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From early in the coronavirus disease 2019 (COVID-19) pandemic, there was interest in using machine learning methods to predict COVID-19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations have been overcome in a study carried out by the Turing-RSS Health Data Laboratory and the UK Health Security Agency. As part of the study, the UK Health Security Agency collected a dataset of acoustic recordings, SARS-CoV-2 infection status and extensive study participant meta-data. This allowed us to rigorously assess state-of-the-art machine learning techniques to predict SARS-CoV-2 infection status based on vocal audio signals. The lessons learned from this project should inform future studies on statistical evaluation methods to assess the performance of machine learning techniques for public health tasks., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Emergency admissions (EA), where a patient requires urgent in-hospital care, are a major challenge for healthcare systems. The development of risk prediction models can partly alleviate this problem by supporting primary care interventions and public health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and unsupervised machine-learning methods applied to routinely collected electronic health records from approximately 4.8M Scottish residents (2013-18). We demonstrate improvements in discrimination and calibration with respect to previous scores deployed in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides insights about the epidemiology of EA risk in Scotland, by studying predictive performance across different population sub-groups and reasons for admission, as well as by quantifying the effect of individual input features. Finally, we discuss broader challenges including reproducibility and how to safely update risk prediction models that are already deployed at population level., (© 2024. The Author(s).)

Background: While national guidelines recommend Venous Thromboembolism (VTE) risk assessment in cancer outpatients and consideration of pharmacologic prophylaxis in high-risk patients, prophylaxis rates are low in community oncology practices. A successful model for guideline implementation (the Vermont Model, VM) is validated in an academic tertiary oncology setting. We undertook an implementation study to determine the success of this model in a multi-site community oncology practice. The study objectives were to: 1) adapt the VM to the community practice setting; 2) implement the adapted VM into practice; and 3) evaluate clinical and implementation outcomes., Methods: The study was carried out in three phases: (1) Pre-implementation, a multidisciplinary team addressed the need to adapt the VM to the local context including electronic medical record (EMR) optimisation and clinician education; (2) implementation of the strategies adapted to the local context, informed by VM and adapted based on stakeholder feedback; (3) prospective evaluation of clinical and implementation outcomes at six months after implementation., Findings: Following creation of a comprehensive initiation roadmap for the adaptation of VM program to the community practice, 302 cancer outpatients initiating new treatment met inclusion criteria over a 6 month implementation period. VTE risk education was provided to 100% of patients, and 98% (296) of patients received a VTE risk assessment. Of 52 patients (18%) who scored as high risk based on a modified Khorana (Protecht) score, 14 (27%) initiated prophylaxis. Barriers to program adaptation included EMR optimization challenges and practice-level responsibility assignment, time constraints, concern about potential drug interactions, and financial & insurance issues., Interpretation: Implementation of a multidisciplinary VTE prevention model in the community-based oncology setting successfully increased VTE education and risk assessment rates. AC prophylaxis rates were modestly increased, highlighting the need to understand and address barriers to anticoagulant prophylaxis prescribing in this setting., Funding: Northern New England Clinical Oncology Society Research Funding Program., Competing Interests: None of the manuscript author declare any competing or conflicts of interest. YR received a travel grant for travel & lodging support from the Northern New England Clinical Oncology Society (NNECOS) to attend the NNECOS 2022 Annual Meeting for an oral presentation of the study findings., (© 2024 The Authors.)

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood., (© 2024. The Author(s).)

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up and help beat coronavirus' digital survey alongside demographic, symptom and self-reported respiratory condition data. Digital survey submissions were linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,565 of 72,999 participants and 24,105 of 25,706 positive cases. Respiratory symptoms were reported by 45.6% of participants. This dataset has additional potential uses for bioacoustics research, with 11.3% participants self-reporting asthma, and 27.2% with linked influenza PCR test results., (© 2024. The Author(s).)

Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a lesion, plays an important role in understanding aging and inflammatory diseases. We propose a scalable hierarchical Bayesian spatial model, called BLESS, capable of handling binary responses by placing continuous spike-and-slab mixture priors on spatially-varying parameters and enforcing spatial dependency on the parameter dictating the amount of sparsity within the probability of inclusion. The use of mean-field variational inference with dynamic posterior exploration, which is an annealing-like strategy that improves optimization, allows our method to scale to large sample sizes. Our method also accounts for underestimation of posterior variance due to variational inference by providing an approximate posterior sampling approach based on Bayesian bootstrap ideas and spike-and-slab priors with random shrinkage targets. Besides accurate uncertainty quantification, this approach is capable of producing novel cluster size based imaging statistics, such as credible intervals of cluster size, and measures of reliability of cluster occurrence. Lastly, we validate our results via simulation studies and an application to the UK Biobank, a large-scale lesion mapping study with a sample size of 40,000 subjects., Competing Interests: Conflict of Interest Statement The authors do not have any conflicts of interest to declare with respect to this work.

The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles., (© 2023. The Author(s).)

