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Abstract Background Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data. Methods 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups. Results Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p

Background: It is an unfortunate truth that Emergency Medicine (EM) physicians will, at some point, have contact with the medicolegal system. However, most EM residency training programs lack education on the legal system in their curriculum, leaving EM physicians unprepared for litigation. To fill this gap, we designed a high-yield and succinct medical legal workshop highlighting legal issues commonly encountered by EM physicians. We aimed to determine the effectiveness of this curriculum by measuring pre and post knowledge questions. Methods: A two-hour session included a case-based discussion of common misconceptions held by physicians about the legal system, proper steps when interacting with the legal system and review of legal documents. This session was developed with the involvement of our hospital legal counsel and discussed real encounters. The effectiveness of the session was determined using pre- and post-session surveys assessing participant knowledge and comfort approaching the scenarios. Results: A total of 34 EM residents had the opportunity to complete this workshop as a part of their conference curriculum. A total of 26 participants completed the pre-survey and 19 participants completed the post-survey. No participants had previous training in the legal aspects of medicine, including handling a subpoena, serving as a witness, or giving a deposition. The pre-survey demonstrated that there was significant uncertainty surrounding the processes, definitions, and the legal system interaction. Many participants stated they would not know what to do if they received a subpoena (85.71%), were called as a witness in a trial (96.43%) or receive correspondence from a lawyer (96.43%). The post survey revealed an increased knowledge base and confidence following the session. 100% of residents reported knowing what to do after receiving a subpoena, being called as a witness and understanding the process involved in giving a deposition. All residents reported that the session was beneficial and provided crucial information. Conclusion: EM residents have limited baseline understanding of how to approach common legal scenarios. Educational materials available for this curriculum topic are limited. Based on the rapid knowledge increase observed in our residents, we believe our workshop could be adapted for use at other residency programs.

Abstract Background Although whole-genome sequencing (WGS) is the preferred genotyping method for most genomic analyses, limitations are often experienced when studying genomes characterized by a high percentage of repetitive elements, high linkage, and recombination deserts. The Asian tiger mosquito (Aedes albopictus), for example, has a genome comprising up to 72% repetitive elements, and therefore we set out to develop a single-nucleotide polymorphism (SNP) chip to be more cost-effective. Aedes albopictus is an invasive species originating from Southeast Asia that has recently spread around the world and is a vector for many human diseases. Developing an accessible genotyping platform is essential in advancing biological control methods and understanding the population dynamics of this pest species, with significant implications for public health. Methods We designed a SNP chip for Ae. albopictus (Aealbo chip) based on approximately 2.7 million SNPs identified using WGS data from 819 worldwide samples. We validated the chip using laboratory single-pair crosses, comparing technical replicates, and comparing genotypes of samples genotyped by WGS and the SNP chip. We then used the chip for a population genomic analysis of 237 samples from 28 sites in the native range to evaluate its usefulness in describing patterns of genomic variation and tracing the origins of invasions. Results Probes on the Aealbo chip targeted 175,396 SNPs in coding and non-coding regions across all three chromosomes, with a density of 102 SNPs per 1 Mb window, and at least one SNP in each of the 17,461 protein-coding genes. Overall, 70% of the probes captured the genetic variation. Segregation analysis found that 98% of the SNPs followed expectations of single-copy Mendelian genes. Comparisons with WGS indicated that sites with genotype disagreements were mostly heterozygotes at loci with WGS read depth 20, indicating that the chip more accurately detects heterozygotes than low-coverage WGS. Sample sizes did not affect the accuracy of the SNP chip genotype calls. Ancestry analyses identified four to five genetic clusters in the native range with various levels of admixture. Conclusions The Aealbo chip is highly accurate, is concordant with genotypes from WGS with high sequence coverage, and may be more accurate than low-coverage WGS. Graphical Abstract

The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution Ku-band radar altimetry data over the polar regions up to 81° North. The combination of synthetic aperture radar (SAR) mode altimetry (SRAL instrument) from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise this synergy between altimetry and imagery to demonstrate a novel application of deep learning to distinguish sea ice from leads in spring. We use SRAL classified leads as training input for pan-Arctic lead detection from OLCI imagery. This surface classification is an important step for estimating sea ice thickness and to predict future sea ice changes in the Arctic and Antarctic regions. We propose the use of Vision Transformers (ViT), an approach adapting the popular deep learning algorithm Transformer, for this task. Their effectiveness, in terms of both quantitative metric including accuracy and qualitative metric including model roll-out, on several entire OLCI images is demonstrated and we show improved skill compared to previous machine learning and empirical approaches. We show the potential for this method to provide lead fraction retrievals at improved accuracy and spatial resolution for sunlit periods before melt onset.

