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Advancements in Fetal Heart Rate Monitoring: A Report on Opportunities and Strategic Initiatives for Better Intrapartum Care.
Cardiotocography (CTG), introduced in the 1960s, was initially expected to prevent hypoxia-related deaths and neurological injuries. However, more than five decades later, evidence supporting the evidence of intrapartum CTG in preventing neonatal and long-term childhood morbidity and mortality remains inconclusive. At the same time, shortcomings in CTG interpretation have been recognised as important contributory factors to rising caesarean section rates and missed opportunities for timely interventions. An important limitation is its high false-positive rate and poor specificity, which undermines reliably identifying foetuses at risk of hypoxia-related injuries. These shortcomings are compounded by the technology's significant intra- and interobserver variability, as well as the subjective and complex nature of fetal heart rate interpretation. However, human factors and other environmental factors are equally significant. Advancements in fetal heart rate monitoring are crucial to support clinicians in improving health outcomes for newborns and their mothers, while at the same time avoiding unnecessary operative deliveries. These limitations highlight the clinical need to enhance neonatal outcomes while minimising unnecessary interventions, such as instrumental deliveries or caesarean sections. We believe that achieving this requires a paradigm shift from subjective interpretation of complex and nonspecific fetal heart rate patterns to evidence-based, quantifiable solutions that integrate hardware, engineering and clinical perspectives. Such transformation necessitates an international, multidisciplinary effort encompassing the entire continuum of pregnancy care and the broader healthcare ecosystem, with emphasis on well-defined, actionable health outcomes. Achieving this will depend on collaborations between researchers, clinicians, medical device manufacturers and other relevant stakeholders. This expert review paper outlines the most relevant and promising directions for research and strategic initiatives to address current challenges in fetal heart rate monitoring. Key themes include advancements in computerised fetal heart rate monitoring, the application of big data and artificial intelligence, innovations in home and remote monitoring and consideration of human factors.
Gated Self Attention Convolutional Neural Networks for Predicting Adverse Birth Outcomes
Early detection of adverse birth outcomes is vital as they are major contributors to neonatal mortality and irreversible neurological complications in infants. These outcomes are typically linked to impaired blood and oxygen flow to the baby brain during or shortly after labour, making its early detection vital. Monitoring fetal heart rate (FHR) is crucial in identifying and capturing these complications. This study proposes a deep learning (DL) framework for enhancing the early detection of the babies at risk, leveraging both raw FHR signals and standard cardiotocography (CTG) features. Unlike traditional methods that primarily focus on abnormal CTG traces (but not birth outcomes), this approach, backed by a substantial cohort of patient records, demonstrates the potential of DL in predicting actual adverse outcomes as early as possible. The DL model combines a convolutional mechanism with a self-attention network, enhanced by a gating mechanism for more accurate feature learning. The investigated DL architecture is trained on a dataset of over 37,000 births, including 1,291 abnormal ones, and is evaluated on a holdout set of 6,459 births, as well as the open-access CTU-CHB CTG dataset of 552 births. The proposed DL model demonstrates superior diagnostic accuracy, outperforming state-of-the-art baseline methods and clinical benchmarks. It achieved sensitivity of 49.08% (95% CI, 46.01-53.36%) at 15% false positive rate (FPR), compared to the clinical benchmark sensitivity of 37.70% (33.10-42.30%) and a previous model's 32.60% (28.20-37.30%) at a similar FPR.
Longitudinal analysis of the ABCD® study.
The Adolescent Brain Cognitive Development® (ABCD) Study provides a unique opportunity to investigate developmental processes in a large, diverse cohort of youths, aged approximately 9-10 at baseline and assessed annually for 10 years. Given the size and complexity of the ABCD Study, researchers analyzing its data will encounter a myriad of methodological and analytical considerations. This review provides an examination of key concepts and techniques related to longitudinal analyses of the ABCD Study data, including: (1) characterization of the factors associated with variation in developmental trajectories; (2) assessment of how level and timing of exposures may impact subsequent development; (3) quantification of how variation in developmental domains may be associated with outcomes, including mediation models and reciprocal relationships. We emphasize the importance of selecting appropriate statistical models to address these research questions. By presenting the advantages and potential challenges of longitudinal analyses in the ABCD Study, this review seeks to equip researchers with foundational knowledge and tools to make informed decisions as they navigate and effectively analyze and interpret the multi-dimensional longitudinal data currently available.
A generative model for evaluating missing data methods in large epidemiological cohorts.
