{ "items": [ "\n\n
AbstractAimTo test the feasibility and acceptability of a reduced\u2010carbohydrate dietary program, intended to reduce the risk of gestational diabetes.Materials and MethodsFifty\u2010one pregnant women at <20\u2009weeks' gestation, with body mass index \u226530\u2009kg/m2, and a normal baseline oral glucose tolerance test (OGTT), were randomized 2:1 to an intervention or control group and followed\u2010up until delivery. The dietary intervention aimed at providing 130\u2013150\u2009g carbohydrate/day. Feasibility outcomes assessed at 24\u201328\u2009weeks' gestation, included adoption of the reduced\u2010carbohydrate diet by the intervention group, and retention of all participants, assessed by completion of a second OGTT. Changes in glycemia, weight gain and dietary intake, and the maternal and neonatal outcomes were also assessed. Participants were interviewed about their experience of the intervention and the study.ResultsForty\u2010nine of 51 participants attended the follow\u2010up OGTT, a retention rate of 96% (95% confidence interval [CI] 86.8%\u201398.9%). In the intervention group, carbohydrate intake at follow\u2010up was 190.4 (95% CI 162.5\u2013215.6) g/day, a reduction of \u221224.6 (95% CI \u221251.5\u20132.4) g/day from baseline. Potentially favourable effects of the intervention on glucose control, weight gain and blood pressure were observed, but the study was not powered to detect significant differences in these. Participants found the intervention acceptable, and were content with the study processes, but some reported barriers to sustained adherence, mainly pertaining to competing priorities.ConclusionsRetention was high, suggesting the study processes are feasible, but the carbohydrate reduction in the intervention group was small, and did not meet progression criteria, limiting the likelihood of achieving the desired goal to prevent gestational diabetes. Trial registration number: ISRCTN16235884.
\n \n\n \n \nPre-eclampsia is associated with postnatal cardiac dysfunction; however, the nature of this relationship remains uncertain. This multicentre retrospective cohort study aimed to determine the prevalence of pre-eclampsia in women with pre-existing cardiac dysfunction (left ventricular ejection fraction\u2009
\n \n\n \n \nRegular physical activity improves glycaemic control in pregnant women with gestational diabetes. Motivational interviewing is an effective technique for increasing activity levels. This report evaluates a clinical pathway developed to integrate physical activity motivational interviewing into routine gestational diabetes care. Women attending a single-centre NHS clinic were invited to engage in a physical activity-focused motivational interview. The aerobic physical activity levels of 62 women were evaluated at baseline and at a 2-week telephone follow up, coded into three categories by minutes of moderate intensity physical activity per week: red (<30 minutes), amber (30\u2013149 minutes) and green (\u2265150 minutes). At baseline, 30.6% of participants were coded red, 41.9% amber and 27.4% green. At follow up, 4.8% women coded red, 38.7% amber and 56.5% green, demonstrating a significant association for increased activity levels after motivational interviewing (P<0.001). This clinical pathway provides encouraging results that physical activity increased significantly in the short term.
\n \n\n \n \nSynthetic data offer a number of advantages over using ground truth data when working with private and personal information about individuals. Firstly, the risk of identifying individuals is reduced considerably, which enables the sharing of data for analysis amongst more organisations. Secondly, the fine tuning of synthetic datapoints to suit particular modelling and analyses could help to build more suitable models that can avoid biases found in the original ground truth data. In this paper we explore how a probabilistic synthetic data generator can be used to model data with high enough fidelity that it can be used to develop and validate state-of-the-art machine learning models. In particular, we use a Bayesian network model trained on gestational diabetes data, generated from a mobile health app collected from a number of health trusts in the UK. These data are used to train and test an established machine learning model developed by Sensyne Health using real-world data, and the resulting performance is compared to performance on ground truth data. In addition, a clinical validation is undertaken to explore if human experts can differentiate real patients from synthetic ones. We demonstrate that the Bayesian network synthetic data generator is able to mimic the ground truth closely enough to make it difficult for a human expert to distinguish between the two. We show that the data generator captures the interactions between features and the multivariate distributions close enough to enable classifiers to be inferred that imitate the key performance characteristics of models inferred from ground truth data. What is more, we demonstrate that the discovered mis-classifications found when testing using the synthetic data, are as informative as when testing using ground truth data.
\n \n\n \n \nIn recent years there has been an explosion of interest in the use of AI technology within research and clinical practice across both medical and surgical disciplines. Obstetrics and gynaecology are no exception: AI has been applied to some of the most topical challenges in women's health, from improving the diagnosis of gynaecological cancers to modelling the risk of stillbirth. This chapter will explore the breadth of existing AI applications and the scope for future development within the field of women's health.
\n \n\n \n \nThyroid dysfunction affects approximately 3% of pregnant women. Adequate thyroid hormone levels are important for fetal development. Normal physiological changes of pregnancy can contribute to subclinical hypothyroidism which may require treatment with thyroxine during pregnancy. Pre-existing hypothyroidism requires an increase in thyroxine dosage. Pre-existing hyperthyroidism may or may not require continued treatment with anti-thyroid medication, though these medications can rarely cause adverse fetal effects. Gestational hyperthyroidism must be distinguished from a new diagnosis of Graves\u2019 disease in pregnancy. Gestational hyperthyroidism does not require treatment with anti-thyroid medication. Graves\u2019 disease requires additional monitoring of mother and fetus and consideration of anti-thyroid medication. Post-partum thyroiditis is an underdiagnosed condition which can cause transient hyperthyroidism before recovery or hypothyroidism, or hypothyroidism without a hyperthyroid phase. Serial monitoring of thyroid function test is required. The vast majority of women with thyroid conditions can be managed to a successful pregnancy outcome.