Abstract Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out (POCO) around nuclear facilities each year, resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases. The UK Nuclear Decommissioning Authority supports the view that human‐in‐the‐loop (HITL) robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites. The authors present a novel implementation of a Cyber‐Physical System (CPS) deployed in an analogue nuclear environment, comprised of a multi‐robot (MR) team coordinated by a HITL operator through a digital twin interface. The development of the CPS created efficient partnerships across systems including robots, digital systems and human. This was presented as a multi‐staged mission within an inspection scenario for the heterogeneous Symbiotic Multi‐Robot Fleet (SMuRF). Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together, where a single robot would face challenges in full characterisation of radiation. Key contributions include the demonstration of symbiotic autonomy and query‐based learning of an autonomous mission supporting scalable autonomy and autonomy as a service. The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.

Abstract Introduction In the context of the COVID-19 pandemic, upstream interventions that tackle social determinants of health inequalities have never been more important. Evaluations of upstream cash transfer trials have failed to capture comprehensively the impacts that such systems might have on population health through inadequate design of the interventions themselves and failure to implement consistent, thorough research measures that can be used in microsimulations to model long-term impact. In this article, we describe the process of developing a generic, adaptive protocol resource to address this issue and the challenges involved in that process. The resource is designed for use in high-income countries (HIC) but draws on examples from a UK context to illustrate means of development and deployment. The resource is capable of further adaptation for use in low- and middle-income countries (LMIC). It has particular application for trials of Universal Basic Income but can be adapted to those covering other kinds of cash transfer and welfare system changes. Methods We outline two types of prospective intervention based on pilots and trials currently under discussion. In developing the remainder of the resource, we establish six key principles, implement a modular approach based on types of measure and their prospective resource intensity, and source (validated where possible) measures and baseline data primarily from routine collection and large, longitudinal cohort studies. Through these measures, we seek to cover all areas of health impact identified in our theoretical model for use in pilot and feasibility studies. Results We find that, in general, self-reported measures alongside routinely collected linked respondent data may provide a feasible means of producing data capable of demonstrating comprehensive health impact. However, we also suggest that, where possible, physiological measures should be included to elucidate underlying biological effects that may not be accurately captured through self-reporting alone and can enable modelling of long-term health outcomes. In addition, accurate self-reported objective income data remains a challenge and requires further development and testing. A process of development and implementation of the resource in pilot and feasibility studies will support assessment of whether or not our proposed health outcome measures are acceptable, feasible and can be used with validity and reliability in the target population. Discussion We suggest that while Open Access evaluation instruments are available and usable to measure most constructs of interest, there remain some areas for which further development is necessary. This includes self-reported wellbeing measures that require paid licences but are used in a range of nationally important longitudinal studies instead of Open Access alternatives.

Abstract The increasing level of air pollutants (e.g. particulates, noise and gases) within the atmosphere are impacting mental wellbeing. In this paper, we define the term ‘DigitalExposome’ as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodal mobile sensing technology. Specifically, we simultaneously collected (for the first time) multi-sensor data including urban environmental factors (e.g. air pollution including: Particulate Matter (PM1), (PM2.5), (PM10), Oxidised, Reduced, Ammonia (NH3) and Noise, People Count in the vicinity), body reaction (physiological reactions including: EDA, HR, HRV, Body Temperature, BVP and movement) and individuals’ perceived responses (e.g. self-reported valence) in urban settings. Our users followed a pre-specified urban path and collected the data using a comprehensive sensing edge device. The data is instantly fused, time-stamped and geo-tagged at the point of collection. A range of multivariate statistical analysis techniques have been applied including Principle Component Analysis, Regression and Spatial Visualisations to unravel the relationship between the variables. Results showed that Electrodermal Activity (EDA) and Heart Rate Variability (HRV) are noticeably impacted by the level of Particulate Matter in the environment. Furthermore, we adopted Convolutional Neural Network (CNN) to classify self-reported wellbeing from the multimodal dataset which achieved an f1-score of 0.76.

