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Identification of undetected SARS-CoV-2 infections by clustering of Nucleocapsid antibody trajectories.
During the COVID-19 pandemic, numerous SARS-CoV-2 infections remained undetected. We combined results from routine monthly nose and throat swabs, and self-reported positive swab tests, from a UK household survey, linked to national swab testing programme data from England and Wales, together with Nucleocapsid (N-)antibody trajectories clustered using a longitudinal variation of K-means (N = 185,646) to estimate the number of infections undetected by either approach. Using N-antibody (hypothetical) infections and swab-positivity, we estimated that 7.4% (95%CI: 7.0-7.8%) of all true infections (detected and undetected) were undetected by both approaches, 25.8% (25.5-26.1%) by swab-positivity-only and 28.6% (28.4-28.9%) by trajectory-based N-antibody-classifications-only. Congruence with swab-positivity was respectively much poorer and slightly better with N-antibody classifications based on fixed thresholds or fourfold increases. Using multivariable logistic regression N-antibody seroconversion was more likely as age increased between 30-60 years, in non-white participants, those less (recently/frequently) vaccinated, for lower cycle threshold values in the range above 30, and in symptomatic and Delta (vs. BA.1) infections. Comparing swab-positivity data sources showed that routine monthly swabs were insufficient to detect infections and incorporating national testing programme/self-reported data substantially increased detection. Overall, whilst N-antibody serosurveillance can identify infections undetected by swab-positivity, optimal use requires fourfold-increase-based or trajectory-based analysis.
Global, regional, and national burden of epilepsy, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.
BackgroundSeizures and their consequences contribute to the burden of epilepsy because they can cause health loss (premature mortality and residual disability). Data on the burden of epilepsy are needed for health-care planning and resource allocation. The aim of this study was to quantify health loss due to epilepsy by age, sex, year, and location using data from the Global Burden of Diseases, Injuries, and Risk Factors Study.MethodsWe assessed the burden of epilepsy in 195 countries and territories from 1990 to 2016. Burden was measured as deaths, prevalence, and disability-adjusted life-years (DALYs; a summary measure of health loss defined by the sum of years of life lost [YLLs] for premature mortality and years lived with disability), by age, sex, year, location, and Socio-demographic Index (SDI; a compound measure of income per capita, education, and fertility). Vital registrations and verbal autopsies provided information about deaths, and data on the prevalence and severity of epilepsy largely came from population representative surveys. All estimates were calculated with 95% uncertainty intervals (UIs).FindingsIn 2016, there were 45·9 million (95% UI 39·9-54·6) patients with all-active epilepsy (both idiopathic and secondary epilepsy globally; age-standardised prevalence 621·5 per 100 000 population; 540·1-737·0). Of these patients, 24·0 million (20·4-27·7) had active idiopathic epilepsy (prevalence 326·7 per 100 000 population; 278·4-378·1). Prevalence of active epilepsy increased with age, with peaks at 5-9 years (374·8 [280·1-490·0]) and at older than 80 years of age (545·1 [444·2-652·0]). Age-standardised prevalence of active idiopathic epilepsy was 329·3 per 100 000 population (280·3-381·2) in men and 318·9 per 100 000 population (271·1-369·4) in women, and was similar among SDI quintiles. Global age-standardised mortality rates of idiopathic epilepsy were 1·74 per 100 000 population (1·64-1·87; 1·40 per 100 000 population [1·23-1·54] for women and 2·09 per 100 000 population [1·96-2·25] for men). Age-standardised DALYs were 182·6 per 100 000 population (149·0-223·5; 163·6 per 100 000 population [130·6-204·3] for women and 201·2 per 100 000 population [166·9-241·4] for men). The higher DALY rates in men were due to higher YLL rates compared with women. Between 1990 and 2016, there was a non-significant 6·0% (-4·0 to 16·7) change in the age-standardised prevalence of idiopathic epilepsy, but a significant decrease in age-standardised mortality rates (24·5% [10·8 to 31·8]) and age-standardised DALY rates (19·4% [9·0 to 27·6]). A third of the difference in age-standardised DALY rates between low and high SDI quintile countries was due to the greater severity of epilepsy in low-income settings, and two-thirds were due to a higher YLL rate in low SDI countries.InterpretationDespite the decrease in the disease burden from 1990 to 2016, epilepsy is still an important cause of disability and mortality. Standardised collection of data on epilepsy in population representative surveys will strengthen the estimates, particularly in countries for which we currently have no or sparse data and if additional data is collected on severity, causes, and treatment. Sizeable gains in reducing the burden of epilepsy might be expected from improved access to existing treatments in low-income countries and from the development of new effective drugs worldwide.FundingBill & Melinda Gates Foundation.
