Search results
Found 9437 matches for
The Institute of Biomedical Engineering and Nuffield Department of Obstetrics and Gynaecology receives Grand Challenges Explorations Grant for Groundbreaking Research in Global Health and Development.
Innovations and strategies for effective implementation of post pregnancy contraception services: Learnings from the FIGO PPIUD initiative.
The global unmet need for contraception continues to be unacceptably high at 218 million. The vast majority of these are women living in low and middle income countries, with a particularly high unmet need in the postpartum period. The FIGO PPIUD initiative demonstrated that it is feasible to embed counselling on contraception and insertion of PPIUD in maternity services. Implementation of these services was greatly enhanced by ensuring that counselling was culturally sensitive and appropriately given through specifically trained individuals, that task sharing was maximized in order to increase access, and that a high fundal placement was achieved resulting in low expulsion rates. Financing for contraception in LMICs is currently precarious and vulnerable to international politics. PPIUD is highly cost-effective. Expansion of contraception services including PPIUD has the potential to impact positively on climate change and a country's development profile if expanded on a large scale.
Genetic and reproductive strategies to prevent mitochondrial diseases.
Mitochondrial DNA (mtDNA) diseases pose unique challenges for genetic counselling and require tailored approaches to address recurrence risks and reproductive options. The intricate dynamics of mtDNA segregation and heteroplasmy shift significantly impact the chances of having affected children. In addition to natural pregnancy, oocyte donation, and adoption, IVF-based approaches can reduce the risk of disease transmission. Prenatal diagnosis (PND) and preimplantation genetic testing (PGT) remain the standard methods for women carrying pathogenic mtDNA mutations; nevertheless, they are not suitable for every patient. Germline nuclear transfer (NT) has emerged as a novel therapeutic strategy, while mitochondrial gene editing has increasingly become a promising research area in the field. However, challenges and safety concerns associated with all these techniques remain, highlighting the need for long-term follow-up studies, an improved understanding of disease mechanisms, and personalized approaches to diagnosis and treatment. Given the inherent risks of adverse maternal and child outcomes, careful consideration of the balance between potential benefits and drawbacks is also warranted. This review will provide critical insights, identify knowledge gaps, and underscore the importance of advancing mitochondrial disease research in reproductive health.
Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study.
INTRODUCTION: Congenital heart defect (CHD) is a significant, rapidly emerging global problem in child health and a leading cause of neonatal and childhood death. Prenatal detection of CHDs with the help of ultrasound allows better perinatal management of such pregnancies, leading to reduced neonatal mortality, morbidity and developmental complications. However, there is a wide variation in reported fetal heart problem detection rates from 34% to 85%, with some low- and middle-income countries detecting as low as 9.3% of cases before birth. Research has shown that deep learning-based or more general artificial intelligence (AI) models can support the detection of fetal CHDs more rapidly than humans performing ultrasound scan. Progress in this AI-based research depends on the availability of large, well-curated and diverse data of ultrasound images and videos of normal and abnormal fetal hearts. Currently, CHD detection based on AI models is not accurate enough for practical clinical use, in part due to the lack of ultrasound data available for machine learning as CHDs are rare and heterogeneous, the retrospective nature of published studies, the lack of multicentre and multidisciplinary collaboration, and utilisation of mostly standard planes still images of the fetal heart for AI models. Our aim is to develop AI models that could support clinicians in detecting fetal CHDs in real time, particularly in nonspecialist or low-resource settings where fetal echocardiography expertise is not readily available. METHODS AND ANALYSIS: We have designed the Clinical Artificial Intelligence Fetal Echocardiography (CAIFE) study as an international multicentre multidisciplinary collaboration led by a clinical and an engineering team at the University of Oxford. This study involves five multicountry hospital sites for data collection (Oxford, UK (n=1), London, UK (n=3) and Southport, Australia (n=1)). We plan to curate 14 000 retrospective ultrasound scans of fetuses with normal hearts (n=13 000) and fetuses with CHDs (n=1000), as well as 2400 prospective ultrasound cardiac scans, including the proposed research-specific CAIFE 10 s video sweeps, from fetuses with normal hearts (n=2000) and fetuses diagnosed with major CHDs (n=400). This gives a total of 16 400 retrospective and prospective ultrasound scans from the participating hospital sites. We will build, train and validate computational models capable of differentiating between normal fetal hearts and those diagnosed with CHDs and recognise specific types of CHDs. Data will be analysed using statistical metrics, namely, sensitivity, specificity and accuracy, which include calculating positive and negative predictive values for each outcome, compared with manual assessment. ETHICS AND DISSEMINATION: We will disseminate the findings through regional, national and international conferences and through peer-reviewed journals. The study was approved by the Health Research Authority, Care Research Wales and the Research Ethics Committee (Ref: 23/EM/0023; IRAS Project ID: 317510) on 8 March 2023. All collaborating hospitals have obtained the local trust research and development approvals.