Polygenic scores (PGSs) are individual-level measures that aggregate the genome-wide genetic predisposition to a given trait. As PGS have predominantly been developed using European-ancestry samples, trait prediction using such European ancestry-derived PGS is less accurate in non-European ancestry individuals. Although there has been recent progress in combining multiple PGS trained on distinct populations, the problem of how to maximize performance given a multiple-ancestry cohort is largely unexplored. Here, we investigate the effect of sample size and ancestry composition on PGS performance for fifteen traits in UK Biobank. For some traits, PGS estimated using a relatively small African-ancestry training set outperformed, on an African-ancestry test set, PGS estimated using a much larger European-ancestry only training set. We observe similar, but not identical, results when considering other minority-ancestry groups within UK Biobank. Our results emphasise the importance of targeted data collection from underrepresented groups in order to address existing disparities in PGS performance., (© 2023. The Author(s).)

Importance: In patients with cancer who have venous thromboembolism (VTE) events, long-term anticoagulation with low-molecular-weight heparin (LMWH) is recommended to prevent recurrent VTE. The effectiveness of a direct oral anticoagulant (DOAC) compared with LMWH for preventing recurrent VTE in patients with cancer is uncertain., Objective: To evaluate DOACs, compared with LMWH, for preventing recurrent VTE and for rates of bleeding in patients with cancer following an initial VTE event., Design, Setting, and Participants: Unblinded, comparative effectiveness, noninferiority randomized clinical trial conducted at 67 oncology practices in the US that enrolled 671 patients with cancer (any invasive solid tumor, lymphoma, multiple myeloma, or chronic lymphocytic leukemia) who had a new clinical or radiological diagnosis of VTE. Enrollment occurred from December 2016 to April 2020. Final follow-up was in November 2020., Intervention: Participants were randomized in a 1:1 ratio to either a DOAC (n = 335) or LMWH (n = 336) and were followed up for 6 months or until death. Physicians and patients selected any DOAC or any LMWH (or fondaparinux) and physicians selected drug doses., Main Outcomes and Measures: The primary outcome was the recurrent VTE rate at 6 months. Noninferiority of anticoagulation with a DOAC vs LMWH was defined by the upper limit of the 1-sided 95% CI for the difference of a DOAC relative to LMWH of less than 3% in the randomized cohort that received at least 1 dose of assigned treatment. The 6 prespecified secondary outcomes included major bleeding, which was assessed using a 2.5% noninferiority margin., Results: Between December 2016 and April 2020, 671 participants were randomized and 638 (95%) completed the trial (median age, 64 years; 353 women [55%]). Among those randomized to a DOAC, 330 received at least 1 dose. Among those randomized to LMWH, 308 received at least 1 dose. Rates of recurrent VTE were 6.1% in the DOAC group and 8.8% in the LMWH group (difference, -2.7%; 1-sided 95% CI, -100% to 0.7%) consistent with the prespecified noninferiority criterion. Of 6 prespecified secondary outcomes, none were statistically significant. Major bleeding occurred in 5.2% of participants in the DOAC group and 5.6% in the LMWH group (difference, -0.4%; 1-sided 95% CI, -100% to 2.5%) and did not meet the noninferiority criterion. Severe adverse events occurred in 33.8% of participants in the DOAC group and 35.1% in the LMWH group. The most common serious adverse events were anemia and death., Conclusions and Relevance: Among adults with cancer and VTE, DOACs were noninferior to LMWH for preventing recurrent VTE over 6-month follow-up. These findings support use of a DOAC to prevent recurrent VTE in patients with cancer., Trial Registration: ClinicalTrials.gov Identifier: NCT02744092.

Objectives: To perform a systematic comparison of in-hospital mortality risk prediction post-cardiac surgery, between the predominant scoring system-European System for Cardiac Operative Risk Evaluation (EuroSCORE) II, logistic regression (LR) retrained on the same variables and alternative machine learning techniques (ML)-random forest (RF), neural networks (NN), XGBoost and weighted support vector machine., Methods: Retrospective analyses of prospectively routinely collected data on adult patients undergoing cardiac surgery in the UK from January 2012 to March 2019. Data were temporally split 70:30 into training and validation subsets. Mortality prediction models were created using the 18 variables of EuroSCORE II. Comparisons of discrimination, calibration and clinical utility were then conducted. Changes in model performance, variable-importance over time and hospital/operation-based model performance were also reviewed., Results: Of the 227 087 adults who underwent cardiac surgery during the study period, there were 6258 deaths (2.76%). In the testing cohort, there was an improvement in discrimination [XGBoost (95% confidence interval (CI) area under the receiver operator curve (AUC), 0.834-0.834, F1 score, 0.276-0.280) and RF (95% CI AUC, 0.833-0.834, F1, 0.277-0.281)] compared with EuroSCORE II (95% CI AUC, 0.817-0.818, F1, 0.243-0.245). There was no significant improvement in calibration with ML and retrained-LR compared to EuroSCORE II. However, EuroSCORE II overestimated risk across all deciles of risk and over time. The calibration drift was lowest in NN, XGBoost and RF compared with EuroSCORE II. Decision curve analysis showed XGBoost and RF to have greater net benefit than EuroSCORE II., Conclusions: ML techniques showed some statistical improvements over retrained-LR and EuroSCORE II. The clinical impact of this improvement is modest at present. However the incorporation of additional risk factors in future studies may improve upon these findings and warrants further study., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery.)