Background: People with chronic kidney disease (CKD) have significantly higher morbidity and mortality following myocardial infarction (MI) than the general population. Despite this, they are less likely to receive invasive management and guideline-directed medications than those without kidney disease. It is unclear why this treatment variation exists and whether or not it reflects equal and appropriate access to care for the high-risk CKD population. Aims: The aims are to (1) understand how management decisions following an MI are made for, and with, people with CKD, and (2) describe the experiences of people with CKD who have recently received MI care in England. Method: We will recruit 15–20 patients with CKD and previous MI, and 15–20 clinical staff who are involved in treatment decision-making, from 3–5 NHS hospital trusts in the UK. Purposive sampling will be used to ensure diversity in participants with respect to age, gender, ethnicity and, where relevant, profession or degree of kidney impairment. We will undertake semi-structured interviews following flexible topic guides developed for patients and clinical staff. Interviews will be audio-recorded and transcribed. Transcripts will be analysed using the six steps outlined in Braun and Clarke’s thematic analysis, within an interpretivist, constructionist approach. Analysis will be primarily inductive. We will label sections of text with codes to reflect semantic or latent meaning and cluster similar codes together to derive themes which we will define and name. We will produce a report comprising themes central to MI treatment decision-making in people with CKD, illustrated with examples of data and analytic commentary, and demonstrate where our research stands within the context of the literature and wider context.

Water quality practices are commonly implemented to reduce human impacts on land and water resources. In series or parallel in a landscape, systems of practices can reduce local and downstream pollution delivery. Many practices function via physical, chemical, and biological processes that are dependent on weather and climate. Climate change will alter the function of many such systems, though effects will vary in different hydroclimatic and watershed settings. Reducing the risk of impacts will require risk-based, adaptive planning. Here, we review the literature addressing climate change effects on practices commonly used to mitigate the water quality impacts of urban stormwater, agriculture, and forestry. Information from the general literature review is used to make qualitative inferences about the resilience of different types of practices. We discuss resilience in the context of two factors: the sensitivity of practice function to changes in climatic drivers, and the adaptability, or relative ease with which a practice can be modified as change occurs. While only a first step in addressing a complex topic, our aim is to help communities incorporate consideration of resilience to climate change as an additional factor in decisions about water quality practices to meet long-term goals. HIGHLIGHTS Many water quality practices function via physical, chemical, and biological processes sensitive to weather and climate.; Climate change presents a risk to practice-based investments in water quality protection; more resilient practices can help reduce this risk.; Practice resilience can be characterized in terms of sensitivity to changes in weather and climate and ability to modify the practice over time.; This review is intended to help communities and water resource managers consider climate resilience when adopting practices to meet water quality goals.;

To address the need for collaborative approaches to managing dementia in primary care, we implemented the Living with Dementia (LWD) program in a geriatric primary care clinic. This study evaluated the impact of short (≤6 months) and longer-term (7+ months) participation in LWD on care partner outcomes (i.e., self-efficacy, depression, and burden) using t -tests and examined dementia support topics discussed with care partners through the intervention using deductive content analysis. Across 20 months analyzed, 57 dyads participated in the LWD program. Short and longer-term LWD participation indicated a significant increase in self-efficacy with small effect sizes; no changes were observed in depression or burden. Dementia support topics most frequently discussed with care partners focused on care partner well-being, behavior management, and offering referrals. This early evaluation suggests a collaborative care program integrated into primary care can address needs related to caring for persons with dementia and may improve care partner self-efficacy.

There is an intriguing association between winter births and subsequent increased risk of schizophrenia. However, little is known about the environmental risk factors that contribute this month-of-birth effect. The aims of this study were to carry out a systematic review and meta-analysis of studies investigating the month-of-birth effect in schizophrenia and to explore possible factors such as latitude, daylight and infections that could explain this epidemiological observation. Medline, Embase and the Cochrane Library were searched for articles published up to December 23, 2021. Study selection, data extraction and analysis were undertaken according to Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines. Generic inverse-variance with random effects models were used to determine the risk ratios (RR) and 95% confidence intervals (CI) for each month-of-birth. Associations between variables latitude and daylight were investigated using linear regression and Kendall's rank correlation coefficients were calculated assess the relationship between monthly infections rates schizophrenia births. Ten studies were included in the meta-analysis encompassing 262,188 schizophrenia patients. We identified significantly higher number of schizophrenia births in December [1.04 (95%CI 1.00–1.08)], January [1.06 (95%CI 1.03–1.1)] and February [1.03 (95%CI 1.00–1.05)]. We did not find any association between latitude and the magnitude of the month-of-birth effect. On the other hand, we found a significant negative correlation between monthly severe enterovirus cases and schizophrenia births (tau −0.57, p = 0.0099) using data from Taiwan. This highlights a role for enterovirus infections in mediating the month-of-birth effect in schizophrenia and these results carry implications for disease prevention strategies.