Multimorbidity Management: A Scoping Review of Interventions and Health Outcomes
Multimorbidity, defined as the co-occurrence of two or more chronic conditions in an individual, has emerged as a worldwide public health concern contributing to mortality and morbidity. This complex health phenomenon is becoming increasingly prevalent worldwide, particularly as populations continue to age. Despite the growing burden of multimorbidity, the development and implementation of interventions published by scholars are still in their early stages with significant variability in strategies and outcomes. The variability in strategy and outcome may result from factors such as lack of infrastructure, socioeconomic status and lifestyle factors. The review aims to synthesize interventions designed to manage and mitigate multimorbidity and explore a range of approaches, including pharmacological treatments, lifestyle modifications, care coordination models, and technological innovations. The scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist. It included 1,553,877 individuals with multimorbidity with no age restriction; in the studies that included gender difference, 463,339 male participants and 1,091,538 female participants were involved. Multimorbidity interventions were defined as strategies or programs designed to manage and improve the health and quality of life of individuals with multiple chronic conditions. Of the downloaded articles, those that met the inclusion criteria were published between 2012 and 2024. The final analysis included 100 articles from 3119 published articles, which resulted in 9 themes and 15 subthemes. Themes on the need for lifestyle and behavioural interventions, patient empowerment and engagement, multimorbidity management, health integration, pharmacotherapy optimization, community and policy interventions, healthcare system improvements, technology and digital health, as well as research and evidence-based practice interventions, emerged. The reviewed literature emphasizes the necessity of multidisciplinary approaches to effectively combat the growing public health challenge of multimorbidity.
Determining a role for Patient and Public Involvement and Engagement (PPIE) in genomic data governance for cancer care
Abstract Comprehensive collections of cancer data, including genomic data, are needed to improve cancer risk prediction and treatments. A recent government review, Better, Broader, Safer: Using health data for research and analysis, has argued for high-quality Patient and Public Involvement and Engagement (PPIE) for ethical data use. In this paper we determine a role and justification for PPIE to govern uses of genomic data in fields like cancer. First, we analyse two public attitudes studies about the role of PPIE in genomics governance. Second, we characterise two ethically-significant features of the context of governing genomic data: 1) data aggregation leading to novel group formation, and 2) the hybrid territory of genomic cancer data uses. Thirdly, we bring together these aspects to describe a fully determined role for PPIE within an approach to governing cancer genomic data, which is tailored to major areas of ethical consideration. Our account is a novel interpretation of what PPIE is for in governance, how it may foster public support and how its success in so doing depends on it being tailored to context.
Methodological opportunities in genomic data analysis to advance health equity.
The causes and consequences of inequities in genomic research and medicine are complex and widespread. However, it is widely acknowledged that underrepresentation of diverse populations in human genetics research risks exacerbating existing health disparities. Efforts to improve diversity are ongoing, but an often-overlooked source of inequity is the choice of analytical methods used to process, analyse and interpret genomic data. This choice can influence all areas of genomic research, from genome-wide association studies and polygenic score development to variant prioritization and functional genomics. New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging within the wider genomic research and genomic medicine ecosystems. At this crucial time point, it is important to clarify where improvements in methods and practices can, or cannot, have a role in improving equity in genomics. Here, we review existing approaches to promote equity and fairness in statistical analysis for genomics, and propose future methodological developments that are likely to yield the most impact for equity.
