support the development of the PLS system, Arielle should be able to simulate the fetal physiology and the PLS system to gain knowledge of the fetal physiology and optimize components. When the PLS systemis implemented, Arielle should be able to interpret vital signals and as a patient specific decision support. Future work focuses on the development and evaluation of Arielle with respect to the defined goals

Oral Health During Pregnancy and Early Childhood

Outlines the benefits of perinatal oral health care, barriers, and limits; examines access by age, income, race/ethnicity, and coverage; and recommends expanding interdisciplinary collaboration, advocacy and education, and provider Medicaid participation

Towards Deep Cellular Phenotyping in Placental Histology

The placenta is a complex organ, playing multiple roles during fetal development. Very little is known about the association between placental morphological abnormalities and fetal physiology. In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell. By utilising two deep Convolutional Neural Network architectures and transfer learning, we can robustly localise and classify placental cells within five classes with an accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic knowledge that is capable of both stratifying five distinct cell populations and learn intraclass phenotypic variance. We envisage that the automation of this pipeline to population scale studies of placenta histology has the potential to improve our understanding of basic cellular placental biology and its variations, particularly its role in predicting adverse birth outcomes.Comment: Updated MRC funding material. Corrected typo that suggested ensembling and Inception accuracy were the same (updated to reflect the fact the ensemble model is 1% better than previously reported

Mathematical tools for identifying the fetal response to physical exercise during pregnancy

In the applied mathematics literature there exist a significant number of tools that can reveal the interaction between mother and fetus during rest and also during and after exercise. These tools are based on techniques from a number of areas such as signal processing, time series analysis, neural networks, heart rate variability as well as dynamical systems and chaos. We will briefly review here some of these methods, concentrating on a method of extracting the fetal heart rate from the mixed maternal-fetal heart rate signal, that is based on phase space reconstructio

A SYSTEMIC REVIEW ON THE FUTURE OF OBSTETRICS AND GYNECOLOGY: HARNESSING ARTIFICIAL INTELLIGENCE.

There is a burgeoning interest in the utilization of artificial intelligence (AI) within the realm of medical research, which exhibits considerable potential for forthcoming advancements. Obstetrics and gynecology encompass specialized disciplines that are associated with a heightened susceptibility to legal matters and suboptimal clinical outcomes. Multiple challenges exist in these domains, encompassing the comprehension of fetal physiology and the precise prognostication of prenatal and labor monitoring. The field of gynecology encounters intricacies within the realm of molecular biology, particularly in the comprehension of gynecological malignancies. This review aims to explore the potential applications of AI within the field of obstetrics and gynecology. The present study aims to investigate the potential utility of AI in enhancing comprehension of fundamental principles within various domains, with a particular focus on its potential impact on the healthcare sector. In the realm of obstetrics and gynecology, AI exhibits considerable potential in tackling enduring obstacles and aiding healthcare providers in their decision-making processes

Variations on fetal heart rate variability.

This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1113/JP27071