\n \n\n \n \nAbstractBackgroundPhysical activity (PA) interventions are an important but underutilised component in the management of gestational diabetes mellitus (GDM). The challenge remains how to deliver cost effective PA interventions that have impact on individual behaviour. Digital technologies can support and promote PA remotely at scale. We describe the development of a behaviourally informed smartphone application (Stay-Active) for women attending an NHS GDM clinic. Stay-Active will support an existing motivational interviewing intervention to increase and maintain PA in this population.MethodsThe behaviour change wheel (BCW) eight step theoretical approach was used to design the application. It provided a systematic approach to understanding the target behaviour, identifying relevant intervention functions, and specifying intervention content. The target behaviour was to increase and maintain PA. To obtain a behavioural diagnosis, qualitative evidence was combined with focus groups on the barriers and facilitators to PA in women with GDM. The findings were mapped onto the Capability Opportunity Motivation-Behaviour (COM-B) model and Theoretical Domains Framework to identify what needs to change for the target behaviour and linked to appropriate intervention functions. Finally, behaviour changes techniques (BCT) and modes of delivery that are most likely to serve the intervention functions were selected. Current evidence, patient focus groups and input from key stakeholders informed Stay-Active\u2019s development.ResultsWe found that psychological capability, reflective and automatic motivation, social and physical opportunity needed to change to increase PA in women with GDM. The four key intervention functions identified were Enablement, Education, Persuasion and Training. Stay-Active incorporates these four intervention functions delivering ten BCTs including: goal setting, credible source, self-monitoring, action planning, prompts and cues. The final design of Stay-Active delivers these BCTs via an educational resource centre, with goal setting and action planning features, personalised performance feedback and individualised promotional messages.ConclusionThe BCW has enabled the systematic and comprehensive development of Stay-Active to promote PA in women with GDM within an NHS Maternity service. The next phase is to conduct a trial to assess the feasibility and acceptability of a multi-component intervention that combines Stay-Active with PA Motivational Interviewing.
\n \n\n \n \nThe identification of tumor-specific molecular dependencies is essential for the development of effective cancer therapies. Genetic and chemical perturbations are powerful tools for discovering these dependencies. Even though chemical perturbations can be applied to primary cancer samples at large scale, the interpretation of experiment outcomes is often complicated by the fact that one chemical compound can affect multiple proteins. To overcome this challenge, Batzilla et al. (PLoS Comput Biol 18(8): e1010438, 2022) proposed DepInfeR, a regularized multi-response regression model designed to identify and estimate specific molecular dependencies of individual cancers from their ex-vivo drug sensitivity profiles. Inspired by their work, we propose a Bayesian extension to DepInfeR. Our proposed approach offers several advantages over DepInfeR, including e.g. the ability to handle missing values in both protein-drug affinity and drug sensitivity profiles without the need for data pre-processing steps such as imputation. Moreover, our approach uses Gaussian Processes to capture more complex molecular dependency structures, and provides probabilistic statements about whether a protein in the protein-drug affinity profiles is informative to the drug sensitivity profiles. Simulation studies demonstrate that our proposed approach achieves better prediction accuracy, and is able to discover unreported dependency structures.
\n \n\n \n \nLarge loop excision of the transformation zone is an extremely common procedure routinely carried out in a gynaecology or colposcopy outpatient setting under local anaesthetic. Here, we present a rare case resulting in emergency hysterectomy. A healthy para 3, who had been diagnosed with microscopic cancer of the cervix, attended colposcopy for repeat excision. The colposcopy revealed a normal cervix, and diathermy loop excision was performed. During the procedure, heavy bleeding from the anterior cutting edge was noted. Despite the best attempts to manage the haemorrhage conservatively in outpatients, the bleeding persisted, and the patient was transferred to theatres. Examination under anaesthesia revealed an injury to the descending branch of the uterine artery, and emergency hysterectomy was performed. Immediate recognition of an extremely rare complication, fast decision-making and a cross-disciplinary approach led to a satisfactory outcome.
\n \n\n \n \nBackground: INTERBIO-21 st is Phase II of the INTERGROWTH-21 st Project, the population-based, research initiative involving nearly 70,000 mothers and babies worldwide coordinated by Oxford University and performed by a multidisciplinary network of more than 400 healthcare professionals and scientists from 35 institutions in 21 countries worldwide. Phase I, conducted 2008-2015, consisted of nine complementary studies designed to describe optimal human growth and neurodevelopment, based conceptually on the WHO prescriptive approach. The studies generated a set of international standards for monitoring growth and neurodevelopment, which complement the existing WHO Child Growth Standards. Phase II aims to improve the functional classification of the highly heterogenous preterm birth and fetal growth restriction syndromes through a better understanding of how environmental exposures, clinical conditions and nutrition influence patterns of human growth from conception to childhood, as well as specific neurodevelopmental domains and associated behaviors at 2 years of age. Methods: In the INTERBIO-21 st Newborn Case-Control Study, a major component of Phase II, our objective is to investigate the mechanisms potentially responsible for preterm birth and small for gestational age and their interactions, using deep phenotyping of clinical, growth and epidemiological data and associated nutritional, biochemical, omic and histological profiles. Here we describe the study sites, population characteristics, study design, methodology and standardization procedures for the collection of longitudinal clinical data and biological samples (maternal blood, umbilical cord blood, placental tissue, maternal feces and infant buccal swabs) for the study that was conducted between 2012 and 2018 in Brazil, Kenya, Pakistan, South Africa, Thailand and the UK. Discussion: Our study provides a unique resource for the planned analyses given the range of potentially disadvantageous exposures (including poor nutrition, pregnancy complications and infections) in geographically diverse populations worldwide. The study should enhance current medical knowledge and provide new insights into environmental influences on human growth and neurodevelopment.
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