BackgroundThe potential value of large scale datasets is constrained by the ubiquitous problem of missing data, arising in either a structured or unstructured fashion. When imputation methods are proposed for large scale data, one limitation is the simplicity of existing evaluation methods. Specifically, most evaluations create synthetic data with only a simple, unstructured missing data mechanism which does not resemble the missing data patterns found in real data. For example, in the UK Biobank missing data tends to appear in blocks, because non-participation in one of the sub-studies leads to missingness for all sub-study variables.MethodsWe propose a tool for generating mixed type missing data mimicking key properties of a given real large scale epidemiological data set with both structured and unstructured missingness while accounting for informative missingness. The process involves identifying sub-studies using hierarchical clustering of missingness patterns and modelling the dependence of inter-variable correlation and co-missingness patterns.ResultsOn the UK Biobank brain imaging cohort, we identify several large blocks of missing data. We demonstrate the use of our tool for evaluating several imputation methods, showing modest accuracy of imputation overall, with iterative imputation having the best performance. We compare our evaluations based on synthetic data to an exemplar study which includes variable selection on a single real imputed dataset, finding only small differences between the imputation methods though with iterative imputation leading to the most informative selection of variables.ConclusionsWe have created a framework for simulating large scale data with that captures the complexities of the inter-variable dependence as well as structured and unstructured informative missingness. Evaluations using this framework highlight the immense challenge of data imputation in this setting and the need for improved missing data methods.
Integrating the environmental and genetic architectures of aging and mortality.
Both environmental exposures and genetics are known to play important roles in shaping human aging. Here we aimed to quantify the relative contributions of environment (referred to as the exposome) and genetics to aging and premature mortality. To systematically identify environmental exposures associated with aging in the UK Biobank, we first conducted an exposome-wide analysis of all-cause mortality (n = 492,567) and then assessed the associations of these exposures with a proteomic age clock (n = 45,441), identifying 25 independent exposures associated with mortality and proteomic aging. These exposures were also associated with incident age-related multimorbidity, aging biomarkers and major disease risk factors. Compared with information on age and sex, polygenic risk scores for 22 major diseases explained less than 2 percentage points of additional mortality variation, whereas the exposome explained an additional 17 percentage points. Polygenic risk explained a greater proportion of variation (10.3-26.2%) compared with the exposome for incidence of dementias and breast, prostate and colorectal cancers, whereas the exposome explained a greater proportion of variation (5.5-49.4%) compared with polygenic risk for incidence of diseases of the lung, heart and liver. Our findings provide a comprehensive map of the contributions of environment and genetics to mortality and incidence of common age-related diseases, suggesting that the exposome shapes distinct patterns of disease and mortality risk, irrespective of polygenic disease risk.
Gestational trophoblastic disease: understanding the molecular mechanisms of placental tumours.
Gestational trophoblastic disease (GTD) describes a group of rare benign and cancerous lesions originating from the trophoblast cells of the placenta. These neoplasms are unconventional entities, being one of the few instances in which cancer develops from the cells of another organism, the foetus. Although this condition was first described over 100 years ago, the specific genetic and non-genetic drivers of this disease remain unknown to this day. However, recent findings have provided valuable insights into the potential mechanisms underlying this rare condition. Unlike previous reviews focused primarily on the clinical and diagnostic aspects of disease development, this Review consolidates the latest research concerning the role of genetics, epigenetics and microRNAs in the initiation and progression of GTD. By examining GTD from a molecular perspective, this Review provides a unique framework for understanding the pathogenesis and progression of this rare disease.
Sonic hedgehog medulloblastoma cells in co-culture with cerebellar organoids converge towards in vivo malignant cell states.
BackgroundIn the malignant brain tumor sonic hedgehog medulloblastoma (SHH-MB) the properties of cancer cells are influenced by their microenvironment, but the nature of those effects and the phenotypic consequences for the tumor are poorly understood. The aim of this study was to identify the phenotypic properties of SHH-MB cells that were driven by the nonmalignant tumor microenvironment.MethodsHuman induced pluripotent cells (iPSC) were differentiated to cerebellar organoids to simulate the nonmaliganant tumor microenvironment. Tumor spheroids were generated from 2 distinct, long-established SHH-MB cell lines which were co-cultured with cerebellar organoids. We profiled the cellular transcriptomes of malignant and nonmalignant cells by performing droplet-based single-cell RNA sequencing (scRNA-seq). The transcriptional profiles of tumor cells in co-culture were compared with those of malignant cell monocultures and with public SHH-MB datasets of patient tumors and patient-derived orthotopic xenograft (PDX) mouse models.ResultsSHH-MB cell lines in organoid co-culture adopted patient tumor-associated phenotypes and showed increased heterogeneity compared to monocultures. Subpopulations of co-cultured SHH-MB cells activated a key marker of differentiating granule cells, NEUROD1 that was not observed in tumor monocultures. Other subpopulations expressed transcriptional determinants consistent with a cancer stem cell-like state that resembled cell states identified in vivo.ConclusionsFor SHH-MB cell lines in co-culture, there was a convergence of malignant cell states towards patterns of heterogeneity in patient tumors and PDX models implying these states were non-cell autonomously induced by the microenvironment. Therefore, we have generated an advanced, novel in vitro model of SHH-MB with potential translational applications.