Molnupiravir or nirmatrelvir-ritonavir plus usual care versus usual care alone in patients admitted to hospital with COVID-19: a randomised, controlled, open-label, platform trial (RECOVERY)
Background: Molnupiravir and nirmatrelvir-ritonavir (Paxlovid) are oral antivirals that were assessed in separate treatment comparisons in the RECOVERY trial, a randomised, controlled, open-label, adaptive platform trial evaluating treatments for patients hospitalised with COVID-19 pneumonia. Methods: Adult participants could join the molnupiravir comparison, the nirmatrelvir-ritonavir comparison, or both. In each comparison, they were randomly allocated in a 1:1 ratio to the relevant antiviral (five days of molnupiravir 800mg twice daily or nirmatrelvir-ritonavir 300mg/100mg twice daily) in addition to usual care, or to usual care alone. The primary outcome was 28-day mortality, and secondary outcomes were time to discharge alive from hospital, and progression to invasive ventilation or death. Analysis was by intention-to-treat. Both comparisons were stopped because of low recruitment. ISRCTN50189673; clinicaltrials.gov NCT04381936. Findings: From January 2022 to May 2023, 923 patients were recruited to the molnupiravir comparison (445 allocated molnupiravir and 478 allocated usual care), and from March 2022 to May 2023, 137 patients were recruited to the nirmatrelvir-ritonavir comparison (68 allocated nirmatrelvir-ritonavir and 69 allocated usual care). Over three-quarters of the patients were vaccinated and had anti-spike antibodies at randomisation, and over two-thirds were receiving other SARS-CoV-2 antivirals. In the molnupiravir comparison, 74 (17%) patients allocated molnupiravir and 79 (17%) patients allocated usual care died within 28 days (hazard ratio [HR] 0.93; 95% confidence interval [CI] 0.68-1.28; p=0.66). In the nirmatrelvir-ritonavir comparison, 13 (19%) patients allocated nirmatrelvir-ritonavir and 13 (19%) patients allocated usual care died within 28 days (HR 1.02; 95% CI 0.47-2.23; p=0.96). In neither comparison was there evidence of any difference in the duration of hospitalisation or the proportion of patients progressing to invasive ventilation or death. Interpretation: Adding molnupiravir or nirmatrelvir-ritonavir to usual care was not associated with improvements in clinical outcomes. However, limited recruitment meant a clinically meaningful benefit of treatment could not be ruled-out, particularly for nirmatrelvir-ritonavir.
The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2.
The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses.
Dominant variants in major spliceosome U4 and U5 small nuclear RNA genes cause neurodevelopmental disorders through splicing disruption.
The major spliceosome contains five small nuclear RNAs (snRNAs; U1, U2, U4, U5 and U6) essential for splicing. Variants in RNU4-2, encoding U4, cause a neurodevelopmental disorder called ReNU syndrome. We investigated de novo variants in 50 snRNA-encoding genes in a French cohort of 23,649 individuals with rare disorders and gathered additional cases through international collaborations. Altogether, we identified 145 previously unreported probands with (likely) pathogenic variants in RNU4-2 and 21 individuals with de novo and/or recurrent variants in RNU5B-1 and RNU5A-1, encoding U5. Pathogenic variants typically arose de novo on the maternal allele and cluster in regions critical for splicing. RNU4-2 variants mainly localize to two structures, the stem III and T-loop/quasi-pseudoknot, which position the U6 ACAGAGA box for 5' splice site recognition and associate with different phenotypic severity. RNU4-2 variants result in specific defects in alternative 5' splice site usage and methylation patterns (episignatures) that correlate with variant location and clinical severity. This study establishes RNU5B-1 as a neurodevelopmental disorder gene, suggests RNU5A-1 as a strong candidate and highlights the role of de novo variants in snRNAs.
Systematic identification of disease-causing promoter and untranslated region variants in 8040 undiagnosed individuals with rare disease.