Mesothelioma in situ is a recently described precursor to invasive mesothelioma. Thus far, all cases in the literature have involved one pleural cavity or the peritoneal cavity. We describe a patient with biopsy-proven mesothelioma in situ involving both pleural cavities and the peritoneal cavity. Genetic analysis results revealed that the patient had a BAP1 germline mutation. This is the first report of mesothelioma in situ involving multiple body cavities and raises a question of whether such patients will all have BAP1 germline mutations.

In recent years the neurobiological underpinnings of catatonia have been an emerging area of interest. Catatonia is frequently encountered in mood disorders, neurological disorders and systemic illnesses. Furthermore, the manifestation of catatonia in autoimmune disorders such as NMDA receptor antibody encephalitis and thyroiditis reinforces its neuropsychiatric nature. Irrespective of cause benzodiazepines and electroconvulsive therapy remain the standard treatments for catatonia, although a proportion fail to respond to the same. This report describes three women with pre-existing bipolar disorder presenting in catatonia. Interestingly in all three, while benzodiazepines and electroconvulsive therapy failed, a dramatic resolution of catatonia with corticosteroids was noted following the detection of Hashimoto's thyroiditis. Hashimoto's encephalopathy presenting as catatonia has been reported, but our patients' profile differed in having had an a priory diagnosis of bipolar disorder. Given that both catatonia and thyroid dysfunction are frequently encountered in bipolar disorder, Hashimoto's encephalopathy as a potential cause for this concurrent manifestation in bipolar disorder may be overlooked. Therefore, it is essential to suspect Hashimoto's encephalopathy when catatonia manifests in bipolar disorder. A timely evaluation would be prudent as they may fail to respond to standard treatments for catatonia but respond remarkably to corticosteroids, saving much time and angst. Recent evidence implicates immune system dysfunction, with neuroinflammation and peripheral immune dysregulation contributing to the pathophysiology of bipolar disorder as well as catatonia. Findings from this study reaffirm the role of immune system dysfunction common to the etiopathogenesis of all these disorders, highlighting the complex interplay between catatonia, thyroiditis and bipolar disorder.

Abstract Endothelial erosion of atherosclerotic plaques is the underlying cause of approximately 30% of acute coronary syndromes (ACS). As the vascular endothelium is profoundly affected by the haemodynamic environment to which it is exposed, we employed computational fluid dynamic (CFD) analysis of the luminal geometry from 17 patients with optical coherence tomography (OCT)-defined plaque erosion, to determine the flow environment permissive for plaque erosion. Our results demonstrate that 15 of the 17 cases analysed occurred on stenotic plaques with median 31% diameter stenosis (interquartile range 28–52%), where all but one of the adherent thrombi located proximal to, or within the region of maximum stenosis. Consequently, all flow metrics related to elevated flow were significantly increased (time averaged wall shear stress, maximum wall shear stress, time averaged wall shear stress gradient) with a reduction in relative residence time, compared to a non-diseased reference segment. We also identified two cases that did not exhibit an elevation of flow, but occurred in a region exposed to elevated oscillatory flow. Our study demonstrates that the majority of OCT-defined erosions occur where the endothelium is exposed to elevated flow, a haemodynamic environment known to evoke a distinctive phenotypic response in endothelial cells.

Sea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness does not extend over multi-decadal timescales. Launched on the Terra satellite in 1999, the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles. Extending on previous work to model surface roughness from specular anisotropy, a training dataset of cloud-free angular reflectance signatures and surface roughness, defined as the standard deviation of the within-pixel lidar elevations, from near-coincident operation IceBridge (OIB) airborne laser data is generated and is modelled using support vector regression (SVR) with a radial basis function (RBF) kernel selected. Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an R2 (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson’s r averaged over six scenes, r = 0.58, p < 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman’s rank, rs = 0.66, p < 0.001), a known roughness proxy. We present a derived sea-ice roughness product at 1.1 km resolution (2000–2020) over the seasonal period of OIB operation and a corresponding time-series analysis. Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable potential in detecting surface-ice characteristics such as deformed rough ice, thin refrozen leads, and polynyas.