Age-related differences in staging, treatment and net survival in relation to frailty in adults with colon cancer in England: an analysis of the COloRECTal cancer data repository (CORECT-R) resource.
ObjectiveTo describe the distribution of disease stages, receipt of major surgery, 1-year net survival (NS) and 1-year conditional NS in relation to age and frailty in adults aged ≥50 diagnosed with colon cancer in England.MethodsWe obtained data on adults aged 50-99 diagnosed with colon cancer between 2014 and 2019, followed up through December 2021, from the national population-based COloRECTal cancer Repository. Frailty was assessed using the Secondary Care Administrative Records Frailty (SCARF) index categorised into fit, mild, moderate and severe frailty. Data on major resection were obtained through linkage with Hospital Episode Statistics dataset. Major resection rates were calculated in adults with stage I-III cancer. Descriptive statistics were used as appropriate. One-year NS from cancer diagnosis and 1-year conditional NS were estimated using the Pohar-Perme estimator.ResultsOut of 130 360 individuals (48% females-50% over 75), 48.9% were fit, ranging from 69% in the 50-64 age group to 31% in the 85-99 age group. Over 80% of adults with stage I-III cancer underwent a major resection. This percentage was 58% amongst fit adults aged over 85. One-year NS decreased as age increased across all frailty levels. Differences in NS between the 50-64 age group and the 85-99 age group were reduced in adults who survived beyond 1 year from diagnosis except for severely frail adults.ConclusionThis population-based study shows that a non-negligible proportion of older adults diagnosed with colon cancer and deemed 'fit' through the SCARF did not receive surgery that may impact their survival.
Quantifying the relative intensity of free-living physical activity: differences across age, association with mortality and clinical interpretation-an observational study.
To describe age-related differences in the absolute and relative intensity of physical activity (PA) and associations with mortality. UK Biobank participants with accelerometer-assessed PA (mg) and fitness data (N=11 463; age: 43-76 years) were included. The intensity distribution of PA was expressed in absolute and relative terms. The outcome was mortality. PA volume (average acceleration) and absolute intensity were lower with increasing age (~-0.03 to -0.04 SD of mean value across all ages per year; p<0.001) but differences in relative intensity by age were markedly smaller in women (-0.003 SD; p<0.184) and men (-0.012 SD; p<0.001). Absolute intensity was higher in men, but relative intensity higher in women (p<0.001). Over a median (IQR) follow-up of 8.1 (7.5-8.6) years, 121 (2.4 per 1000-person-years) deaths occurred in women and 203 (5.0 per 1000-person-years) in men. Lower risk of mortality was observed for increasing absolute or relative intensity in women, but for absolute intensity only in men. In men, the lowest risk (HR 0.62, 95% CI 0.43, 0.91) was observed in those with high absolute intensity (80th centile), but low relative intensity (20th centile). Conversely, in women, the lowest risk was associated with high levels (80th centile) of both absolute and relative intensity (HR 0.59, 95% CI 0.41, 0.86). Absolute PA intensity dropped with age, while relative intensity was fairly stable. Associations between PA intensity and mortality suggest that prescribing intensity in absolute terms appears appropriate for men, while either absolute or relative terms may be appropriate for women.
Artificial intelligence for modelling infectious disease epidemics.
Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI.
UK Biobank-A Unique Resource for Discovery and Translation Research on Genetics and Neurologic Disease.
UK Biobank is a large-scale prospective study with extensive genetic and phenotypic data from half a million adults. Participants, aged 40 to 69, were recruited from the general UK population between 2006 and 2010. During recruitment, participants completed questionnaires covering lifestyle and medical history, underwent physical measurements, and provided biological samples for long-term storage. Whole-cohort assays have been conducted, including biochemical markers, genotyping, whole-exome and whole-genome sequencing, as well as proteomics and metabolomics in large subsets of the cohort, with potential for additional assays in the future. Participants consented to link their data to electronic health records, enabling the identification of health outcomes over time. Research studies using UK Biobank data have already enhanced our understanding of the role of genetic variation in neurologic disease, offering insights into potential therapeutic approaches. The integration of genetic and imaging data has led to significant discoveries regarding the relationship between genetic variants and brain structure and function, particularly in Alzheimer disease and Parkinson disease. Genetic data have also allowed Mendelian randomization analyses to be performed, enabling further investigation into the causality of associations between behavioral and physiologic factors-such as diet and blood pressure-and neurologic outcomes. Furthermore, genetic and proteomic data have been particularly useful in identifying new drug targets for neurologic disease and in enhancing risk prediction algorithms that are increasingly applied in clinical practice to identify those at higher risk. As UK Biobank continues to be enhanced, and the cases of neurologic disease accrue over time, the study will become increasingly valuable for both discovery and translational research on genetics and neurologic disease.