Effect of supine or semi-recumbent positions on arterial stiffness among pregnant women
Objective: We aimed to evaluate whether there was a difference in arterial stiffness measurements in supine and semirecumbent positions among pregnant women. Methods: This was a cross-sectional study in four public hospitals in Uganda. Women were interviewed to capture demographic data and obstetric history before undergoing arterial stiffness examinations using the Arteriograph device (TensioMed, Budapest, Hungary) in supine and semirecumbent positions. Three consecutive measurements were acquired per position and were transformed to gestational age adjusted z-scores for analysis using a two-sample t-test, paired t-test and Pearson correlation coefficients. Results: We included 194 pregnant women, with median age of 25 years (interquartile rage (IQR), 21–29), and body mass index of 23.9 kg/m2 (IQR, 21.1–27.6). None was smoking or had renal and cardiac diseases. The procedure failure rate was less than 3 %. There were no significant differences in measurements between supine and semirecumbent positions at 11–42 weeks. We found a strong positive correlation between hemodynamic indices performed in supine and semirecumbent positions: aortic pulse wave velocity z-scores (r = 0.84 (95 % CI, 0.77–0.89)) and aortic augmentation index z-scores (r = 0.95 (95 % CI, 0.92–0.97)) at 11–27 weeks’ gestation, and central systolic blood pressure z-scores (r = 0.81 (95 % CI, 0.72–0.87)) and mean arterial pressure z-scores (r = 0.84 (95 % CI, 0.77–0.89)) at 28–42 weeks’ gestation. Repeated measurements taken in same position were strongly correlated throughout gestation. Conclusion: Arterial stiffness parameters measured in supine and semirecumbent positions were comparable and had very minimal failure rates. As a supine position is often not tolerated well in late gestation, our study suggests that pregnant women could assume a semirecumbent position during Arteriograph test procedures between 11 and 42 weeks of gestation.
WERF Endometriosis Phenome and Biobanking Harmonisation Project for Experimental Models in Endometriosis Research (EPHect-EM-Organoids): endometrial organoids as an emerging technology for endometriosis research
The aetiology of endometriosis remains poorly understood. In vitro model systems provide the opportunity to identify the mechanisms driving disease pathogenesis using human cells. Three-dimensional models, particularly organoid systems, have revolutionized how we study epithelial biology and are powerful tools for modelling endometriosis. As an emerging model system, it is important to define protocols and identify the remaining challenges surrounding endometrial organoid culture to increase reproducibility and scientific rigour in endometriosis research. The World Endometriosis Research Foundation (WERF) established an international working group comprised of experts using in vitro approaches for the study of endometriosis. This working group harmonized protocols and documentation of existing and emerging organoid systems to maximize comparison and replication across the field and guide specific research hypotheses testing. This evaluation of organoid protocols, limitations, challenges, and alternative approaches assessed both published and grey literature papers across several disciplines pertinent to endometriosis research. Recommendations for protocol and documentation harmonization are presented, and we created the first-ever decision tree diagram to guide and facilitate the selection of existing models best suited for specific areas of endometriosis research. Rigorous and systematic assessment of emerging organoid systems, recognizing the inferential strengths and limitations of these approaches, is vital for endometriosis research. This comprehensive review of the benefits, limitations, and utilization of organoid models, as well as the consequent integration of protocols and documentation, will contribute to the scientific knowledge base by maximizing the reproducibility, comparability, and interpretation of research studies in endometriosis. Additionally, these newly developed protocols and documentation should serve as a resource for, and facilitate collaboration between, endometriosis investigators using organoids in their research methods.