To support informed decision making, clear descriptions of the beneficial and harmful effects of a treatment are needed by various stakeholders. The current paradigm is to generate evidence sequentially through different experiments. However, data generated later, perhaps through observational studies, can be difficult to compare with earlier randomized trial data, resulting in confusion in understanding and interpretation of treatment effects. Moreover, the scientific questions these later experiments can serve to answer often remain vague. We propose Flexible Augmented Clinical Trial for Improved eVidence gEneration (FACTIVE), a new class of study designs enabling flexible augmentation of confirmatory randomized controlled trials with concurrent and close-to-real-world elements. Our starting point is to use clearly defined objectives for evidence generation, which are formulated through early discussion with health technology assessment (HTA) bodies and are additional to regulatory requirements for authorization of a new treatment. These enabling designs facilitate estimation of certain well-defined treatment effects in the confirmatory part and other complementary treatment effects in a concurrent real-world part. Each stakeholder should use the evidence that is relevant within their own decision-making framework. High quality data are generated under one single protocol and the use of randomization ensures rigorous statistical inference and interpretation within and between the different parts of the experiment. Evidence for the decision making of HTA bodies could be available earlier than is currently the case., (© 2023 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the model's predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

The function of the majority of genes in the human and mouse genomes is unknown. Investigating and illuminating this dark genome is a major challenge for the biomedical sciences. The International Mouse Phenotyping Consortium (IMPC) is addressing this through the generation and broad-based phenotyping of a knockout (KO) mouse line for every protein-coding gene, producing a multidimensional data set that underlies a genome-wide annotation map from genes to phenotypes. Here, we develop a multivariate (MV) statistical approach and apply it to IMPC data comprising 148 phenotypes measured across 4,548 KO lines. There are 4,256 (1.4% of 302,997 observed data measurements) hits called by the univariate (UV) model analysing each phenotype separately, compared to 31,843 (10.5%) hits in the observed data results of the MV model, corresponding to an estimated 7.5-fold increase in power of the MV model relative to the UV model. One key property of the data set is its 55.0% rate of missingness, resulting from quality control filters and incomplete measurement of some KO lines. This raises the question of whether it is possible to infer perturbations at phenotype-gene pairs at which data are not available, i.e., to infer some in vivo effects using statistical analysis rather than experimentation. We demonstrate that, even at missing phenotypes, the MV model can detect perturbations with power comparable to the single-phenotype analysis, thereby filling in the complete gene-phenotype map with good sensitivity. A factor analysis of the MV model's fitted covariance structure identifies 20 clusters of phenotypes, with each cluster tending to be perturbed collectively. These factors cumulatively explain 75% of the KO-induced variation in the data and facilitate biological interpretation of perturbations. We also demonstrate that the MV approach strengthens the correspondence between IMPC phenotypes and existing gene annotation databases. Analysis of a subset of KO lines measured in replicate across multiple laboratories confirms that the MV model increases power with high replicability., Competing Interests: The authors have declared that no competing interests exist.

Multiplexed immunofluorescence provides an un-precedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions.

Background: Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML model over traditional logistic regression (LR) remains controversial. We performed a systematic review and meta-analysis of studies comparing the discrimination accuracy between ML models versus LR in predicting operative mortality following cardiac surgery., Methods: The present systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. Discrimination ability was assessed using the C-statistic. Pooled C-statistics and its 95% credibility interval for ML models and LR were obtained were obtained using a Bayesian framework. Pooled estimates for ML models and LR were compared to inform on difference between the 2 approaches., Results: We identified 459 published citations of which 15 studies met inclusion criteria and were used for the quantitative and qualitative analysis. When the best ML model from individual study was used, meta-analytic estimates showed that ML were associated with a significantly higher C-statistic (ML, 0.88; 95% credibility interval, 0.83-0.93 vs LR, 0.81; 95% credibility interval, 0.77-0.85; P = .03). When individual ML algorithms were instead selected, we found a nonsignificant trend toward better prediction with each of ML algorithms. We found no evidence of publication bias (P = .70)., Conclusions: The present findings suggest that when compared with LR, ML models provide better discrimination in mortality prediction after cardiac surgery. However, the magnitude and clinical influence of such an improvement remains uncertain., (Copyright © 2020 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.

Obtaining up to date information on the number of UK COVID-19 regional infections is hampered by the reporting lag in positive test results for people with COVID-19 symptoms. In the UK, for 'Pillar 2' swab tests for those showing symptoms, it can take up to five days for results to be collated. We make use of the stability of the under reporting process over time to motivate a statistical temporal model that infers the final total count given the partial count information as it arrives. We adopt a Bayesian approach that provides for subjective priors on parameters and a hierarchical structure for an underlying latent intensity process for the infection counts. This results in a smoothed time-series representation nowcasting the expected number of daily counts of positive tests with uncertainty bands that can be used to aid decision making. Inference is performed using sequential Monte Carlo., (© 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.)