Large simple randomized controlled trials-from drugs to medical devices: lessons from recent experience.
Randomized controlled trials (RCTs) are the cornerstone of modern evidence-based medicine. They are considered essential to establish definitive evidence of efficacy and safety for new drugs, and whenever possible they should also be the preferred method for investigating new high-risk medical devices. Well-designed studies robustly inform clinical practice guidelines and decision-making, but administrative obstacles have made it increasingly difficult to conduct informative RCTs. The obstacles are compounded for RCTs of high-risk medical devices by extra costs related to the interventional procedure that is needed to implant the device, challenges with willingness to randomize patients throughout a trial, and difficulties in ensuring proper blinding even with sham procedures. One strategy that may help is to promote the wider use of simpler and more streamlined RCTs using data that are collected routinely during healthcare delivery. Recent large simple RCTs have successfully compared the performance of drugs and of high-risk medical devices, against alternative treatments; they enrolled many patients in a short time, limited costs, and improved efficiency, while also achieving major impact. From a task conducted within the CORE-MD project, we report from our combined experience of designing and conducting large pharmaceutical trials during the COVID-19 pandemic, and of planning and coordinating large registry-based RCTs of cardiovascular devices. We summarize the essential principles and utility of large simple RCTs, likely applicable to all interventions but especially in order to promote their wider adoption to evaluate new medical devices.
Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation
Models for segmentation of brain lesions in multi-modal MRI are commonly trained for a specific pathology using a single database with a predefined set of MRI modalities, determined by a protocol for the specific disease. This work explores the following open questions: Is it feasible to train a model using multiple databases that contain varying sets of MRI modalities and annotations for different brain pathologies? Will this joint learning benefit performance on the sets of modalities and pathologies available during training? Will it enable analysis of new databases with different sets of modalities and pathologies? We develop and compare different methods and show that promising results can be achieved with appropriate, simple and practical alterations to the model and training framework. We experiment with 7 databases containing 5 types of brain pathologies and different sets of MRI modalities. Results demonstrate, for the first time, that joint training on multi-modal MRI databases with different brain pathologies and sets of modalities is feasible and offers practical benefits. It enables a single model to segment pathologies encountered during training in diverse sets of modalities, while facilitating segmentation of new types of pathologies such as via follow-up fine-tuning. The insights this study provides into the potential and limitations of this paradigm should prove useful for guiding future advances in the direction. Code and pretrained models: https://github.com/WenTXuL/MultiUnet.
Effect of vitamin A on adult lung function: a triangulation of evidence approach.
BackgroundVitamin A, an essential micronutrient obtained through the diet, plays a crucial role in lung development and contributes to lung regeneration. We aimed to investigate its effect on adult lung function using triangulation of evidence from both observational and genetic data.MethodsUsing data on 150 000 individuals from UK Biobank and correcting for measurement error (generalised structural equation modelling), we first investigated the association of dietary vitamin A intake (total vitamin A, carotene and retinol) with lung function (forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1)/FVC)). We then assessed the causality of these associations using Mendelian randomisation (MR), and we investigated the effects on adult lung function of 39 genes related to vitamin A, and their interaction with vitamin A intake.FindingsOur observational analysis suggests a positive association between carotene intake and FVC only (13.3 mL per 100 µg/day; p=2.9×10-9), with stronger associations in smokers, but no association of retinol intake with FVC or FEV1/FVC. The MR similarly shows a beneficial effect of serum beta-carotene on FVC only, with no effect of serum retinol on FVC nor FEV1/FVC. Nine of the vitamin A-related genes were associated with adult lung function, six of which have not been previously identified in genome-wide studies and three (NCOA2, RDH10, RXRB) in any type of genetic study of lung function. Five genes showed possible gene-vitamin A intake interactions.InterpretationOur triangulation study provides convincing evidence for a causal effect of vitamin A, carotene in particular, on adult lung function, suggesting a beneficial effect of a carotene-rich diet on adult lung health.