WERF Endometriosis Phenome and Biobanking Harmonisation Project for Experimental Models in Endometriosis Research (EPHect-EM-Pain): methods to assess pain behaviour in rodent models of endometriosis
Pain is a debilitating symptom of endometriosis, and its mechanisms are often explored using rodent models. However, a lack of harmonization amongst models and behavioural measures, in addition to inconsistent reporting, might limit the overall clinical relevance and hinder translation of findings. An additional challenge is accurately linking rodent behaviour to human experiences of endometriosis. This study aimed to: (i) review current measures of pain-associated behaviours used in endometriosis studies; (ii) recommend best practices for each method and their suitability to study endometriosis-associated pain; and (iii) develop internationally agreed-upon standard operating procedures ('EPHect-EM-Pain SOPs'). The World Endometriosis Research Foundation (WERF) assembled an international working group, from which a 'pain behaviour working group' consisting of experts in the field was established. The group used additional consultation from experimental pain model scientists in the broader field. Stimulus-evoked (reflexive) and stimulus-independent (spontaneous) measures are currently used to assess pain-associated behaviours in rodents with experimental endometriosis. All existing methods offer advantages and limitations regarding ethological relevance, output quality, and equipment/training requisites. Internationally standardized pain SOPs as well as summary documentation outlining the minimum and standard requirements for several behavioural measures were developed, as well as consensus recommendations on experimental designs and documentation. To more closely reflect the lived experiences of those with endometriosis, the consortium recommends that, following validation, multiple types of pain-related and/or parallel rodent behaviours (e.g. anxiety) should be quantified as surrogate outcome measures for endometriosis-associated pain. These harmonized methods and documentation for endometriosis research will facilitate essential comparisons among studies, improve translational applicability, and provide a superior holistic view of animal (and thus human) wellbeing.
WERF Endometriosis Phenome and Biobanking Harmonisation Project for Experimental Models in Endometriosis Research (EPHect-EM-Heterologous): heterologous rodent models
Endometriosis, defined as the growth of endometrial-like tissues outside the uterus, is a common disease among women. Numerous in vivo rodent models of endometriosis have been developed to explore multiple aspects of this poorly understood disease. Heterologous models utilize human endometrial tissues engrafted into immunocompromized mice, while homologous models engraft rodent endometrium into immunocompetent mice or rats. Heterologous models of endometriosis more closely replicate the human disease; however, the murine humoral immune response must be suppressed to prevent rejection of the xenograft tissue. Although the innate immune system remains intact, suppression of the humoral response leads to a markedly different local and systemic immune environments compared to humans. Despite this limitation, experiments using heterologous models have contributed significantly to our understanding of endometriosis establishment and progression, the pre-clinical effectiveness of various therapeutic strategies, and genetically modifiable host factors that contribute to disease. Unfortunately, a lack of harmonization of the models used by different laboratories has impeded the reproducibility and comparability of results between groups. Therefore, the World Endometriosis Research Foundation (WERF) formed an international working group of experts in heterologous models of endometriosis to develop guidelines and protocols that could contribute to unifying experimental approaches across laboratories. Nine critical variables were identified: (i) mouse strain; (ii) human tissue type; (iii) hormonal status of the human tissue donor; (iv) human tissue preparation; (v) method and location of tissue placement; (vi) hormonal status of the recipient animal; (vii) whether or not mice were engrafted with human immune cells; (viii) endpoint assessments; and (ix) number and type of replicates. Herein, we outline important considerations for each major variable and make recommendations for unification of approaches. Widespread adoption of harmonized protocols and implementation of standardized documentation and reporting should further improve the reproducibility and translation of experimental findings both within and between laboratories.
WERF Endometriosis Phenome and Biobanking Harmonisation Project for Experimental Models in Endometriosis Research (EPHect-EM-Homologous): homologous rodent models
In vivo models of endometriosis enable the discovery and preclinical testing of new therapies. Several rodent models of endometriosis exist, but a lack of harmonization impedes reproducibility and comparability of results among investigators. Homologous models are advantageous as they allow the contribution of the immune system/inflammation to be studied. We reviewed published homologous rodent models of endometriosis to develop standard operating procedures ('EPHect-EM-Homologous-SOPs') to guide and facilitate the choice and implementation of these models and harmonize documentation to enhance interpretation and comparability of results. The World Endometriosis Research Foundation (WERF) established an international working group of experts in models of endometriosis and formed a working sub-group to discuss homologous rodent models of endometriosis. A systematic literature review and detailed analysis of protocols was performed. The identified models have advantages and limitations regarding physiological relevance and utility. To harmonize key variables for endometriosis rodent models, the working group focused on species and animal strains, placement of ectopic tissue, uterine tissue volume, method of induction, hormonal status, and uterine tissue 'type'. A decision tree and recommendations on model use were developed for mice and rats to serve as guides for the use of harmonized EPHect-EM-Homologous-SOPs, experimental design, reporting standards, and research of question-dependent key variables. No 'ideal' homologous model of endometriosis was identified. The choice of model for specific research should be guided according to a best-fit strategy. Harmonization of SOPs, documentation, and reporting standards will improve replicability and translational applicability of studies and better highlight where de novo model creation is needed.