BackgroundBoth promoters and untranslated regions (UTRs) have critical regulatory roles, yet variants in these regions are largely excluded from clinical genetic testing due to difficulty in interpreting pathogenicity. The extent to which these regions may harbour diagnoses for individuals with rare disease is currently unknown.MethodsWe present a framework for the identification and annotation of potentially deleterious proximal promoter and UTR variants in known dominant disease genes. We use this framework to annotate de novo variants (DNVs) in 8040 undiagnosed individuals in the Genomics England 100,000 genomes project, which were subject to strict region-based filtering, clinical review, and validation studies where possible. In addition, we performed region and variant annotation-based burden testing in 7862 unrelated probands against matched unaffected controls.ResultsWe prioritised eleven DNVs and identified an additional variant overlapping one of the eleven. Ten of these twelve variants (82%) are in genes that are a strong match to the individual's phenotype and six had not previously been identified. Through burden testing, we did not observe a significant enrichment of potentially deleterious promoter and/or UTR variants in individuals with rare disease collectively across any of our region or variant annotations.ConclusionsWhilst screening promoters and UTRs can uncover additional diagnoses for individuals with rare disease, including these regions in diagnostic pipelines is not likely to dramatically increase diagnostic yield. Nevertheless, we provide a framework to aid identification of these variants.
Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection
Federated Learning (FL) in healthcare ensures patient privacy by allowing hospitals to collaboratively train machine learning models while keeping sensitive medical data secure and localized. Most existing research in FL has concentrated on unimodal scenarios, where all healthcare institutes share the same type of data. However, in real-world healthcare situations, some clients may have access to multiple types of data pertaining to the same disease. Multimodal Federated Learning (MMFL) utilizes multiple modalities to build a more powerful FL model than its unimodal counterpart. However, the impact of missing modality in different clients, called modality incongruity, has been greatly overlooked. This paper, for the first time, analyses the impact of modality incongruity and reveals its connection with data heterogeneity across participating clients. We particularly inspect whether incongruent MMFL with unimodal and multimodal clients is more beneficial than unimodal FL. Furthermore, we examine three potential routes of addressing this issue. Firstly, we study the effectiveness of various self-attention mechanisms towards incongruity-agnostic information fusion in MMFL. Secondly, we introduce a modality imputation network (MIN) pre-trained in a multimodal client for modality translation in unimodal clients and investigate its potential towards mitigating the missing modality problem. Thirdly, we introduce several client-level and server-level regularization techniques including Modality-aware knowledge Distillation (MAD) and Leave-one-out teacher (LOOT) towards mitigating modality incongruity effects. Experiments are conducted with Chest X-Ray and radiology reports under several MMFL settings on two publicly available real-world datasets, MIMIC-CXR and Open-I.
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities
Segmentation models for brain lesions in MRI are commonly developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities, nor can they segment other types of diseases. Moreover, this training paradigm prevents a model from using the advantages of learning from heterogeneous databases that may contain scans and segmentation labels for different brain pathologies and diverse sets of MRI modalities. Additionally, the confidentiality of patient data often prevents central data aggregation, necessitating a decentralized approach. Is it feasible to use Federated Learning (FL) to train a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients, training with random modality drop, and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that this FL framework can train a single model that achieves very promising results in segmenting all disease types seen during training. Importantly, it can segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate, for the first time, the feasibility and effectiveness of using FL to train a single 3D segmentation model on decentralised data with diverse brain diseases and MRI modalities, a necessary step towards leveraging heterogeneous real-world databases. Code: https://github.com/FelixWag/FedUniBrain
Molecular clock complexities of Clostridioides difficile.
ObjectivesReconstruct the phylogenetic status of a collection of historical Clostridioides difficile isolates and evaluate the congruence of their evolutionary trajectories with established molecular clock models.MethodsPhylogenetic analysis was performed on Illumina sequence reads from previously analysed historic C. difficile isolates (1980-86; n=75) demonstrating multiple antimicrobial resistances. Data was grouped by ribotype (RT), including comparators from European surveillance (2012-13) and phylogenetic studies (1985-2010). Reads were mapped to CD630/CD196 reference genomes and compared using recombination-adjusted maximum likelihood trees. Prediction intervals for expected SNP differences by age were calculated using a Poisson distribution and molecular clock estimates (0.74 SNPs per genome/per year). Root-to-tip analysis was performed to determine the date of most common recent ancestor of genomes sharing a ribotype.ResultsMoxifloxacin-resistant (>16 mg/L) RT027 isolate JV67 (1986) was two SNPs distinct from a 2006 genome, fewer than the expected lower estimate (4.4 SNPs) under current molecular clock calculations; (p=3.93x10-5). For isolate JV02 (1981), the 13 SNP divergence from a 2008 isolate was consistent with expectations (5.9 SNPs; p=0.07). JV73 (1983) demonstrated an 8 SNP difference, which although above the expected lower limit (5.5 SNPs), was outside the 95% prediction interval; (p= 4.51x10-3). Only sixty-nine percent of historical genomes fit within the prediction interval for the number of SNPs expected compared to recent isolates, with fewer SNPs observed more frequently than expected. Root-to-tip analysis demonstrated only a weak linear correlation.ConclusionsC. difficile molecular clock estimations may be more complex than previously considered, with periods of spore quiescence potentially complicating analyses.