Competing Interests: AW acknowledges support from a Turing AI Fellowship under grant EP/V025379/1, The Alan Turing Institute, the Leverhulme Trust via the Leverhulme Centre for Future Intelligence (CFI), and Engineering and Physical Sciences Research Council (EPSRC) grants EP/V056522/1 and EP/V056883/1; is an advisor to retrain.ai (received options); and is a board member and senior advisory board member of GNS Healthcare (Boston) and a board member of its parent company GNS (Gene Network Sciences; receives options), and an advisory board member (paid) of the UK Government Centre for Data Ethics and Innovation. AD is the chairman of the Preemptive Medicine and Health Security Initiative of Flagship Pioneering. CH has consulted for Norvartis and the Novo Nordisk Foundation; is supported by grants from the EPSRC, the Medical Research Council, and the Department of Health and Social Care of the UK Government; and is on the international scientific advisory board of the UK Biobank. XL acknowledges support from the Medicines and Healthcare Products Regulatory Agency, the Wellcome Trust, the National Institute of Health Research, NHSX, and the Health Foundation. HA is the chief scientific officer of the Preemptive Medicine and Health Security Initiative of Flagship Pioneering. All other authors declare no competing interests. Funding and infrastructure support for this research is provided by the NHS AI Lab, NHSX, London, UK.

Purpose: Cyberattacks targeting health care organizations are becoming more frequent and affect all aspects of care delivery. Cancer care is particularly susceptible to major disruptions because of the potential of immediate and long-term consequences for patients who often rely on timely diagnostic testing and regular administration of systemic therapy in addition to other local treatment modalities to cure or control their diseases. On October 28, 2020, a cyberattack was launched on the University of Vermont Health Network with wide-ranging consequences for oncology, including loss of access to all network intranet servers, e-mail communications, and the electronic medical record (EMR)., Methods: This review details the immediate challenges faced by hematology and oncology during the cyberattack. The impact and response on inpatient, outpatient, and special patient populations are described. Steps that other academic- and community-based oncology practices can take to lessen the brunt of such an assault are suggested., Results: The two areas of immediate impact after the cyberattack were communications and lack of EMR access. The oncology-specific impact included loss of the individualized EMR chemotherapy plan templates and electronic safeguards built into multistep treatment preparation and delivery. With loss of access to schedules, basic patient information, encrypted communications platforms and radiology, and laboratory and pharmacy services, clinical outpatient care delivery was reduced by 40%. The infusion visit volume dropped by 52% in the first week and new patients could not access necessary services for timely diagnostic evaluation, requiring the creation of command centers to oversee ethical and transparent triage and allocation of systemic therapies and address new patient referrals. This included appropriate transfer of patients to alternate sites to minimize delays. Inpatient care including transitions of care was particularly challenging and addressing patient populations whose survival might be affected by delays in care., Conclusion: Oncology health care leaders and providers should be aware of the potential impact of a cyberattack on cancer care delivery and preventively develop processes to mitigate the impact., Competing Interests: Steven AdesResearch Funding: Lilly, EMD Serono Alissa A. ThomasResearch Funding: Novocure, Spectrum Pharmaceuticals, Spectrum Pharmaceuticals Julian SpragueStock and Other Ownership Interests: Gilead Sciences, Johnson and Johnson, Merk and Company, Roche/Genentech, Pfizer Peter A. KaufmanStock and Other Ownership Interests: AmgenHonoraria: Lilly, Polyphor, Macrogenics, EisaiConsulting or Advisory Role: Polyphor, Roche/Genentech, Lilly, Eisai, Macrogenics, Pfizer, Merck, AstraZenecaSpeakers' Bureau: LillyResearch Funding: Eisai, Polyphor, Roche/Genentech, Lilly, Novartis, Macrogenics, Pfizer, SanofiTravel, Accommodations, Expenses: Lilly, Polyphor, MacrogenicsNo other potential conflicts of interest were reported.

Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, R t , which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and R t . We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of R t are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and R t that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease., (© 2021. The Author(s).)

Background: Novartis and the University of Oxford's Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression., Method: The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the "IL-17" project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA))., Results: A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project., Conclusions: An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools., (© 2021. The Author(s).)

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction., Competing Interests: G.M. is a director of and shareholder in Genomics PLC, and is a partner in Peptide Groove LLP. The other authors declare no competing financial interests.

The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention 1-6 , but its epidemiological impact has remained uncertain 7 . Here we investigate the impact of the National Health Service (NHS) COVID-19 app for England and Wales, from its launch on 24 September 2020 to the end of December 2020. It was used regularly by approximately 16.5 million users (28% of the total population), and sent approximately 1.7 million exposure notifications: 4.2 per index case consenting to contact tracing. We estimated that the fraction of individuals notified by the app who subsequently showed symptoms and tested positive (the secondary attack rate (SAR)) was 6%, similar to the SAR for manually traced close contacts. We estimated the number of cases averted by the app using two complementary approaches: modelling based on the notifications and SAR gave an estimate of 284,000 (central 95% range of sensitivity analyses 108,000-450,000), and statistical comparison of matched neighbouring local authorities gave an estimate of 594,000 (95% confidence interval 317,000-914,000). Approximately one case was averted for each case consenting to notification of their contacts. We estimated that for every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (using modelling) or 2.3% (using statistical analysis). These findings support the continued development and deployment of such apps in populations that are awaiting full protection from vaccines.