Prevalence and types of errors in the electronic health record: protocol for a mixed systematic review.
INTRODUCTION: In countries with access to the electronic health record (EHR), both patients and healthcare professionals have reported finding errors in the EHR, so-called EHRrors. These can range from simple typos to more serious cases of missing or incorrect health information. Despite their potential detrimental effect, the evidence on EHRrors has not been systematically analysed. It is unknown how common EHRrors are or how they impact patients and healthcare professionals. METHODS AND ANALYSIS: A mixed systematic review will be carried out to address the research gap. We will search PubMed, Web of Science and CINAHL for studies published since 2000, which report original research data on patient-identified and healthcare professional-identified EHRrors. We will analyse (1) the prevalence of EHRrors, (2) the types of EHRrors and (3) their impact on care. Quantitative and qualitative findings will be synthesised following the Joanna Briggs Institute Framework for Mixed Systematic Reviews. Identified studies will be critically appraised for meta-biases and risk of bias in individual studies. The confidence in the emerging evidence will be further assessed through the Grading of Recommendations Assessment, Development and Evaluation approach. Findings will be contextualised and interpreted involving an international team of patient representatives and practising healthcare professionals. ETHICS AND DISSEMINATION: The study will not involve collection or analysis of individual patient data; thus, ethical approval is not required. Results will be published in a peer-reviewed publication and further disseminated through scientific events and educational materials. PROSPERO REGISTRATION NUMBER: CRD42024622849.
Small for Gestational Age sub-groups have differential morbidity, growth and neurodevelopment at age 2: the INTERBIO-21st Newborn Study.
BACKGROUND: Small for Gestational Age (SGA) is a complex perinatal syndrome associated with increased neonatal morbidity, mortality, and impaired childhood growth and neurodevelopment. Current classifications rely primarily on birth weight, which does not capture the heterogeneity of the condition nor predict long-term health outcomes. Here we aim to identify and characterise distinct SGA sub-groups and assess their neonatal and early childhood health trajectories. OBJECTIVES: To refine the classification of SGA by identifying sub-groups based on maternal, fetal, and environmental factors and evaluating their associations with neonatal morbidity, growth, and neurodevelopment at age 2. STUDY DESIGN: Prospective Cohort Study. In six countries worldwide, between 2012 and 2018, the INTERBIO-21st Study enrolled SGA and non-SGA newborns defined by the <10th centile of international standards with moderate (≥3rd to <10th centile) and severe (<3rd centile) SGA sub-groups; we assessed their growth, health, nutrition, motor development, and neurodevelopment up to age 2. We used 2-step cluster analysis to identify SGA sub-groups, and a probabilistic approach to choose the optimal sub-group model based on a statistical measure of fit. We performed logistic regression analysis (OR; 95% CI) to assess health and development outcomes among sub-groups using the non-SGA as reference group, adjusting for key confounders. RESULTS: We enrolled 5153 non-SGA and 1549 SGA newborns: moderate (≥3rd to <10th centile) SGA=947 and severe (<3rd centile) SGA=602). We identified nine SGA sub-groups: 'no main condition detected' (29.0%); 'previous low birth weight (LBW)/preterm birth (PTB)' (14.6%); 'severe maternal disease' (12.0%); 'maternal short stature (11.6%); 'hypertensive disorders' (9.6%); 'extrauterine infection' (6.8%); 'previous miscarriage(s)' (6.5%); 'smoking' (5.2%), and 'maternal under-nutrition' (4.7%). Severe SGA newborns in the 'severe maternal disease' (OR: 3.2; 95% CI, 1.8-6.0), 'previous LBW/PTB' (OR: 2.8; 95% CI, 1.6-4.8), and 'smoking' (OR: 5.4; 95% CI, 1.3-21.8) sub-groups had increased risk of neonatal and long-term morbidity, and low anthropometric measures at age 2 as compared to the non-SGA group. Moderate SGA newborns in the "hypertensive disorders" sub-group had increased risk of neonatal morbidity (OR: 2.6; 95% CI, 1.5-4.6), and higher odds of scoring <10th centile of normative values in language (OR: 3.5; 95%CI, 1.0-12.0) and positive behavior (OR: 2.2; 95%CI, 1.1-4.5). The 'severe maternal disease' sub-group had also higher risk of deficit (<10th centile of normative values) in language (OR: 5.7; 95%CI, 1.3-24.8), positive behavior (OR: 3.4; 95%CI, 1.5-7.6). CONCLUSIONS: SGA comprises heterogeneous sub-groups with distinct patterns of neonatal morbidity, postnatal growth, and neurodevelopmental outcomes up to age 2.