Grey-Matter Structure Markers of Alzheimer's Disease, Alzheimer's Conversion, Functioning and Cognition: A Meta-Analysis Across 11 Cohorts.
Alzheimer's disease (AD) brain markers are needed to select people with early-stage AD for clinical trials and as quantitative endpoint measures in trials. Using 10 clinical cohorts (N = 9140) and the community volunteer UK Biobank (N = 37,664) we performed region of interest (ROI) and vertex-wise analyses of grey-matter structure (thickness, surface area and volume). We identified 94 trait-ROI significant associations, and 307 distinct cluster of vertex-associations, which partly overlap the ROI associations. For AD versus controls, smaller hippocampus, amygdala and of the medial temporal lobe (fusiform and parahippocampal gyri) was confirmed and the vertex-wise results provided unprecedented localisation of some of the associated region. We replicated AD associated differences in several subcortical (putamen, accumbens) and cortical regions (inferior parietal, postcentral, middle temporal, transverse temporal, inferior temporal, paracentral, superior frontal). These grey-matter regions and their relative effect sizes can help refine our understanding of the brain regions that may drive or precede the widespread brain atrophy observed in AD. An AD grey-matter score evaluated in independent cohorts was significantly associated with cognition, MCI status, AD conversion (progression from cognitively normal or MCI to AD), genetic risk, and tau concentration in individuals with none or mild cognitive impairments (AUC in 0.54-0.70, p-value
Rapid identification of bacterial isolates using microfluidic adaptive channels and multiplexed fluorescence microscopy.
We demonstrate the rapid capture, enrichment, and identification of bacterial pathogens using Adaptive Channel Bacterial Capture (ACBC) devices. Using controlled tuning of device backpressure in polydimethylsiloxane (PDMS) devices, we enable the controlled formation of capture regions capable of trapping bacteria from low cell density samples with near 100% capture efficiency. The technical demands to prepare such devices are much lower compared to conventional methods for bacterial trapping and can be achieved with simple benchtop fabrication methods. We demonstrate the capture and identification of seven species of bacteria with bacterial concentrations lower than 1000 cells per mL, including common Gram-negative and Gram-positive pathogens such as Escherichia coli and Staphylococcus aureus. We further demonstrate that species identification of the trapped bacteria can be undertaken in the order of one-hour using multiplexed 16S rRNA-FISH with identification accuracies of 70-98% with unsupervised classification methods across 7 species of bacteria. Finally, by using the bacterial capture capabilities of the ACBC chip with an ultra-rapid antimicrobial susceptibility testing method employing fluorescence imaging and convolutional neural network (CNN) classification, we demonstrate that we can use the ACBC chip as an imaging flow cytometer that can predict the antibiotic susceptibility of E. coli cells after identification.
Infection Inspection: using the power of citizen science for image-based prediction of antibiotic resistance in Escherichia coli treated with ciprofloxacin.
Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its threat. This study uses citizen science and image feature analysis to profile the cellular features associated with antibiotic resistance in Escherichia coli. Between February and April 2023, we conducted the Infection Inspection project, in which 5273 volunteers made 1,045,199 classifications of single-cell images from five E. coli strains, labelling them as antibiotic-sensitive or antibiotic-resistant based on their response to the antibiotic ciprofloxacin. User accuracy in image classification reached 66.8 ± 0.1%, lower than our deep learning model's performance at 75.3 ± 0.4%, but both users and the model were more accurate when classifying cells treated at a concentration greater than the strain's own minimum inhibitory concentration. We used the users' classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications happened when cellular features varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our diagnostic methodology. Infection Inspection is another demonstration of the potential for public participation in research, specifically increasing public awareness of antibiotic resistance.