Introduction: Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence.We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity., Methods and Analysis: The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation., Ethics and Dissemination: The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Background: Noise and distraction are recognized as contributing factors in critical incidents associated with surgery and anesthesia. In addition, excessive noise exposure can have negative effects on patients and staff members in these environments., Aims: We aimed to quantify noise exposure of the anesthesiology team and patients in pediatric operating theaters, comparing them to adult studies, internationally recognized standards, and identifying factors contributing to noise and/or distraction., Methods: We measured noise levels during three phases of anesthesia (pre-induction, induction, and postinduction/maintenance) and recorded additional environmental sources of distraction and noise., Results: We included 49 theater cases. The median and interquartile range (IQR) of mean noise levels during pre-induction, induction, and maintenance were 61 (59.7-63.5), 61.9 (60.6-63.3), and 61.6 (60.4-63.7) dB, respectively, while the median (IQR) of maximal noise levels was 81.4 (77.3-86.8), 83.7 (78.5-87.4), and 86.3 (84.6-90.4), respectively. There was an average of 8.3 people present in the room during induction. In 23 cases, there were additional unnecessary conversations occurring within the room at induction. We noted the maintenance phase was the noisiest phase of anesthesia. We recorded frequent potential sources of distraction during pre-induction and induction, though no significant correlations between these distractions and recorded noise levels., Conclusions: We documented noise levels well above recommended levels during all phases of pediatric anesthesia. We highlighted factors with potential to contribute to noise and distraction but found no statistically significant correlation with noise levels., (© 2020 John Wiley & Sons Ltd.)

Background: In May 2020, the UK National Health Service (NHS) Test and Trace programme was launched in England in response to the COVID-19 pandemic. The programme was first rolled out on the Isle of Wight and included version 1 of the NHS contact tracing app. The aim of the study was to make a preliminary assessment of the epidemiological impact of the Test and Trace programme using publicly available data., Methods: We used COVID-19 daily case data from Public Health England to infer incidence of new infections and estimate the reproduction number (R) for each of the 150 Upper-Tier Local Authorities (UTLAs) in England and nationally, before and after the launch of the Test and Trace programme on the Isle of Wight. We used Bayesian and maximum-likelihood methods to estimate R and compared the Isle of Wight with other UTLAs using a synthetic control method., Findings: We observed significant decreases in incidence and R on the Isle of Wight immediately after the launch of the Test and Trace programme. The Isle of Wight had a marked reduction in R, from 1·3 before the Test and Trace programme to 0·5 after by one of our measures, and went from having the third highest R before the Test and Trace programme, to the twelfth lowest afterwards compared with other UTLAs., Interpretation: Our results show that the epidemic on the Isle of Wight was controlled quickly and effectively after the launch of Test and Trace. Our findings highlight the need for further research to determine the causes of the reduction in the spread of the disease, as these could be translated into local and national non-pharmaceutical intervention strategies in the period before a treatment or vaccination for COVID-19 becomes available., Funding: Li Ka Shing Foundation and UK Economic and Social Research Council., (© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.)

We hypothesise that the NRF2 transcription factor would act a biomarker of poor prognosis in colorectal cancer. We derived and validated an mRNA based metagene signature of NRF2 signalling and validated it in 1360 patients from 4 different datasets as an independent biomarker of poor prognosis. This is a novel insight into the molecular signalling of colorectal cancer., Background: NRF2 over activity confers poor prognosis in some cancers but its prognostic role in colorectal cancer (CRC) is unknown. As a transcription factor, we hypothesise a signature of NRF2 regulated genes could act as a prognostic biomarker in CRC and reveal novel biological insights., Methods: Using known NRF2 regulated genes, differentially expressed in CRC, we defined a signature of NRF2 pathway activity using principal component analysis and Cox proportional hazard models and tested it in four independent mRNA datasets, profiled on three different mRNA platforms., Results: 36 genes comprised the final NRF2 signature. 1360 patients were included in the validation. High NRF2 was associated with worse disease free survival (DFS) and/or overall survival (OS) in all datasets: (GSE14333 HR=1.55, 95% C.I 1.2-2.004, p = 0.0008; GSE39582 HR=1.24, 95% C.I 1.086-1.416, p = 0.001; GSE87211 HR=1.431, 95% C.I 1.06-1.93, p = 0.056; MRC FOCUS trial HR=1.14, 95% C.I 1.04-1.26, p = 0.008). In multivariate analyses, NRF2 remained significant when adjusted for stage and adjuvant chemotherapy in stage I-III disease, and BRAF V600E mutation and sidedness in stage IV disease. NRF2 activity was particularly enriched in Consensus Molecular Subtype (CMS) 4., Conclusion: For the first time, NRF2 is shown to be a consistent, robust prognostic biomarker across all stages of colorectal cancer with additional clinical value to current known prognostic biomarkers. High NRF2 signalling in CMS 4 further refines the molecular taxonomy of CRC, a new biological insight, suggesting avenues of further study., Competing Interests: Declaration of Competing Interest Dr. Maughan reports grants from Medical Research Council, grants from Cancer Research UK, during the conduct of the study; non-financial support and other from Astra Zeneca, personal fees from Pierre Fabre, outside the submitted work. Dr Holmes reports funding from Novartis for research collaboration outside the submitted work., (Copyright © 2020. Published by Elsevier Inc.)