Susceptibility to schizophrenia is mediated by genetic and environmental risk factors. Infection driven maternal immune activation (MIA) during pregnancy is a key environmental risk factor. However, little is known about how MIA during pregnancy could contribute to adult-onset schizophrenia. In this study, we investigated if maternal immune activation induces changes in methylation of genes linked to schizophrenia. We found that differentially expressed genes in schizophrenia brain were significantly enriched among MIA induced differentially methylated genes in the foetal brain in a cell-type-specific manner. Upregulated genes in layer V pyramidal neurons were enriched among hypomethylated genes at gestational day 9 (fold change = 1.57, FDR = 0.049) and gestational day 17 (fold change = 1.97, FDR = 0.0006). A linear regression analysis, which showed a decrease in gene expression with an increase in methylation in gestational day 17, supported findings from our enrichment analysis. Collectively, our results highlight a connection between MIA driven methylation changes during gestation and schizophrenia gene expression signatures in the adult brain. These findings carry important implications for early preventative strategies in schizophrenia.

Background: Context-specific evidence of the spectrum of type 2 diabetes (T2D) burden is essential for setting priorities and designing interventions to reduce associated morbidity and mortality. However, there are currently limited data on the burden of T2D complications and comorbidity in sub-Saharan Africa (SSA). Methods: T2D complications and comorbidities were assessed in 2,784 participants with diabetes enrolled from tertiary health centres and contextualised in 3,209 individuals without diabetes in Nigeria, Ghana and Kenya. T2D complications and comorbidities evaluated included cardiometabolic, ocular, neurological and renal characteristics. Findings: The most common complications/comorbidities among the T2D participants were hypertension (71%; 95% CI 69–73), hyperlipidaemia (34%; 95% CI 32–36), and obesity (27%; 95% CI 25–29). Additionally, the prevalence of cataracts was 32% (95% CI 30–35), diabetic retinopathy 15% (95% CI 13–17), impaired renal function 13% (95% CI 12–15), and erectile dysfunction (in men) 35% (95% CI 32–38). T2D population-attributable fraction for these comorbidities ranged between 6 and 64%. Interpretation: The burden of diabetes complications and comorbidity is substantial in SSA highlighting the urgent need for innovative public health strategies that prioritise promotion of healthy lifestyles for prevention and early detection of T2D. Also needed are strategies to strengthen health care system capacities to provide treatment and care for diabetes complications. Keywords: Type 2 Diabetes, Sub-Sahara Africa, Complications, Co-morbidity, Epidemiology

Abstract Malignant mesothelioma (MM) is an almost invariably fatal cancer caused by asbestos exposure. The toxicity of asbestos fibers is related to their physicochemical properties and the generation of free radicals. We set up a pilot study to investigate the potential of the zeolite clinoptilolite to counteract the asbestos carcinogenesis by preventing the generation of reactive nitrogen and oxygen radicals. In cell culture experiments, clinoptilolite prevented asbestos‐induced cell death, reactive oxygen species production, DNA degradation, and overexpression of genes known to be up‐regulated by asbestos. In an asbestos‐induced transgenic mouse model of MM, mice were injected intraperitoneal injections with blue asbestos, with or without clinoptilolite, and monitored for 30 weeks. By the end of the trial all 13 mice injected with asbestos alone had reached humane end points, whereas only 7 of 29 mice receiving crocidolite and clinoptilolite reached a similar stage of disease. Post‐mortem examination revealed pinpoint mesothelioma‐like tumors in affected mice, and the absence of tumor formation in surviving mice. Interestingly, the macrophage clearance system, which was largely suppressed in asbestos‐treated mice, exhibited evidence of increased phagocytosis in mice treated with asbestos and clinoptilolite. Our study suggests that inhibiting the asbestos‐induced generation of reactive oxygen species and stimulating the macrophage system may represent a pathway to amelioration of asbestos‐induced toxicity. Additional studies are warranted to explore the underlying mechanisms responsible for our observations.