OXSeg: Multidimensional Attention UNet-Based Lip Segmentation Using Semi-Supervised Lip Contours
Lip segmentation plays a crucial role in various domains, such as lip synchronization, lip-reading, and diagnostics. However, the effectiveness of supervised lip segmentation is constrained by the availability of lip contour in the training phase. A further challenge with lip segmentation is its reliance on image quality, lighting, and skin tone, leading to inaccuracies in the detected boundaries. To address these challenges, we propose a sequential lip segmentation method that integrates attention UNet and multidimensional input. We unravel the micro-patterns in facial images using local binary patterns to build multidimensional inputs. Subsequently, the multidimensional inputs are fed into sequential attention UNets, where the lip contour is reconstructed. We introduce a mask generation method that uses a few anatomical landmarks and estimates the complete lip contour to improve segmentation accuracy. This mask has been utilized in the training phase for lip segmentation. To evaluate the proposed method, we use facial images to segment the upper lips and subsequently assess lip-related facial anomalies in subjects with fetal alcohol syndrome (FAS). Using the proposed lip segmentation method, we achieved a mean dice score of 84.75%, and a mean pixel accuracy of 99.77% in upper lip segmentation. To further evaluate the method, we implemented classifiers to identify those with FAS. Using a generative adversarial network (GAN), we reached an accuracy of 98.55% in identifying FAS in one of the study populations. This method could be used to improve lip segmentation accuracy, especially around Cupid’s bow, and sheds light on distinct lip-related characteristics of FAS.
Latent Profiles of Early Language Development in a Large Finnish-Speaking Sample of the FinnBrain Birth Cohort Study.
PURPOSE: Research on early language development has primarily used two categories to group at-risk children, differing by the age at which risk is identified. Late talkers are toddlers with late onset of language development, some of whom may catch up with peers. Developmental language disorder is used to refer to children above the age of 4 years. To this day, the longitudinal relationship between the two categories remains unclear. In this study, we explored early language trajectories in a large birth cohort using exploratory methodology to gain better understanding of the types and prevalence of language trajectories from 14 months to 5 years of age, with particular interest in risk trajectories that cluster statistically. METHOD: We conducted latent profile analysis (LPA) on seven language variables collected between 1 and 5 years of age (N = 1,281). Multinomial logistic regression procedure was used to identify child and family characteristics that predicted profile memberships. RESULTS: The LPA yielded three profiles of language development described as persistent low, stable average, and stable high. Female sex, longer duration of pregnancy, and higher maternal socioeconomic status increased the odds of belonging to the stable high-language profile, whereas male sex and not being first born increased the odds of belonging to persistent low language profile. CONCLUSIONS: Contrary to previous research, we did not observe increasing or decreasing profiles, suggesting that toddler language difficulties tend to persist at age 5 years, at least in this birth cohort. This suggests commencing language intervention early instead of the wait-and-see approach. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.29441471.
Genome-wide associations spanning 194 in-hospital drug dosage change phenotypes highlight diverse genetic backgrounds in concurrent drug therapy
As populations get older and medicine consumption rises, the rate of concurrent drug use and polypharmacy among patients is increasing. Polypharmacy is known to complicate therapy and increase the risk of drug-drug interactions, the individuality of which remain largely unexplored. Here, we perform a series of genome-wide association studies to identify variants associated with dosage changes during episodes of concurrent drug therapy. We extracted in-hospital drug prescription records from 847,537 patients in a population-wide Danish hospital cohort. Using imputed genotype data from the Copenhagen Hospital Biobank and the Danish Blood Donor Study we then performed a series of genome-wide association analyses across 194 drug pair phenotypes fulfilling selection criteria. We identified 51 genome-wide significant (p < 5E-08) loci, 49 so far unreported in any genome-wide association studies, associated with dosage changes across 42 different drug pair phenotypes. 49 of the identified loci were unique to the respective drug pairs. Through annotation of the identified loci, expression quantitative trait loci analyses, and gene-based tests we found links to 57 distinct genes, several of which have previously been associated with disease. This study identifies genes that may modulate response to drug therapy in the context of polypharmacy. Our findings reveal distinct patterns of genetic variation across different drug pairs, suggesting a diverse set of genes involved in drug efficacy and drug response. This study may give a better understanding of the individuality of such mechanisms and may aid the development personalized treatment approaches.