Objective: To the best of our knowledge, no bibliometric studies have characterised the paediatric anaesthesia research in Ireland. In this study, we aim to analyse the research output from two anaesthetic departments in Irish paediatric hospitals., Methods: A Scopus database search was conducted to identify the publications from 2007 to 2018 of the departments of anaesthesia and intensive care medicine in the Children's University Hospital, Temple Street (CUH), and Our Lady's Children's Hospital, Crumlin (OLCHC)., Results: The Irish publications in paediatric anaesthesia and intensive care included 108 publications. CUH and OLCHC published 37 (34.9%) and 73 (68.8%) documents, respectively, with 6 (5.6%) documents affiliated with both hospitals. The number of original research articles was 28 (75.7%) for CUH versus 46 (63%) for OLCHC. The number of published reviews was 5 (13.5%) for CUH versus 11 (15.1%) for OLCHC. Over the last 2 years (2016, 2017), the number of OLCHC publications was almost double (13 and 14 publications) that of CUH (4 and 6 publications). For CUH, only two publications were in specialised journals. For OLCHC, 18 publications were in specialised journals, in addition to four publications in high-ranked journals. The mean impact factor for CUH publications was 3.78 (standard deviation [SD], 7.19) versus 4.52 (SD, 10.56) for OLCHC. From OLCHC, 20 authors published with a median h-index of 2.00 (interquartile range, 0-4.25), versus 14 authors form CUH with a median h-index of 1.50 (1.00-4.50)., Conclusion: Anaesthetic publications from the two Irish paediatric hospitals are unexceptional and with limited cooperation between the two hospitals. Research plans should be implemented., Competing Interests: Conflict of Interest: The authors have no conflicts of interest to declare., (© Copyright 2020 by Turkish Anaesthesiology and Intensive Care Society.)

Background: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease., Methods: In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation., Findings: We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41-4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0., Interpretation: We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality., Funding: National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical sciences. A common question in many studies is whether there is an association between a response variable measured on each sample and the latent group structure represented by some given tree. Currently, this is addressed on an ad hoc basis, usually requiring the user to decide on an appropriate number of clusters to prune out of the tree to be tested against the response variable. Here, we present a statistical method with statistical guarantees that tests for association between the response variable and a fixed tree structure across all levels of the tree hierarchy with high power while accounting for the overall false positive error rate. This enhances the robustness and reproducibility of such findings., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Competing Interests: Competing interests: We have read and understood BMJ policy on declaration of interests and declare the following interests: GSC and KGMM are part of the TRIPOD steering group. GSC is director of the UK EQUATOR Centre. The remaining authors have no additional declarations.

Competing Interests: Competing interests We have read and understood BMJ policy on declaration of interests and declare the following interests: MW is funded as a scientist in the Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, University of Cambridge. CEDAR is a UK clinical research collaboration, public health research centre of excellence, which received funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council (MRC), National Institute of Health Research (NIHR), and Wellcome Trust. MW is also funded by NIHR as director of its public health research funding programme. MW, with RS, holds a research grant from NIHR to evaluate the UK soft drinks industry levy; a grant from the Canadian Institutes of Health Research to fund the International Food Policy Study; and has held a grant from MRC to develop consensus on the governance of relationships between public health scientists and the food industry. MW is an expert adviser to the Food Foundation and the House of Lords Committee on Food, Poverty Health and the Environment; and a member of the Obesity Health Alliance, Independent Obesity Strategy working group. Between 2008 and 2011, DTF received funds from government, non-profits and the private sector to organise three meetings on building trust to examine the epidemic of obesity. CH receives no income from the commercial sector. In the period 1990-2005, CH was employed, with Nestle and Mondelez, in various commercial roles in the UK and mainland Europe. Since 2005, CH has been studying childhood obesity in various positions across government and the charitable sector. GS reports grants from the Australian Research Council, the National Heart Foundation of Australia, and the National Health and Medical Research Council (Australia); and GS recently conducted a study to benchmark the nutrition related policies of major food and beverage companies in Australia. GS is a researcher involved in a NHMRC-funded trial of healthy supermarket interventions in partnership with IGA (supermarket retailer), City of Greater Bendigo and VicHealth.

Hepatitis C virus (HCV) is a highly variable pathogen that frequently establishes chronic infection. This genetic variability is affected by the adaptive immune response but the contribution of other host factors is unclear. Here, we examined the role played by interferon lambda-4 (IFN-λ4) on HCV diversity; IFN-λ4 plays a crucial role in spontaneous clearance or establishment of chronicity following acute infection. We performed viral genome-wide association studies using human and viral data from 485 patients of white ancestry infected with HCV genotype 3a. We demonstrate that combinations of host genetic variants, which determine IFN-λ4 protein production and activity, influence amino acid variation across the viral polyprotein - not restricted to specific viral proteins or HLA restricted epitopes - and modulate viral load. We also observed an association with viral di-nucleotide proportions. These results support a direct role for IFN-λ4 in exerting selective pressure across the viral genome, possibly by a novel mechanism., Competing Interests: MA, EA, CI, Ad, SL, CB, DB, AT, PP, VS, VC, EH, RB, AP, ET, PS, PK, CH, EB, CS, JM, VP No competing interests declared, GF Grants Consulting and Speaker/Advisory Board: AbbVie, Alcura, Bristol-Myers Squibb, Gilead, Janssen, GlaxoSmithKline, Merck, Roche, Springbank, Idenix, Tekmira, Novartis, WI Grants, Consulting and Advisory/ Speaker Board: Roche, Janssen Cilag, Gilead Sciences, Novartis, GlaxoSmithKline, Pfizer, Abbvie and Bristol-Myers Squibb, KA Grants, Consulting and Advisory/ Speaker Board: Achillion, Alnylam, Astellas, Abbvie, Bristol-Myers Squibb, Gilead, GlaxoSmithKline, Janssen, Merck, Roche, Novartis, Vir, (© 2019, Ansari et al.)

Background: Many modern anesthetic machines offer automated control of anesthetic vapor. The user simply sets a desired end-tidal concentration and the machine will manipulate the vaporizer and gas flow rates to obtain and maintain the preset target. Greater efficiency, and more accurate delivery of anesthetic vapor have been documented across multiple machines within the adult setting however, there is little evidence for their use in children., Aims: The aim of this study was to compare the consumption of sevoflurane using the Maquet Flow-i anesthesia machine (Maquet, Solna, Sweden) in automatic gas control mode vs manual mode in pediatric anesthesia. The primary outcome measure is rate of sevoflurane use., Method: Data logs were collected from our three Maquet Flow-i anesthesia machines over a 4-week period. We compared the rate of sevoflurane use when in manual mode vs cases where the automatic gas control mode was used. We also examined each automatic gas control case to determine whether percentage of anesthesia time in this mode correlated significantly with average rate of sevoflurane consumption., Results: Sevoflurane was the primary anesthetic used in 220 cases, comprising over 230 hours of anesthesia time. Of these, 36 cases were identified as automatic gas control cases and 184 as manual cases. Consumption of sevoflurane liquid in mL/min was significantly lower in automatic gas control cases (median 0.46, IQR 0.32-0.72 mL/min for automatic gas control; median 0.82, IQR 0.62-1.17 mL/min for manual; P < 0.001 by Wilcoxon Rank Sum test). For a case of median duration (49 minutes), average rate of sevoflurane liquid consumption was 0.54 mL/min for automatic gas control cases vs 0.81 mL/min for manual cases, a reduction of 33% (bootstrapped 95% CI 0.21-0.61 mL/min, P < 0.001)., Conclusion: Maquet's Flow-i automatic gas control mode reduced use of sevoflurane an average of one-third in a pediatric anesthesia setting., (© 2019 John Wiley & Sons Ltd.)

Physical activity and sleep duration are established risk factors for many diseases, but their aetiology is poorly understood, partly due to relying on self-reported evidence. Here we report a genome-wide association study (GWAS) of device-measured physical activity and sleep duration in 91,105 UK Biobank participants, finding 14 significant loci (7 novel). These loci account for 0.06% of activity and 0.39% of sleep duration variation. Genome-wide estimates of ~ 15% phenotypic variation indicate high polygenicity. Heritability is higher in women than men for overall activity (23 vs. 20%, p = 1.5 × 10 -4 ) and sedentary behaviours (18 vs. 15%, p = 9.7 × 10 -4 ). Heritability partitioning, enrichment and pathway analyses indicate the central nervous system plays a role in activity behaviours. Two-sample Mendelian randomisation suggests that increased activity might causally lower diastolic blood pressure (beta mmHg/SD: -0.91, SE = 0.18, p = 8.2 × 10 -7 ), and odds of hypertension (Odds ratio/SD: 0.84, SE = 0.03, p = 4.9 × 10 -8 ). Our results advocate the value of physical activity for reducing blood pressure.

SH3 and cysteine-rich domain-containing protein 3 (STAC3) is an essential component of the skeletal muscle excitation-contraction coupling (ECC) machinery, though its role and function are not yet completely understood. Here, we report 18 patients carrying a homozygous p.(Trp284Ser) STAC3 variant in addition to a patient compound heterozygous for the p.(Trp284Ser) and a novel splice site change (c.997-1G > T). Clinical severity ranged from prenatal onset with severe features at birth, to a milder and slowly progressive congenital myopathy phenotype. A malignant hyperthermia (MH)-like reaction had occurred in several patients. The functional analysis demonstrated impaired ECC. In particular, KCl-induced membrane depolarization resulted in significantly reduced sarcoplasmic reticulum Ca 2+ release. Co-immunoprecipitation of STAC3 with Ca V 1.1 in patients and control muscle samples showed that the protein interaction between STAC3 and Ca V 1.1 was not significantly affected by the STAC3 variants. This study demonstrates that STAC3 gene analysis should be included in the diagnostic work up of patients of any ethnicity presenting with congenital myopathy, in particular if a history of MH-like episodes is reported. While the precise pathomechanism remains to be elucidated, our functional characterization of STAC3 variants revealed that defective ECC is not a result of Ca V 1.1 sarcolemma mislocalization or impaired STAC3-Ca V 1.1 interaction., (© 2018 Wiley Periodicals, Inc.)

When it is acknowledged that all candidate parameterised statistical models are misspecified relative to the data generating process, the decision maker (DM) must currently concern themselves with inference for the parameter value minimising the Kullback-Leibler (KL)-divergence between the model and this process (Walker, 2013). However, it has long been known that minimising the KL-divergence places a large weight on correctly capturing the tails of the sample distribution. As a result, the DM is required to worry about the robustness of their model to tail misspecifications if they want to conduct principled inference. In this paper we alleviate these concerns for the DM. We advance recent methodological developments in general Bayesian updating (Bissiri, Holmes & Walker, 2016) to propose a statistically well principled Bayesian updating of beliefs targeting the minimisation of more general divergence criteria. We improve both the motivation and the statistical foundations of existing Bayesian minimum divergence estimation (Hooker & Vidyashankar, 2014; Ghosh & Basu, 2016), allowing the well principled Bayesian to target predictions from the model that are close to the genuine model in terms of some alternative divergence measure to the KL-divergence. Our principled formulation allows us to consider a broader range of divergences than have previously been considered. In fact, we argue defining the divergence measure forms an important, subjective part of any statistical analysis, and aim to provide some decision theoretic rational for this selection. We illustrate how targeting alternative divergence measures can impact the conclusions of simple inference tasks, and discuss then how our methods might apply to more complicated, high dimensional models.

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high- intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara-Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models., (© 2017 The Author(s).)

Characterizing the technical precision of measurements is a necessary stage in the planning of experiments and in the formal sample size calculation for optimal design. Instruments that measure multiple analytes simultaneously, such as in high-throughput assays arising in biomedical research, pose particular challenges from a statistical perspective. The current most popular method for assessing precision of high-throughput assays is by scatterplotting data from technical replicates. Here, we question the statistical rationale of this approach from both an empirical and theoretical perspective, illustrating our discussion using four example data sets from different genomic platforms. We demonstrate that such scatterplots convey little statistical information of relevance and are potentially highly misleading. We present an alternative framework for assessing the precision of high-throughput assays and planning biomedical experiments. Our methods are based on repeatability-a long-established statistical quantity also known as the intraclass correlation coefficient. We provide guidance and software for estimation and visualization of repeatability of high-throughput assays, and for its incorporation into study design. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd., (© 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

Background: Single-cell RNA-Seq can be a valuable and unbiased tool to dissect cellular heterogeneity, despite the transcriptome's limitations in describing higher functional phenotypes and protein events. Perhaps the most important shortfall with transcriptomic 'snapshots' of cell populations is that they risk being descriptive, only cataloging heterogeneity at one point in time, and without microenvironmental context. Studying the genetic ('nature') and environmental ('nurture') modifiers of heterogeneity, and how cell population dynamics unfold over time in response to these modifiers is key when studying highly plastic cells such as macrophages., Results: We introduce the programmable Polaris™ microfluidic lab-on-chip for single-cell sequencing, which performs live-cell imaging while controlling for the culture microenvironment of each cell. Using gene-edited macrophages we demonstrate how previously unappreciated knockout effects of SAMHD1, such as an altered oxidative stress response, have a large paracrine signaling component. Furthermore, we demonstrate single-cell pathway enrichments for cell cycle arrest and APOBEC3G degradation, both associated with the oxidative stress response and altered proteostasis. Interestingly, SAMHD1 and APOBEC3G are both HIV-1 inhibitors ('restriction factors'), with no known co-regulation., Conclusion: As single-cell methods continue to mature, so will the ability to move beyond simple 'snapshots' of cell populations towards studying the determinants of population dynamics. By combining single-cell culture, live-cell imaging, and single-cell sequencing, we have demonstrated the ability to study cell phenotypes and microenvironmental influences. It's these microenvironmental components - ignored by standard single-cell workflows - that likely determine how macrophages, for example, react to inflammation and form treatment resistant HIV reservoirs.

The combination of personalized therapy with immunotherapy might lead to rapid complete remission in patients who are too sick to be eligible for clinical trials. We report 2 such extraordinary responders. A discussion on the use and purpose of clinical trials in this new era of very active anticancer drug discovery concludes that a paradigm shift is urgently needed., (© 2016 S. Karger AG, Basel.)